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Leng L. Challenge, integration, and change: ChatGPT and future anatomical education. Med Educ Online 2024; 29:2304973. [PMID: 38217884 PMCID: PMC10791098 DOI: 10.1080/10872981.2024.2304973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 01/08/2024] [Indexed: 01/15/2024]
Abstract
With the vigorous development of ChatGPT and its application in the field of education, a new era of the collaborative development of human and artificial intelligence and the symbiosis of education has come. Integrating artificial intelligence (AI) into medical education has the potential to revolutionize it. Large language models, such as ChatGPT, can be used as virtual teaching aids to provide students with individualized and immediate medical knowledge, and conduct interactive simulation learning and detection. In this paper, we discuss the application of ChatGPT in anatomy teaching and its various application levels based on our own teaching experiences, and discuss the advantages and disadvantages of ChatGPT in anatomy teaching. ChatGPT increases student engagement and strengthens students' ability to learn independently. At the same time, ChatGPT faces many challenges and limitations in medical education. Medical educators must keep pace with the rapid changes in technology, taking into account ChatGPT's impact on curriculum design, assessment strategies and teaching methods. Discussing the application of ChatGPT in medical education, especially anatomy teaching, is helpful to the effective integration and application of artificial intelligence tools in medical education.
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Affiliation(s)
- Lige Leng
- Fujian Provincial Key Laboratory of Neurodegenerative Disease and Aging Research, Institute of Neuroscience, School of Medicine, Xiamen University, Xiamen, Fujian, P.R. China
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Franco D’Souza R, Mathew M, Mishra V, Surapaneni KM. Twelve tips for addressing ethical concerns in the implementation of artificial intelligence in medical education. Med Educ Online 2024; 29:2330250. [PMID: 38566608 PMCID: PMC10993743 DOI: 10.1080/10872981.2024.2330250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 03/08/2024] [Indexed: 04/04/2024]
Abstract
Artificial Intelligence (AI) holds immense potential for revolutionizing medical education and healthcare. Despite its proven benefits, the full integration of AI faces hurdles, with ethical concerns standing out as a key obstacle. Thus, educators should be equipped to address the ethical issues that arise and ensure the seamless integration and sustainability of AI-based interventions. This article presents twelve essential tips for addressing the major ethical concerns in the use of AI in medical education. These include emphasizing transparency, addressing bias, validating content, prioritizing data protection, obtaining informed consent, fostering collaboration, training educators, empowering students, regularly monitoring, establishing accountability, adhering to standard guidelines, and forming an ethics committee to address the issues that arise in the implementation of AI. By adhering to these tips, medical educators and other stakeholders can foster a responsible and ethical integration of AI in medical education, ensuring its long-term success and positive impact.
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Affiliation(s)
- Russell Franco D’Souza
- Department of Education, UNESCO Chair in Bioethics, Melbourne, Australia
- Department of Organisational Psychological Medicine, International Institute of Organisational Psychological Medicine, Melbourne, Australia
| | - Mary Mathew
- Department of Pathology, Kasturba Medical College, Manipal, Manipal Academy of Higher Education (MAHE), Manipal, India
| | - Vedprakash Mishra
- School of Hogher Education and Research, Datta Meghe Institute of Higher Education and Research (Deemed to be University), Nagpur, India
| | - Krishna Mohan Surapaneni
- Department of Biochemistry, Panimalar Medical College Hospital & Research Institute, Chennai, India
- Department of Medical Education, Panimalar Medical College Hospital & Research Institute, Chennai, India
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Park SH, Pinto-Powell R, Thesen T, Lindqwister A, Levy J, Chacko R, Gonzalez D, Bridges C, Schwendt A, Byrum T, Fong J, Shasavari S, Hassanpour S. Preparing healthcare leaders of the digital age with an integrative artificial intelligence curriculum: a pilot study. Med Educ Online 2024; 29:2315684. [PMID: 38351737 PMCID: PMC10868429 DOI: 10.1080/10872981.2024.2315684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 02/02/2024] [Indexed: 02/16/2024]
Abstract
Artificial intelligence (AI) is rapidly being introduced into the clinical workflow of many specialties. Despite the need to train physicians who understand the utility and implications of AI and mitigate a growing skills gap, no established consensus exists on how to best introduce AI concepts to medical students during preclinical training. This study examined the effectiveness of a pilot Digital Health Scholars (DHS) non-credit enrichment elective that paralleled the Dartmouth Geisel School of Medicine's first-year preclinical curriculum with a focus on introducing AI algorithms and their applications in the concurrently occurring systems-blocks. From September 2022 to March 2023, ten self-selected first-year students enrolled in the elective curriculum run in parallel with four existing curricular blocks (Immunology, Hematology, Cardiology, and Pulmonology). Each DHS block consisted of a journal club, a live-coding demonstration, and an integration session led by a researcher in that field. Students' confidence in explaining the content objectives (high-level knowledge, implications, and limitations of AI) was measured before and after each block and compared using Mann-Whitney U tests. Students reported significant increases in confidence in describing the content objectives after all four blocks (Immunology: U = 4.5, p = 0.030; Hematology: U = 1.0, p = 0.009; Cardiology: U = 4.0, p = 0.019; Pulmonology: U = 4.0, p = 0.030) as well as an average overall satisfaction level of 4.29/5 in rating the curriculum content. Our study demonstrates that a digital health enrichment elective that runs in parallel to an institution's preclinical curriculum and embeds AI concepts into relevant clinical topics can enhance students' confidence in describing the content objectives that pertain to high-level algorithmic understanding, implications, and limitations of the studied models. Building on this elective curricular design, further studies with a larger enrollment can help determine the most effective approach in preparing future physicians for the AI-enhanced clinical workflow.
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Affiliation(s)
- Soo Hwan Park
- Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | | | - Thomas Thesen
- Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | | | - Joshua Levy
- Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Rachael Chacko
- Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | | | - Connor Bridges
- Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Adam Schwendt
- Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Travis Byrum
- Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Justin Fong
- Geisel School of Medicine at Dartmouth, Hanover, NH, USA
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Yao Z, Kuang M, Li Z. Global trends of delayed graft function in kidney transplantation from 2013 to 2023: a bibliometric analysis. Ren Fail 2024; 46:2316277. [PMID: 38357764 PMCID: PMC10877662 DOI: 10.1080/0886022x.2024.2316277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Accepted: 02/03/2024] [Indexed: 02/16/2024] Open
Abstract
Delayed graft function (DGF) is an early complication after kidney transplantation. The literature on DGF has experienced substantial growth. However, there is a lack of bibliometric analysis of DGF. This study aimed to analyze the scientific outputs of DGF and explore its hotspots from 2013 to 2023 by using CiteSpace and VOSviewer. The 2058 pieces of literature collected in the Web of Science Core Collection (WOSCC) from 1 January 2013 to 31 December 2023 were visually analyzed in terms of the annual number of publications, authors, countries, journals, literature co-citations, and keyword clustering by using CiteSpace and VOSviewer. We found that the number of papers published in the past ten years showed a trend of first increasing and then decreasing; 2021 was the year with the most posts. The largest number of papers was published by the University of California System, and the largest number of papers was published by the United States. The top five keyword frequency rankings are: 'delayed graft function', 'kidney transplantation', 'renal transplantation', 'survival', and 'recipients'. These emerging trends include 'brain death donors', 'blood absence re-injection injuries', 'tacrolimus', 'older donors and recipients', and 'artificial intelligence and DGF'. In summary, this study reveals the authors and institutions that could be cooperated with and discusses the research hotspots in the past ten years. It provides a reference and direction for future research and application of DGF.
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Affiliation(s)
- Zhiling Yao
- Department of Organ Transplantation, First Affiliated Hospital of Kunming Medical University, Kunming City, Yunnan Province, China
| | - Mingqian Kuang
- Department of Organ Transplantation, First Affiliated Hospital of Kunming Medical University, Kunming City, Yunnan Province, China
| | - Zhen Li
- Department of Organ Transplantation, First Affiliated Hospital of Kunming Medical University, Kunming City, Yunnan Province, China
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Larsen TJ, Pettersen MB, Nygaard Jensen H, Lynge Pedersen M, Lund-Andersen H, Jørgensen ME, Byberg S. The use of artificial intelligence to assess diabetic eye disease among the Greenlandic population. Int J Circumpolar Health 2024; 83:2314802. [PMID: 38359160 PMCID: PMC10877649 DOI: 10.1080/22423982.2024.2314802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 02/01/2024] [Indexed: 02/17/2024] Open
Abstract
Background: Retina fundus images conducted in Greenland are telemedically assessed for diabetic retinopathy by ophthalmological nurses in Denmark. Applying an AI grading solution, in a Greenlandic setting, could potentially improve the efficiency and cost-effectiveness of DR screening.Method: We developed an AI model using retina fundus photos, performed on persons registered with diabetes in Greenland and Denmark, using Optos® ultra wide-field scanning laser ophthalmoscope, graded according to ICDR.Using the ResNet50 network we compared the model's ability to distinguish between different images of ICDR severity levels in a confusion matrix.Results: Comparing images with ICDR level 0 to images of ICDR level 4 resulted in an accuracy of 0.9655, AUC of 0.9905, sensitivity and specificity of 96.6%.Comparing ICDR levels 0,1,2 with ICDR levels 3,4, we achieved a performance with an accuracy of 0.8077, an AUC of 0.8728, a sensitivity of 84.6% and a specificity of 78.8%. For the other comparisons, we achieved a modest performance.Conclusion: We developed an AI model using Greenlandic data, to automatically detect DR on Optos retina fundus images. The sensitivity and specificity were too low for our model to be applied directly in a clinical setting, thus optimising the model should be prioritised.
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Affiliation(s)
- Trine Jul Larsen
- Greenland Center of Health Research, Institute of Nursing and Health Science, University of Greenland, Nuuk, Greenland
| | | | | | - Michael Lynge Pedersen
- Greenland Center of Health Research, Institute of Nursing and Health Science, University of Greenland, Nuuk, Greenland
- Rigshospitalet-Glostrup University Hospital, Glostrup, Denmark
| | - Henrik Lund-Andersen
- Clinical Epidemiology, Steno Diabetes Center Copenhagen, Copenhagen, Denmark
- Rigshospitalet-Glostrup University Hospital, Glostrup, Denmark
| | | | - Stine Byberg
- Clinical Epidemiology, Steno Diabetes Center Copenhagen, Copenhagen, Denmark
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Rossi M, Belotti G, Mainardi L, Baroni G, Cerveri P. Feasibility of proton dosimetry overriding planning CT with daily CBCT elaborated through generative artificial intelligence tools. Comput Assist Surg (Abingdon) 2024; 29:2327981. [PMID: 38468391 DOI: 10.1080/24699322.2024.2327981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/13/2024] Open
Abstract
Radiotherapy commonly utilizes cone beam computed tomography (CBCT) for patient positioning and treatment monitoring. CBCT is deemed to be secure for patients, making it suitable for the delivery of fractional doses. However, limitations such as a narrow field of view, beam hardening, scattered radiation artifacts, and variability in pixel intensity hinder the direct use of raw CBCT for dose recalculation during treatment. To address this issue, reliable correction techniques are necessary to remove artifacts and remap pixel intensity into Hounsfield Units (HU) values. This study proposes a deep-learning framework for calibrating CBCT images acquired with narrow field of view (FOV) systems and demonstrates its potential use in proton treatment planning updates. Cycle-consistent generative adversarial networks (cGAN) processes raw CBCT to reduce scatter and remap HU. Monte Carlo simulation is used to generate CBCT scans, enabling the possibility to focus solely on the algorithm's ability to reduce artifacts and cupping effects without considering intra-patient longitudinal variability and producing a fair comparison between planning CT (pCT) and calibrated CBCT dosimetry. To showcase the viability of the approach using real-world data, experiments were also conducted using real CBCT. Tests were performed on a publicly available dataset of 40 patients who received ablative radiation therapy for pancreatic cancer. The simulated CBCT calibration led to a difference in proton dosimetry of less than 2%, compared to the planning CT. The potential toxicity effect on the organs at risk decreased from about 50% (uncalibrated) up the 2% (calibrated). The gamma pass rate at 3%/2 mm produced an improvement of about 37% in replicating the prescribed dose before and after calibration (53.78% vs 90.26%). Real data also confirmed this with slightly inferior performances for the same criteria (65.36% vs 87.20%). These results may confirm that generative artificial intelligence brings the use of narrow FOV CBCT scans incrementally closer to clinical translation in proton therapy planning updates.
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Affiliation(s)
- Matteo Rossi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
- Laboratory of Innovation in Sleep Medicine, Istituto Auxologico Italiano, Milan, Italy
| | - Gabriele Belotti
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Luca Mainardi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Guido Baroni
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
- Bioengineering Unit, Clinical Department, National Center for Oncological Hadrontherapy (CNAO), Pavia, Italy
| | - Pietro Cerveri
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
- Laboratory of Innovation in Sleep Medicine, Istituto Auxologico Italiano, Milan, Italy
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Kim BR, Kim MJ, Koo J, Choi HJ, Paik KH, Kwon SH, Choi HR, Huh CH, Shin JW, Park DS, Na JI. Artificial intelligence-based prescription of personalized scalp cosmetics improved the scalp condition: efficacy results from 100 participants. J DERMATOL TREAT 2024; 35:2337908. [PMID: 38616301 DOI: 10.1080/09546634.2024.2337908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 11/01/2023] [Indexed: 04/16/2024]
Abstract
Background: Scalp-related symptoms such as dandruff and itching are common with diverse underlying etiologies. We previously proposed a novel classification and scoring system for scalp conditions, called the scalp photographic index (SPI); it grades five scalp features using trichoscopic images with good reliability. However, it requires trained evaluators.Aim: To develop artificial intelligence (AI) algorithms for assessment of scalp conditions and to assess the feasibility of AI-based recommendations on personalized scalp cosmetics.Methods: Using EfficientNet, convolutional neural network (CNN) models (SPI-AI) ofeach scalp feature were established. 101,027 magnified scalp images graded according to the SPI scoring were used for training, validation, and testing the model Adults with scalp discomfort were prescribed shampoos and scalp serums personalized according to their SPI-AI-defined scalp types. Using the SPI, the scalp conditions were evaluated at baseline and at weeks 4, 8, and 12 of treatment.Results: The accuracies of the SPI-AI for dryness, oiliness, erythema, folliculitis, and dandruff were 91.3%, 90.5%, 89.6%, 87.3%, and 95.2%, respectively. Overall, 100 individuals completed the 4-week study; 43 of these participated in an extension study until week 12. The total SPI score decreased from 32.70 ± 7.40 at baseline to 15.97 ± 4.68 at week 4 (p < 0.001). The efficacy was maintained throughout 12 weeks.Conclusions: SPI-AI accurately assessed the scalp condition. AI-based prescription of tailored scalp cosmetics could significantly improve scalp health.
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Affiliation(s)
- Bo Ri Kim
- Department of Dermatology, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Min Jae Kim
- Department of Dermatology, Seoul National University Bundang Hospital, Seongnam, Korea
- Department of Dermatology, Seoul National University College of Medicine, Seoul, Korea
| | - Jieun Koo
- Aram Huvis Co., Ltd, Seongnam, Korea
| | - Hwa-Jung Choi
- Department of Beauty Art, Youngsan University, Busan, South Korea
| | - Kyung Ho Paik
- Department of Dermatology, Seoul National University Bundang Hospital, Seongnam, Korea
- Department of Dermatology, Seoul National University College of Medicine, Seoul, Korea
| | - Soon Hyo Kwon
- Department of Dermatology, Kyung Hee University Hospital at Gangdong, Seoul, Korea
| | - Hye-Ryung Choi
- Department of Dermatology, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Chang Hun Huh
- Department of Dermatology, Seoul National University Bundang Hospital, Seongnam, Korea
- Department of Dermatology, Seoul National University College of Medicine, Seoul, Korea
| | - Jung Won Shin
- Department of Dermatology, Seoul National University Bundang Hospital, Seongnam, Korea
- Department of Dermatology, Seoul National University College of Medicine, Seoul, Korea
| | | | - Jung-Im Na
- Department of Dermatology, Seoul National University Bundang Hospital, Seongnam, Korea
- Department of Dermatology, Seoul National University College of Medicine, Seoul, Korea
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Garcia Valencia OA, Thongprayoon C, Miao J, Bruminhent J, Craici IM, Cheungpasitporn W. Perspectives on AI-based recommendations for mask-wearing and COVID-19 vaccination for transplant recipients in the post-COVID-19 era. Ren Fail 2024; 46:2337291. [PMID: 38584142 PMCID: PMC11000603 DOI: 10.1080/0886022x.2024.2337291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 03/26/2024] [Indexed: 04/09/2024] Open
Abstract
In the aftermath of the COVID-19 pandemic, the ongoing necessity for preventive measures such as mask-wearing and vaccination remains particularly critical for organ transplant recipients, a group highly susceptible to infections due to immunosuppressive therapy. Given that many individuals nowadays increasingly utilize Artificial Intelligence (AI), understanding AI perspectives is important. Thus, this study utilizes AI, specifically ChatGPT 4.0, to assess its perspectives in offering precise health recommendations for mask-wearing and COVID-19 vaccination tailored to this vulnerable population. Through a series of scenarios reflecting diverse environmental settings and health statuses in December 2023, we evaluated the AI's responses to gauge its precision, adaptability, and potential biases in advising high-risk patient groups. Our findings reveal that ChatGPT 4.0 consistently recommends mask-wearing in crowded and indoor environments for transplant recipients, underscoring their elevated risk. In contrast, for settings with fewer transmission risks, such as outdoor areas where social distancing is possible, the AI suggests that mask-wearing might be less imperative. Regarding vaccination guidance, the AI strongly advocates for the COVID-19 vaccine across most scenarios for kidney transplant recipients. However, it recommends a personalized consultation with healthcare providers in cases where patients express concerns about vaccine-related side effects, demonstrating an ability to adapt recommendations based on individual health considerations. While this study provides valuable insights into the current AI perspective on these important topics, it is crucial to note that the findings do not directly reflect or influence health policy. Nevertheless, given the increasing utilization of AI in various domains, understanding AI's viewpoints on such critical matters is essential for informed decision-making and future research.
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Affiliation(s)
- Oscar A Garcia Valencia
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA
| | - Charat Thongprayoon
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA
| | - Jing Miao
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA
| | - Jackrapong Bruminhent
- Department of Medicine, Division of Infectious Diseases, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
- Ramathibodi Excellence Center for Organ Transplantation, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Iasmina M Craici
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA
| | - Wisit Cheungpasitporn
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA
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Rezapour M, Yazdinejad M, Rajabi Kouchi F, Habibi Baghi M, Khorrami Z, Khavanin Zadeh M, Pourbaghi E, Rezapour H. Text mining of hypertension researches in the west Asia region: a 12-year trend analysis. Ren Fail 2024; 46:2337285. [PMID: 38616180 PMCID: PMC11018045 DOI: 10.1080/0886022x.2024.2337285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 03/27/2024] [Indexed: 04/16/2024] Open
Abstract
More than half of the world population lives in Asia and hypertension (HTN) is the most prevalent risk factor found in Asia. There are numerous articles published about HTN in Eastern Mediterranean Region (EMRO) and artificial intelligence (AI) methods can analyze articles and extract top trends in each country. Present analysis uses Latent Dirichlet allocation (LDA) as an algorithm of topic modeling (TM) in text mining, to obtain subjective topic-word distribution from the 2790 studies over the EMRO. The period of checked studied is last 12 years and results of LDA analyses show that HTN researches published in EMRO discuss on changes in BP and the factors affecting it. Among the countries in the region, most of these articles are related to I.R Iran and Egypt, which have an increasing trend from 2017 to 2018 and reached the highest level in 2021. Meanwhile, Iraq and Lebanon have been conducting research since 2010. The EMRO word cloud illustrates 'BMI', 'mortality', 'age', and 'meal', which represent important indicators, dangerous outcomes of high BP, and gender of HTN patients in EMRO, respectively.
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Affiliation(s)
- Mohammad Rezapour
- Faculty Member of the Iranian Ministry of Science, Research and Technology, Tehran, Iran
| | | | - Faezeh Rajabi Kouchi
- Department of Computer Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran
| | | | - Zahra Khorrami
- Ophthalmic Epidemiology Research Center, Research Institute for Ophthalmology and Vision Science, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Morteza Khavanin Zadeh
- Hasheminejad Kidney Center, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Elmira Pourbaghi
- Faculty of Advanced Sciences and Technology, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Hassan Rezapour
- Department of Transportation and Urban Infrastructure Studies, Morgan State University, Baltimore, MD, USA
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Ita K, Roshanaei S. Artificial intelligence for skin permeability prediction: deep learning. J Drug Target 2024; 32:334-346. [PMID: 38258521 DOI: 10.1080/1061186x.2024.2309574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 01/07/2024] [Indexed: 01/24/2024]
Abstract
BACKGROUND AND OBJECTIVE Researchers have put in significant laboratory time and effort in measuring the permeability coefficient (Kp) of xenobiotics. To develop alternative approaches to this labour-intensive procedure, predictive models have been employed by scientists to describe the transport of xenobiotics across the skin. Most quantitative structure-permeability relationship (QSPR) models are derived statistically from experimental data. Recently, artificial intelligence-based computational drug delivery has attracted tremendous interest. Deep learning is an umbrella term for machine-learning algorithms consisting of deep neural networks (DNNs). Distinct network architectures, like convolutional neural networks (CNNs), feedforward neural networks (FNNs), and recurrent neural networks (RNNs), can be employed for prediction. METHODS In this project, we used a convolutional neural network, feedforward neural network, and recurrent neural network to predict skin permeability coefficients from a publicly available database reported by Cheruvu et al. The dataset contains 476 records of 145 chemicals, xenobiotics, and pharmaceuticals, administered on the human epidermis in vitro from aqueous solutions of constant concentration either saturated in infinite dose quantities or diluted. All the computations were conducted with Python under Anaconda and Jupyterlab environment after importing the required Python, Keras, and Tensorflow modules. RESULTS We used a convolutional neural network, feedforward neural network, and recurrent neural network to predict log kp. CONCLUSION This research work shows that deep learning networks can be successfully used to digitally screen and predict the skin permeability of xenobiotics.
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Affiliation(s)
- Kevin Ita
- College of Pharmacy, Touro University, Vallejo, CA, USA
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Kurniawan MH, Handiyani H, Nuraini T, Hariyati RTS, Sutrisno S. A systematic review of artificial intelligence-powered (AI-powered) chatbot intervention for managing chronic illness. Ann Med 2024; 56:2302980. [PMID: 38466897 PMCID: PMC10930147 DOI: 10.1080/07853890.2024.2302980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Accepted: 12/31/2023] [Indexed: 03/13/2024] Open
Abstract
BACKGROUND Utilizing artificial intelligence (AI) in chatbots, especially for chronic diseases, has become increasingly prevalent. These AI-powered chatbots serve as crucial tools for enhancing patient communication, addressing the rising prevalence of chronic conditions, and meeting the growing demand for supportive healthcare applications. However, there is a notable gap in comprehensive reviews evaluating the impact of AI-powered chatbot interventions in healthcare within academic literature. This study aimed to assess user satisfaction, intervention efficacy, and the specific characteristics and AI architectures of chatbot systems designed for chronic diseases. METHOD A thorough exploration of the existing literature was undertaken by employing diverse databases such as PubMed MEDLINE, CINAHL, EMBASE, PsycINFO, ACM Digital Library and Scopus. The studies incorporated in this analysis encompassed primary research that employed chatbots or other forms of AI architecture in the context of preventing, treating or rehabilitating chronic diseases. The assessment of bias risk was conducted using Risk of 2.0 Tools. RESULTS Seven hundred and eighty-four results were obtained, and subsequently, eight studies were found to align with the inclusion criteria. The intervention methods encompassed health education (n = 3), behaviour change theory (n = 1), stress and coping (n = 1), cognitive behavioural therapy (n = 2) and self-care behaviour (n = 1). The research provided valuable insights into the effectiveness and user-friendliness of AI-powered chatbots in handling various chronic conditions. Overall, users showed favourable acceptance of these chatbots for self-managing chronic illnesses. CONCLUSIONS The reviewed studies suggest promising acceptance of AI-powered chatbots for self-managing chronic conditions. However, limited evidence on their efficacy due to insufficient technical documentation calls for future studies to provide detailed descriptions and prioritize patient safety. These chatbots employ natural language processing and multimodal interaction. Subsequent research should focus on evidence-based evaluations, facilitating comparisons across diverse chronic health conditions.
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Affiliation(s)
- Moh Heri Kurniawan
- Doctoral Student, Faculty of Nursing, Universitas Indonesia, Depok, Indonesia
- Departement of Nursing, Faculty of Health, Universitas Aisyah Pringsewu, Kabupaten Pringsewu, Indonesia
| | - Hanny Handiyani
- Department of Nursing, Faculty of Nursing, Universitas Indonesia, Depok, Indonesia
| | - Tuti Nuraini
- Department of Nursing, Faculty of Nursing, Universitas Indonesia, Depok, Indonesia
| | | | - Sutrisno Sutrisno
- Departement of Nursing, Faculty of Health, Universitas Aisyah Pringsewu, Kabupaten Pringsewu, Indonesia
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Zhang B, Jiang X, Yang J, Huang J, Hu C, Hong Y, Ni H, Zhang Z. Application of artificial intelligence in the management of patients with renal dysfunction. Ren Fail 2024; 46:2337289. [PMID: 38570197 PMCID: PMC10993745 DOI: 10.1080/0886022x.2024.2337289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Accepted: 03/27/2024] [Indexed: 04/05/2024] Open
Affiliation(s)
- Bo Zhang
- Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Xiaocong Jiang
- Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Jie Yang
- Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Jiajie Huang
- Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Chaoming Hu
- Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yucai Hong
- Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Hongying Ni
- Department of Critical Care Medicine, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, China
| | - Zhongheng Zhang
- Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
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13
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Nath PC, Mishra AK, Sharma R, Bhunia B, Mishra B, Tiwari A, Nayak PK, Sharma M, Bhuyan T, Kaushal S, Mohanta YK, Sridhar K. Recent advances in artificial intelligence towards the sustainable future of agri-food industry. Food Chem 2024; 447:138945. [PMID: 38461725 DOI: 10.1016/j.foodchem.2024.138945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 02/26/2024] [Accepted: 03/02/2024] [Indexed: 03/12/2024]
Abstract
Artificial intelligence has the potential to alter the agricultural and food processing industries, with significant ramifications for sustainability and global food security. The integration of artificial intelligence in agriculture has witnessed a significant uptick in recent years. Therefore, comprehensive understanding of these techniques is needed to broaden its application in agri-food supply chain. In this review, we explored cutting-edge artificial intelligence methodologies with a focus on machine learning, neural networks, and deep learning. The application of artificial intelligence in agri-food industry and their quality assurance throughout the production process is thoroughly discussed with an emphasis on the current scientific knowledge and future perspective. Artificial intelligence has played a significant role in transforming agri-food systems by enhancing efficiency, sustainability, and productivity. Many food industries are implementing the artificial intelligence in modelling, prediction, control tool, sensory evaluation, quality control, and tackling complicated challenges in food processing. Similarly, artificial intelligence applied in agriculture to improve the entire farming process, such as crop yield optimization, use of herbicides, weeds identification, and harvesting of fruits. In summary, the integration of artificial intelligence in agri-food systems offers the potential to address key challenges in agriculture, enhance sustainability, and contribute to global food security.
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Affiliation(s)
- Pinku Chandra Nath
- Bioproducts Processing Research Laboratory (BPRL), Department of Bio Engineering, National Institute of Technology, Agartala 799046, India; Department of Applied Biology, University of Science and Technology Meghalaya, Baridua 793101, India
| | - Awdhesh Kumar Mishra
- Department of Biotechnology, Yeungnam University, Gyeongsan 38541, Gyeongbuk, Republic of Korea
| | - Ramesh Sharma
- Bioproducts Processing Research Laboratory (BPRL), Department of Bio Engineering, National Institute of Technology, Agartala 799046, India; Sri Shakthi Institute of Engineering and Technology, Chinniyampalayam, 641062 Coimbatore, India
| | - Biswanath Bhunia
- Bioproducts Processing Research Laboratory (BPRL), Department of Bio Engineering, National Institute of Technology, Agartala 799046, India
| | - Bishwambhar Mishra
- Department of Biotechnology, Chaitanya Bharathi Institute of Technology, Hyderabad 500075, India
| | - Ajita Tiwari
- Department of Agricultural Engineering, Assam University, Silchar 788011, India
| | - Prakash Kumar Nayak
- Department of Food Engineering and Technology, Central Institute of Technology Kokrajhar, Kokrajhar 783370, India
| | - Minaxi Sharma
- Department of Applied Biology, University of Science and Technology Meghalaya, Baridua 793101, India
| | - Tamanna Bhuyan
- Department of Applied Biology, University of Science and Technology Meghalaya, Baridua 793101, India
| | - Sushant Kaushal
- Department of Tropical Agriculture and International Cooperation, National Pingtung University of Science and Technology, Pingtung 91201, Taiwan
| | - Yugal Kishore Mohanta
- Department of Applied Biology, University of Science and Technology Meghalaya, Baridua 793101, India; Centre for Herbal Pharmacology and Environmental Sustainability, Chettinad Hospital and Research Institute, Chettinad Academy of Research and Education, Kelambakkam 603103, India.
| | - Kandi Sridhar
- Department of Food Technology, Karpagam Academy of Higher Education (Deemed to be University), Coimbatore 641021, India.
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14
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Bernardini LG, Rosinger C, Bodner G, Keiblinger KM, Izquierdo-Verdiguier E, Spiegel H, Retzlaff CO, Holzinger A. Learning vs. understanding: When does artificial intelligence outperform process-based modeling in soil organic carbon prediction? N Biotechnol 2024; 81:20-31. [PMID: 38462171 DOI: 10.1016/j.nbt.2024.03.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 02/24/2024] [Accepted: 03/06/2024] [Indexed: 03/12/2024]
Abstract
In recent years, machine learning (ML) algorithms have gained substantial recognition for ecological modeling across various temporal and spatial scales. However, little evaluation has been conducted for the prediction of soil organic carbon (SOC) on small data sets commonly inherent to long-term soil ecological research. In this context, the performance of ML algorithms for SOC prediction has never been tested against traditional process-based modeling approaches. Here, we compare ML algorithms, calibrated and uncalibrated process-based models as well as multiple ensembles on their performance in predicting SOC using data from five long-term experimental sites (comprising 256 independent data points) in Austria. Using all available data, the ML-based approaches using Random forest and Support vector machines with a polynomial kernel were superior to all process-based models. However, the ML algorithms performed similar or worse when the number of training samples was reduced or when a leave-one-site-out cross validation was applied. This emphasizes that the performance of ML algorithms is strongly dependent on the data-size related quality of learning information following the well-known curse of dimensionality phenomenon, while the accuracy of process-based models significantly relies on proper calibration and combination of different modeling approaches. Our study thus suggests a superiority of ML-based SOC prediction at scales where larger datasets are available, while process-based models are superior tools when targeting the exploration of underlying biophysical and biochemical mechanisms of SOC dynamics in soils. Therefore, we recommend applying ensembles of ML algorithms with process-based models to combine advantages inherent to both approaches.
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Affiliation(s)
| | - Christoph Rosinger
- Institute of Agronomy, University of Natural Resources and Life Sciences (BOKU) Vienna, Konrad Lorenz-Straße 24, 3430 Tulln an der Donau, Austria; Institute of Soil Research, University of Natural Resources and Life Sciences (BOKU) Vienna, Peter Jordan-Straße 82, 1190 Vienna, Austria.
| | - Gernot Bodner
- Institute of Agronomy, University of Natural Resources and Life Sciences (BOKU) Vienna, Konrad Lorenz-Straße 24, 3430 Tulln an der Donau, Austria
| | - Katharina M Keiblinger
- Institute of Soil Research, University of Natural Resources and Life Sciences (BOKU) Vienna, Peter Jordan-Straße 82, 1190 Vienna, Austria
| | - Emma Izquierdo-Verdiguier
- Institute of Geomatics, University of Natural Resources and Life Sciences (BOKU) Vienna, Peter Jordan-Straße 82, 1190 Vienna, Austria
| | - Heide Spiegel
- Austrian Agency for Health and Food Safety (AGES), Institute for Soil Health and Plant Nutrition, Spargelfeldstraße 191, 1226 Vienna, Austria
| | - Carl O Retzlaff
- Human-Centered AI Lab, Institute of Forest Engineering, University of Natural Resources and Life Sciences (BOKU) Vienna, Peter Jordan-Straße 82, 1190 Vienna, Austria
| | - Andreas Holzinger
- Human-Centered AI Lab, Institute of Forest Engineering, University of Natural Resources and Life Sciences (BOKU) Vienna, Peter Jordan-Straße 82, 1190 Vienna, Austria
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15
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Moulahoum H, Ghorbanizamani F. Navigating the development of silver nanoparticles based food analysis through the power of artificial intelligence. Food Chem 2024; 445:138800. [PMID: 38382253 DOI: 10.1016/j.foodchem.2024.138800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 02/12/2024] [Accepted: 02/16/2024] [Indexed: 02/23/2024]
Abstract
In the ongoing pursuit of enhancing food safety and quality through advanced technologies, silver nanoparticles (AgNPs) stand out for their antimicrobial properties. Despite being overshadowed by other nanoparticles in food sensing applications, AgNPs possess inherent qualities that make them effective tools for rapid and selective contaminant detection in food matrices. This review aims to reinvigorate the interest in AgNPs in the food industry, emphasizing their sensing mechanism and the transformative potential of integrating them with artificial intelligence (AI) for enhanced food safety monitoring. It discusses key AI tools and principles in the food industry, demonstrating their positive impact on food analytical chemistry. The interplay between AI and biosensors offers many advantages and adaptability to dynamic analytical challenges, significantly improving food safety monitoring and potentially redefining the landscape of food safety and quality assurance.
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Affiliation(s)
- Hichem Moulahoum
- Department of Biochemistry, Faculty of Science, Ege University, 35100-Bornova, Izmir, Turkey.
| | - Faezeh Ghorbanizamani
- Department of Biochemistry, Faculty of Science, Ege University, 35100-Bornova, Izmir, Turkey.
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16
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Xie X, Yu W, Wang L, Yang J, Tu X, Liu X, Liu S, Zhou H, Chi R, Huang Y. SERS-based AI diagnosis of lung and gastric cancer via exhaled breath. Spectrochim Acta A Mol Biomol Spectrosc 2024; 314:124181. [PMID: 38527410 DOI: 10.1016/j.saa.2024.124181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 03/13/2024] [Accepted: 03/20/2024] [Indexed: 03/27/2024]
Abstract
Distinct diagnosis between Lung cancer (LC) and gastric cancer (GC) according to the same biomarkers (e.g. aldehydes) in exhaled breath based on surface-enhanced Raman spectroscopy (SERS) remains a challenge in current studies. Here, an accurate diagnosis of LC and GC is demonstrated, using artificial intelligence technologies (AI) based on SERS spectrum of exhaled breath in plasmonic metal organic frameworks nanoparticle (PMN) film. In the PMN film with optimal structure parameters, 1780 SERS spectra are collected, in which 940 spectra come from healthy people (n = 49), another 440 come from LC patients (n = 22) and the rest 400 come from GC patients (n = 8). The SERS spectra are trained through artificial neural network (ANN) model with the deep learning (DL) algorithm, and the result exhibits a good identification accuracy of LC and GC with an accuracy over 89 %. Furthermore, combined with information of SERS peaks, the data mining in ANN model is successfully employed to explore the subtle compositional difference in exhaled breath from healthy people (H) and L/GC patients. This work achieves excellent noninvasive diagnosis of multiple cancer diseases in breath analysis and provides a new avenue to explore the feature of disease based on SERS spectrum.
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Affiliation(s)
- Xin Xie
- Chongqing Key Laboratory of Interface Physics in Energy Conversion, College of Physics, Chongqing University, Chongqing 400044, China
| | - Wenrou Yu
- Chongqing Key Laboratory of Interface Physics in Energy Conversion, College of Physics, Chongqing University, Chongqing 400044, China
| | - Li Wang
- School of Optoelectronics Engineering, Chongqing University, Chongqing 401331, China
| | - Junjun Yang
- Chongqing Key Laboratory of Interface Physics in Energy Conversion, College of Physics, Chongqing University, Chongqing 400044, China
| | - Xiaobin Tu
- Department of Oncology and Department of Hematology, Chongqing Wulong People's Hospital, Chongqing 408500, China
| | - Xiaochun Liu
- Department of Oncology and Department of Hematology, Chongqing Wulong People's Hospital, Chongqing 408500, China
| | - Shihong Liu
- Department of Geriatric Oncology and Department of Palliative Care, Chongqing University Cancer Hospital, Chongqing 400030, China.
| | - Han Zhou
- Chongqing Key Laboratory of Interface Physics in Energy Conversion, College of Physics, Chongqing University, Chongqing 400044, China
| | - Runwei Chi
- Chongqing Key Laboratory of Interface Physics in Energy Conversion, College of Physics, Chongqing University, Chongqing 400044, China
| | - Yingzhou Huang
- Chongqing Key Laboratory of Interface Physics in Energy Conversion, College of Physics, Chongqing University, Chongqing 400044, China.
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Carvalho Macruz FBD, Dias ALMP, Andrade CS, Nucci MP, Rimkus CDM, Lucato LT, Rocha AJD, Kitamura FC. The new era of artificial intelligence in neuroradiology: current research and promising tools. Arq Neuropsiquiatr 2024; 82:1-12. [PMID: 38565188 PMCID: PMC10987255 DOI: 10.1055/s-0044-1779486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 12/13/2023] [Indexed: 04/04/2024]
Abstract
Radiology has a number of characteristics that make it an especially suitable medical discipline for early artificial intelligence (AI) adoption. These include having a well-established digital workflow, standardized protocols for image storage, and numerous well-defined interpretive activities. The more than 200 commercial radiologic AI-based products recently approved by the Food and Drug Administration (FDA) to assist radiologists in a number of narrow image-analysis tasks such as image enhancement, workflow triage, and quantification, corroborate this observation. However, in order to leverage AI to boost efficacy and efficiency, and to overcome substantial obstacles to widespread successful clinical use of these products, radiologists should become familiarized with the emerging applications in their particular areas of expertise. In light of this, in this article we survey the existing literature on the application of AI-based techniques in neuroradiology, focusing on conditions such as vascular diseases, epilepsy, and demyelinating and neurodegenerative conditions. We also introduce some of the algorithms behind the applications, briefly discuss a few of the challenges of generalization in the use of AI models in neuroradiology, and skate over the most relevant commercially available solutions adopted in clinical practice. If well designed, AI algorithms have the potential to radically improve radiology, strengthening image analysis, enhancing the value of quantitative imaging techniques, and mitigating diagnostic errors.
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Affiliation(s)
- Fabíola Bezerra de Carvalho Macruz
- Universidade de São Paulo, Hospital das Clínicas, Departamento de Radiologia e Oncologia, Seção de Neurorradiologia, Faculdade de Medicina, São Paulo SP, Brazil.
- Rede D'Or São Luiz, Departamento de Radiologia e Diagnóstico por Imagem, São Paulo SP, Brazil.
- Universidade de São Paulo, Laboratório de Investigação Médica em Ressonância Magnética (LIM 44), São Paulo SP, Brazil.
- Academia Nacional de Medicina, Rio de Janeiro RJ, Brazil.
| | | | | | - Mariana Penteado Nucci
- Universidade de São Paulo, Laboratório de Investigação Médica em Ressonância Magnética (LIM 44), São Paulo SP, Brazil.
| | - Carolina de Medeiros Rimkus
- Universidade de São Paulo, Hospital das Clínicas, Departamento de Radiologia e Oncologia, Seção de Neurorradiologia, Faculdade de Medicina, São Paulo SP, Brazil.
- Rede D'Or São Luiz, Departamento de Radiologia e Diagnóstico por Imagem, São Paulo SP, Brazil.
- Universidade de São Paulo, Laboratório de Investigação Médica em Ressonância Magnética (LIM 44), São Paulo SP, Brazil.
| | - Leandro Tavares Lucato
- Universidade de São Paulo, Hospital das Clínicas, Departamento de Radiologia e Oncologia, Seção de Neurorradiologia, Faculdade de Medicina, São Paulo SP, Brazil.
- Diagnósticos da América SA, São Paulo SP, Brazil.
| | | | - Felipe Campos Kitamura
- Diagnósticos da América SA, São Paulo SP, Brazil.
- Universidade Federal de São Paulo, São Paulo SP, Brazil.
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Ye X, Fang L, Chen Y, Tong J, Ning X, Feng L, Xu Y, Yang D. Performance comparison of two automated digital morphology analyzers for leukocyte differential in patients with malignant hematological diseases: Mindray MC-80 and Sysmex DI-60. Int J Lab Hematol 2024; 46:457-465. [PMID: 38212663 DOI: 10.1111/ijlh.14227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 12/28/2023] [Indexed: 01/13/2024]
Abstract
BACKGROUND The MC-80 (Mindray, Shenzhen, China), a newly available artificial intelligence (AI)-based digital morphology analyzer, is the focus of this study. We aim to compare the leukocyte differential performance of the Mindray MC-80 with that of the Sysmex DI-60 and the gold standard, manual microscopy. METHODS A total of 100 abnormal peripheral blood (PB) smears were compared across the MC-80, DI-60, and manual microscopy. Sensitivity, specificity, predictive value, and efficiency were calculated according to the Clinical and Laboratory Standards Institute (CLSI) EP12-A2 guidelines. Comparisons were made using Bland-Altman analysis and Passing-Bablok regression analysis. Additionally, within-run imprecision was evaluated using five samples, each with varying percentages of mature leukocytes and blasts, in accordance with CLSI EP05-A3 guidelines. RESULTS The within-run coefficient of variation (%CV) of the MC-80 for most cell classes in the five samples was lower than that of the DI-60. Sensitivities for the MC-80 ranged from 98.2% for nucleated red blood cells (NRBC) to 28.6% for reactive lymphocytes. The DI-60's sensitivities varied between 100% for basophils and reactive lymphocytes, and 11.1% for metamyelocytes. Both analyzers demonstrated high specificity, negative predictive value, and efficiency, with over 90% for most cell classes. However, the DI-60 showed relatively lower specificity for lymphocytes (73.2%) and lower efficiency for blasts and lymphocytes (80.1% and 78.6%, respectively) compared with the MC-80. Bland-Altman analysis indicated that the absolute mean differences (%) ranged from 0.01 to 4.57 in MC-80 versus manual differential and 0.01 to 3.39 in DI-60 versus manual differential. After verification by technicians, both analyzers exhibited a very high correlation (r = 0.90-1.00) with the manual differential results in neutrophils, lymphocytes, and blasts. CONCLUSIONS The Mindray MC-80 demonstrated good performance for leukocyte differential in PB smears, notably exhibiting higher sensitivity for blasts identification than the DI-60.
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Affiliation(s)
- Xianfei Ye
- Department of Laboratory Medicine, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, People's Republic of China
- Key Laboratory of Clinical In Vitro Diagnostic Techniques of Zhejiang Province, Hangzhou, People's Republic of China
| | - Lijuan Fang
- Hangzhou Dian Medical Laboratory Center Co., Ltd, Hangzhou, People's Republic of China
| | - Yunying Chen
- Department of Laboratory Medicine, Hangzhou Children's Hospital, Hangzhou, People's Republic of China
| | - Jixiang Tong
- Department of Hematology, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, People's Republic of China
| | - Xiaoni Ning
- Hangzhou Dian Medical Laboratory Center Co., Ltd, Hangzhou, People's Republic of China
| | - Lanjun Feng
- Hangzhou Dian Medical Laboratory Center Co., Ltd, Hangzhou, People's Republic of China
| | - Yuting Xu
- Department of Hematology, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, People's Republic of China
| | - Dagan Yang
- Department of Laboratory Medicine, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, People's Republic of China
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Frost EK, Bosward R, Aquino YSJ, Braunack-Mayer A, Carter SM. Facilitating public involvement in research about healthcare AI: A scoping review of empirical methods. Int J Med Inform 2024; 186:105417. [PMID: 38564959 DOI: 10.1016/j.ijmedinf.2024.105417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 03/06/2024] [Accepted: 03/17/2024] [Indexed: 04/04/2024]
Abstract
OBJECTIVE With the recent increase in research into public views on healthcare artificial intelligence (HCAI), the objective of this review is to examine the methods of empirical studies on public views on HCAI. We map how studies provided participants with information about HCAI, and we examine the extent to which studies framed publics as active contributors to HCAI governance. MATERIALS AND METHODS We searched 5 academic databases and Google Advanced for empirical studies investigating public views on HCAI. We extracted information including study aims, research instruments, and recommendations. RESULTS Sixty-two studies were included. Most were quantitative (N = 42). Most (N = 47) reported providing participants with background information about HCAI. Despite this, studies often reported participants' lack of prior knowledge about HCAI as a limitation. Over three quarters (N = 48) of the studies made recommendations that envisaged public views being used to guide governance of AI. DISCUSSION Provision of background information is an important component of facilitating research with publics on HCAI. The high proportion of studies reporting participants' lack of knowledge about HCAI as a limitation reflects the need for more guidance on how information should be presented. A minority of studies adopted technocratic positions that construed publics as passive beneficiaries of AI, rather than as active stakeholders in HCAI design and implementation. CONCLUSION This review draws attention to how public roles in HCAI governance are constructed in empirical studies. To facilitate active participation, we recommend that research with publics on HCAI consider methodological designs that expose participants to diverse information sources.
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Affiliation(s)
- Emma Kellie Frost
- Australian Centre for Health Engagement, Evidence and Values, School of Health and Society, Faculty of the Arts, Social Sciences, and Humanities, University of Wollongong, Australia.
| | - Rebecca Bosward
- Australian Centre for Health Engagement, Evidence and Values, School of Health and Society, Faculty of the Arts, Social Sciences, and Humanities, University of Wollongong, Australia.
| | - Yves Saint James Aquino
- Australian Centre for Health Engagement, Evidence and Values, School of Health and Society, Faculty of the Arts, Social Sciences, and Humanities, University of Wollongong, Australia.
| | - Annette Braunack-Mayer
- Australian Centre for Health Engagement, Evidence and Values, School of Health and Society, Faculty of the Arts, Social Sciences, and Humanities, University of Wollongong, Australia.
| | - Stacy M Carter
- Australian Centre for Health Engagement, Evidence and Values, School of Health and Society, Faculty of the Arts, Social Sciences, and Humanities, University of Wollongong, Australia.
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20
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Liu Y, Uttam S. Perspective on quantitative phase imaging to improve precision cancer medicine. J Biomed Opt 2024; 29:S22705. [PMID: 38584967 PMCID: PMC10996848 DOI: 10.1117/1.jbo.29.s2.s22705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 03/03/2024] [Accepted: 03/15/2024] [Indexed: 04/09/2024]
Abstract
Significance Quantitative phase imaging (QPI) offers a label-free approach to non-invasively characterize cellular processes by exploiting their refractive index based intrinsic contrast. QPI captures this contrast by translating refractive index associated phase shifts into intensity-based quantifiable data with nanoscale sensitivity. It holds significant potential for advancing precision cancer medicine by providing quantitative characterization of the biophysical properties of cells and tissue in their natural states. Aim This perspective aims to discuss the potential of QPI to increase our understanding of cancer development and its response to therapeutics. It also explores new developments in QPI methods towards advancing personalized cancer therapy and early detection. Approach We begin by detailing the technical advancements of QPI, examining its implementations across transmission and reflection geometries and phase retrieval methods, both interferometric and non-interferometric. The focus then shifts to QPI's applications in cancer research, including dynamic cell mass imaging for drug response assessment, cancer risk stratification, and in-vivo tissue imaging. Results QPI has emerged as a crucial tool in precision cancer medicine, offering insights into tumor biology and treatment efficacy. Its sensitivity to detecting nanoscale changes holds promise for enhancing cancer diagnostics, risk assessment, and prognostication. The future of QPI is envisioned in its integration with artificial intelligence, morpho-dynamics, and spatial biology, broadening its impact in cancer research. Conclusions QPI presents significant potential in advancing precision cancer medicine and redefining our approach to cancer diagnosis, monitoring, and treatment. Future directions include harnessing high-throughput dynamic imaging, 3D QPI for realistic tumor models, and combining artificial intelligence with multi-omics data to extend QPI's capabilities. As a result, QPI stands at the forefront of cancer research and clinical application in cancer care.
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Affiliation(s)
- Yang Liu
- University of Illinois Urbana-Champaign, Beckman Institute for Advanced Science and Technology, Cancer Center at Illinois, Department of Bioengineering, Department of Electrical and Computer Engineering, Urbana, Illinois, United States
- University of Pittsburgh, Departments of Medicine and Bioengineering, Pittsburgh, Pennsylvania, United States
| | - Shikhar Uttam
- University of Pittsburgh, Department of Computational and Systems Biology, Pittsburgh, Pennsylvania, United States
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21
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Chen P, Fu R, Shi Y, Liu C, Yang C, Su Y, Lu T, Zhou P, He W, Guo Q, Fei C. Optimizing BP neural network algorithm for Pericarpium Citri Reticulatae (Chenpi) origin traceability based on computer vision and ultra-fast gas-phase electronic nose data fusion. Food Chem 2024; 442:138408. [PMID: 38241985 DOI: 10.1016/j.foodchem.2024.138408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 01/03/2024] [Accepted: 01/08/2024] [Indexed: 01/21/2024]
Abstract
This study utilized computer vision to extract color and texture features of Pericarpium Citri Reticulatae (PCR). The ultra-fast gas-phase electronic nose (UF-GC-E-nose) technique successfully identified 98 volatile components, including olefins, alcohols, and esters, which significantly contribute to the flavor profile of PCR. Multivariate statistical Analysis was applied to the appearance traits of PCR, identifying 57 potential marker-trait factors (VIP > 1 and P < 0.05) from the 118 trait factors that can distinguish PCR from different origins. These factors include color, texture, and odor traits. By integrating multivariate statistical Analysis with the BP neural network algorithm, a novel artificial intelligence algorithm was developed and optimized for traceability of PCR origin. This algorithm achieved a 100% discrimination rate in differentiating PCR samples from various origins. This study offers a valuable reference and data support for developing intelligent algorithms that utilize data fusion from multiple intelligent sensory technologies to achieve rapid traceability of food origins.
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Affiliation(s)
- Peng Chen
- Institute of Chinese Medicinal Materials, Nanjing Agricultural University, Nanjing 210095, China
| | - Rao Fu
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Yabo Shi
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Chang Liu
- Institute of Chinese Medicinal Materials, Nanjing Agricultural University, Nanjing 210095, China
| | - Chenlu Yang
- Institute of Chinese Medicinal Materials, Nanjing Agricultural University, Nanjing 210095, China
| | - Yong Su
- Institute of Chinese Medicinal Materials, Nanjing Agricultural University, Nanjing 210095, China
| | - Tulin Lu
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Peina Zhou
- State Key Laboratory of Natural Medicines, School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing 210009, China
| | - Weitong He
- Jiangsu Wigroup Technologies Co., Ltd., Nanjing 210000, China
| | - Qiaosheng Guo
- Institute of Chinese Medicinal Materials, Nanjing Agricultural University, Nanjing 210095, China.
| | - Chenghao Fei
- Institute of Chinese Medicinal Materials, Nanjing Agricultural University, Nanjing 210095, China.
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22
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Branda F. The impact of artificial intelligence in the fight against antimicrobial resistance. Infect Dis (Lond) 2024; 56:484-486. [PMID: 38515266 DOI: 10.1080/23744235.2024.2331255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Accepted: 03/11/2024] [Indexed: 03/23/2024] Open
Affiliation(s)
- Francesco Branda
- Unit of Medical Statistics and Molecular Epidemiology, University Campus Bio-Medico of Rome, Rome, Italy
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Favero TG. Using artificial intelligence platforms to support student learning in physiology. Adv Physiol Educ 2024; 48:193-199. [PMID: 38269404 DOI: 10.1152/advan.00213.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 12/06/2023] [Accepted: 01/17/2024] [Indexed: 01/26/2024]
Abstract
The advent of artificial intelligence (AI)-powered language models presents new opportunities and challenges in education. By teaching students how to craft prompts that elicit insightful responses, faculty can scaffold activities where AI acts as a supplemental resource to amplify critical thinking and support student learning. Ongoing dialogue and iteration focused on ethical usage norms can achieve the right balance between emerging technology and foundational skills development. With care and intention, AI-assisted study tactics offer students personalized support while adhering to academic standards. While AI-powered tools provide many positive opportunities, students and faculty need to learn about and use them responsibly and ethically, not as replacements for required thinking and effort. Before implementing these AI tools for studying biology, there are several key things to discuss with students. This article outlines several ways that students can employ these tools to support better learning along with a set of guidelines for all to be wary of when implementing these in an academic setting.NEW & NOTEWORTHY Utilizing of artificial intelligence tools offers a promising new technology to support student learning. This article outlines several ways that students can employ these tools to support better learning along with a set of guidelines for all to be wary of when implementing these in an academic setting.
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Affiliation(s)
- Terence G Favero
- Department of Biology, University of Portland, Portland, Oregon, United States
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24
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Li S, Li H, Lian R, Xie J, Feng R. New perspective of small-molecule antiviral drugs development for RNA viruses. Virology 2024; 594:110042. [PMID: 38492519 DOI: 10.1016/j.virol.2024.110042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 02/20/2024] [Accepted: 03/01/2024] [Indexed: 03/18/2024]
Abstract
High variability and adaptability of RNA viruses allows them to spread between humans and animals, causing large-scale infectious diseases which seriously threat human and animal health and social development. At present, AIDS, viral hepatitis and other viral diseases with high incidence and low cure rate are still spreading around the world. The outbreaks of Ebola, Zika, dengue and in particular of the global pandemic of COVID-19 have presented serious challenges to the global public health system. The development of highly effective and broad-spectrum antiviral drugs is a substantial and urgent research subject to deal with the current RNA virus infection and the possible new viral infections in the future. In recent years, with the rapid development of modern disciplines such as artificial intelligence technology, bioinformatics, molecular biology, and structural biology, some new strategies and targets for antivirals development have emerged. Here we review the main strategies and new targets for developing small-molecule antiviral drugs against RNA viruses through the analysis of the new drug development progress against several highly pathogenic RNA viruses, to provide clues for development of future antivirals.
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Affiliation(s)
- Shasha Li
- College of Life Science and Engineering, Northwest Minzu University, Lanzhou, 730030, China; Key Laboratory of Biotechnology and Bioengineering of State Ethnic Affairs Commission, Biomedical Research Center, Northwest Minzu University, Lanzhou, 730030, China
| | - Huixia Li
- Key Laboratory of Biotechnology and Bioengineering of State Ethnic Affairs Commission, Biomedical Research Center, Northwest Minzu University, Lanzhou, 730030, China
| | - Ruiya Lian
- College of Life Science and Engineering, Northwest Minzu University, Lanzhou, 730030, China; Key Laboratory of Biotechnology and Bioengineering of State Ethnic Affairs Commission, Biomedical Research Center, Northwest Minzu University, Lanzhou, 730030, China
| | - Jingying Xie
- College of Life Science and Engineering, Northwest Minzu University, Lanzhou, 730030, China; Key Laboratory of Biotechnology and Bioengineering of State Ethnic Affairs Commission, Biomedical Research Center, Northwest Minzu University, Lanzhou, 730030, China
| | - Ruofei Feng
- Key Laboratory of Biotechnology and Bioengineering of State Ethnic Affairs Commission, Biomedical Research Center, Northwest Minzu University, Lanzhou, 730030, China.
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Kim K, Yoon Y, Shin S. Explainable prediction of problematic smartphone use among South Korea's children and adolescents using a Machine learning approach. Int J Med Inform 2024; 186:105441. [PMID: 38564961 DOI: 10.1016/j.ijmedinf.2024.105441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 03/28/2024] [Accepted: 03/28/2024] [Indexed: 04/04/2024]
Abstract
BACKGROUND Korea is known for its technological prowess, has the highest smartphone ownership rate in the world at 95%, and the smallest gap in smartphone ownership between generations. Since the onset of the COVID-19 pandemic, problematic smartphone use is becoming more prevalent among Korean children and adolescent owing to limited school attendance and outdoor activities, resulting in increased reliance on smartphones. 40.1% of adolescents are classified as high-risk, with only the adolescent group showing a persistent rise year after year. OBJECTIVE The study purpose is to present data-driven analysis results for predicting and preventing smartphone addiction in Korea, where problematic smartphone use is severe. PARTICIPANTS AND METHODS To predict the risk of problematic smartphone use in Korean children and adolescents at an early stage, we used data collected from the Smartphone Overdependence Survey conducted by the National Information Society Agency between 2017 and 2021. Eight representative machine and deep learning algorithms were used to predict groups at high risk for smartphone addiction: Logistic Regression, Random Forest, Gradient Boosting Machine (GBM), extreme Gradient Boosting (XGBoost), Light GBM, Categorical Boosting, Multilayer Perceptron, and Convolutional Neural Network. RESULTS The XGBoost ensemble algorithm predicted 87.60% of participants at risk of future problematic smartphone usebased on precision. Our results showed that prolonged use of games, webtoons/web novels, and e-books, which have not been found in previous studies, further increased the risk of problematic smartphone use. CONCLUSIONS Artificial intelligence algorithms have potential predictive and explanatory capabilities for identifying early signs of problematic smartphone use in adolescents and young children. We recommend that a variety of healthy, beneficial, and face-to-face activities be offered as alternatives to smartphones for leisure and play culture.
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Affiliation(s)
- Kyungwon Kim
- School of International Trade and Business, Incheon National University, Incheon 22012, Republic of Korea
| | - Yoewon Yoon
- Department of Social Welfare, Dongguk University, Seoul 04620, Republic of Korea
| | - Soomin Shin
- Department of Social Service, Social Welfare Major, Yuhan University, Bucheon 14780, Republic of Korea.
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Romero-Oraá R, Herrero-Tudela M, López MI, Hornero R, García M. Attention-based deep learning framework for automatic fundus image processing to aid in diabetic retinopathy grading. Comput Methods Programs Biomed 2024; 249:108160. [PMID: 38583290 DOI: 10.1016/j.cmpb.2024.108160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 01/26/2024] [Accepted: 03/30/2024] [Indexed: 04/09/2024]
Abstract
BACKGROUND AND OBJECTIVE Early detection and grading of Diabetic Retinopathy (DR) is essential to determine an adequate treatment and prevent severe vision loss. However, the manual analysis of fundus images is time consuming and DR screening programs are challenged by the availability of human graders. Current automatic approaches for DR grading attempt the joint detection of all signs at the same time. However, the classification can be optimized if red lesions and bright lesions are independently processed since the task gets divided and simplified. Furthermore, clinicians would greatly benefit from explainable artificial intelligence (XAI) to support the automatic model predictions, especially when the type of lesion is specified. As a novelty, we propose an end-to-end deep learning framework for automatic DR grading (5 severity degrees) based on separating the attention of the dark structures from the bright structures of the retina. As the main contribution, this approach allowed us to generate independent interpretable attention maps for red lesions, such as microaneurysms and hemorrhages, and bright lesions, such as hard exudates, while using image-level labels only. METHODS Our approach is based on a novel attention mechanism which focuses separately on the dark and the bright structures of the retina by performing a previous image decomposition. This mechanism can be seen as a XAI approach which generates independent attention maps for red lesions and bright lesions. The framework includes an image quality assessment stage and deep learning-related techniques, such as data augmentation, transfer learning and fine-tuning. We used the architecture Xception as a feature extractor and the focal loss function to deal with data imbalance. RESULTS The Kaggle DR detection dataset was used for method development and validation. The proposed approach achieved 83.7 % accuracy and a Quadratic Weighted Kappa of 0.78 to classify DR among 5 severity degrees, which outperforms several state-of-the-art approaches. Nevertheless, the main result of this work is the generated attention maps, which reveal the pathological regions on the image distinguishing the red lesions and the bright lesions. These maps provide explainability to the model predictions. CONCLUSIONS Our results suggest that our framework is effective to automatically grade DR. The separate attention approach has proven useful for optimizing the classification. On top of that, the obtained attention maps facilitate visual interpretation for clinicians. Therefore, the proposed method could be a diagnostic aid for the early detection and grading of DR.
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Affiliation(s)
- Roberto Romero-Oraá
- Biomedical Engineering Group, University of Valladolid, Valladolid, 47011, Spain; Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Spain.
| | - María Herrero-Tudela
- Biomedical Engineering Group, University of Valladolid, Valladolid, 47011, Spain
| | - María I López
- Biomedical Engineering Group, University of Valladolid, Valladolid, 47011, Spain; Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Spain
| | - Roberto Hornero
- Biomedical Engineering Group, University of Valladolid, Valladolid, 47011, Spain; Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Spain
| | - María García
- Biomedical Engineering Group, University of Valladolid, Valladolid, 47011, Spain; Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Spain
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Krishnan SR, Bung N, Srinivasan R, Roy A. Target-specific novel molecules with their recipe: Incorporating synthesizability in the design process. J Mol Graph Model 2024; 129:108734. [PMID: 38442440 DOI: 10.1016/j.jmgm.2024.108734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Revised: 02/14/2024] [Accepted: 02/15/2024] [Indexed: 03/07/2024]
Abstract
Application of Artificial intelligence (AI) in drug discovery has led to several success stories in recent times. While traditional methods mostly relied upon screening large chemical libraries for early-stage drug-design, de novo design can help identify novel target-specific molecules by sampling from a much larger chemical space. Although this has increased the possibility of finding diverse and novel molecules from previously unexplored chemical space, this has also posed a great challenge for medicinal chemists to synthesize at least some of the de novo designed novel molecules for experimental validation. To address this challenge, in this work, we propose a novel forward synthesis-based generative AI method, which is used to explore the synthesizable chemical space. The method uses a structure-based drug design framework, where the target protein structure and a target-specific seed fragment from co-crystal structures can be the initial inputs. A random fragment from a purchasable fragment library can also be the input if a target-specific fragment is unavailable. Then a template-based forward synthesis route prediction and molecule generation is performed in parallel using the Monte Carlo Tree Search (MCTS) method where, the subsequent fragments for molecule growth can again be obtained from a purchasable fragment library. The rewards for each iteration of MCTS are computed using a drug-target affinity (DTA) model based on the docking pose of the generated reaction intermediates at the binding site of the target protein of interest. With the help of the proposed method, it is now possible to overcome one of the major obstacles posed to the AI-based drug design approaches through the ability of the method to design novel target-specific synthesizable molecules.
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Affiliation(s)
| | - Navneet Bung
- TCS Research (Life Sciences Division), Tata Consultancy Services Limited, Hyderabad, 500081, India
| | - Rajgopal Srinivasan
- TCS Research (Life Sciences Division), Tata Consultancy Services Limited, Hyderabad, 500081, India
| | - Arijit Roy
- TCS Research (Life Sciences Division), Tata Consultancy Services Limited, Hyderabad, 500081, India.
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de Villedon de Naide V, Maes JD, Villegas-Martinez M, Ribal I, Maillot A, Ozenne V, Montier G, Boullé T, Sridi S, Gut P, Küstner T, Stuber M, Cochet H, Bustin A. Fully automated contrast selection of joint bright- and black-blood late gadolinium enhancement imaging for robust myocardial scar assessment. Magn Reson Imaging 2024; 109:256-263. [PMID: 38522623 DOI: 10.1016/j.mri.2024.03.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 03/14/2024] [Accepted: 03/21/2024] [Indexed: 03/26/2024]
Abstract
PURPOSE Joint bright- and black-blood MRI techniques provide improved scar localization and contrast. Black-blood contrast is obtained after the visual selection of an optimal inversion time (TI) which often results in uncertainties, inter- and intra-observer variability and increased workload. In this work, we propose an artificial intelligence-based algorithm to enable fully automated TI selection and simplify myocardial scar imaging. METHODS The proposed algorithm first localizes the left ventricle using a U-Net architecture. The localized left cavity centroid is extracted and a squared region of interest ("focus box") is created around the resulting pixel. The focus box is then propagated on each image and the sum of the pixel intensity inside is computed. The smallest sum corresponds to the image with the lowest intensity signal within the blood pool and healthy myocardium, which will provide an ideal scar-to-blood contrast. The image's corresponding TI is considered optimal. The U-Net was trained to segment the epicardium in 177 patients with binary cross-entropy loss. The algorithm was validated retrospectively in 152 patients, and the agreement between the algorithm and two magnetic resonance (MR) operators' prediction of TI values was calculated using the Fleiss' kappa coefficient. Thirty focus box sizes, ranging from 2.3mm2 to 20.3cm2, were tested. Processing times were measured. RESULTS The U-Net's Dice score was 93.0 ± 0.1%. The proposed algorithm extracted TI values in 2.7 ± 0.1 s per patient (vs. 16.0 ± 8.5 s for the operator). An agreement between the algorithm's prediction and the MR operators' prediction was found in 137/152 patients (κ= 0.89), for an optimal focus box of size 2.3cm2. CONCLUSION The proposed fully-automated algorithm has potential of reducing uncertainties, variability, and workload inherent to manual approaches with promise for future clinical implementation for joint bright- and black-blood MRI.
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Affiliation(s)
| | - Jean-David Maes
- CHU de Bordeaux, Department of Cardiovascular Imaging, INSERM, U 1045, F-33000 Bordeaux, France
| | | | - Indra Ribal
- Université de Bordeaux, INSERM, CRCTB, U 1045, IHU Liryc, F-33000 Bordeaux, France
| | - Aurélien Maillot
- Université de Bordeaux, INSERM, CRCTB, U 1045, IHU Liryc, F-33000 Bordeaux, France
| | - Valéry Ozenne
- Université de Bordeaux, INSERM, CRCTB, U 1045, IHU Liryc, F-33000 Bordeaux, France
| | - Géraldine Montier
- CHU de Bordeaux, Department of Cardiovascular Imaging, INSERM, U 1045, F-33000 Bordeaux, France
| | - Thibaut Boullé
- CHU de Bordeaux, Department of Cardiovascular Imaging, INSERM, U 1045, F-33000 Bordeaux, France
| | - Soumaya Sridi
- CHU de Bordeaux, Department of Cardiovascular Imaging, INSERM, U 1045, F-33000 Bordeaux, France
| | - Pauline Gut
- Université de Bordeaux, INSERM, CRCTB, U 1045, IHU Liryc, F-33000 Bordeaux, France; Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Thomas Küstner
- Medical Image and Data Analysis (MIDAS.lab), Department of Diagnostic and Interventional Radiology, University Hospital of Tübingen, 72076 Tübingen, Germany
| | - Matthias Stuber
- Université de Bordeaux, INSERM, CRCTB, U 1045, IHU Liryc, F-33000 Bordeaux, France; Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; Center for Biomedical Imaging (CIBM), Lausanne, Switzerland
| | - Hubert Cochet
- Université de Bordeaux, INSERM, CRCTB, U 1045, IHU Liryc, F-33000 Bordeaux, France; CHU de Bordeaux, Department of Cardiovascular Imaging, INSERM, U 1045, F-33000 Bordeaux, France
| | - Aurélien Bustin
- Université de Bordeaux, INSERM, CRCTB, U 1045, IHU Liryc, F-33000 Bordeaux, France; CHU de Bordeaux, Department of Cardiovascular Imaging, INSERM, U 1045, F-33000 Bordeaux, France; Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
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Shi L, Jia W, Zhang R, Fan Z, Bian W, Mo H. High-throughput analysis of hazards in novel food based on the density functional theory and multimodal deep learning. Food Chem 2024; 442:138468. [PMID: 38266417 DOI: 10.1016/j.foodchem.2024.138468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Revised: 12/30/2023] [Accepted: 01/15/2024] [Indexed: 01/26/2024]
Abstract
The emergence of cultured meat presents the potential for personalized food additive manufacturing, offering a solution to address future food resource scarcity. Processing raw materials and products in synthetic food products poses challenges in identifying hazards, impacting the entire industrial chain during the industry's further evolution. It is crucial to examine the correlation of biological information at different levels and to reveal the temporal dynamics jointly. Proposed active prevention method includes four aspects: (i) Investigating the molecular-level mechanism underlying the binding and dissociation of hazards with proteins represents a novel approach to mitigate matrix effect. (ii) Identifying distinct fragments is a pivotal advancement toward developing a novel screening strategy for hazards throughout the food chain. (iii) Designing an artificial intelligence model-based approach to acquire multi-dimensional histology data also holds significant potential for various applications. (iv) Integrating multimodal data is a practical approach to enhance evaluation and feedback control accuracy.
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Affiliation(s)
- Lin Shi
- School of Food and Biological Engineering, Shaanxi University of Science and Technology, Xi'an 710021, China
| | - Wei Jia
- School of Food and Biological Engineering, Shaanxi University of Science and Technology, Xi'an 710021, China; Shaanxi Testing Institute of Product Quality Supervision, Xi'an, Shaanxi 710048, China; Shaanxi Research Institute of Agricultural Products Processing Technology, Xi'an 710021, China; Shaanxi Sky Pet Biotechnology Co., Ltd, Xi'an 710075, China.
| | - Rong Zhang
- School of Food and Biological Engineering, Shaanxi University of Science and Technology, Xi'an 710021, China
| | - Zibian Fan
- School of Food and Biological Engineering, Shaanxi University of Science and Technology, Xi'an 710021, China
| | - Wenwen Bian
- Shaanxi Testing Institute of Product Quality Supervision, Xi'an, Shaanxi 710048, China
| | - Haizhen Mo
- School of Food and Biological Engineering, Shaanxi University of Science and Technology, Xi'an 710021, China; Shaanxi Research Institute of Agricultural Products Processing Technology, Xi'an 710021, China.
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Peschel E, Krotsetis S, Seidlein AH, Nydahl P. Opening Pandora's box by generating ICU diaries through artificial intelligence: A hypothetical study protocol. Intensive Crit Care Nurs 2024; 82:103661. [PMID: 38394982 DOI: 10.1016/j.iccn.2024.103661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 02/09/2024] [Accepted: 02/16/2024] [Indexed: 02/25/2024]
Abstract
BACKGROUND Patients and families on Intensive Care Units (ICU) benefit from ICU diaries, enhancing their coping and understanding of their experiences. Staff shortages and a limited amount of time severely restrict the application of ICU diaries. To counteract this limitation, generating diary entries from medical and nursing records using an artificial intelligence (AI) might be a solution. DESIGN AND PURPOSE Protocol for a hypothetical multi-center, mixed method study to identify the usability and impact of AI-generated ICU diaries, compared with hand-written diaries. METHOD A hand-written ICU diary will be written for patients with expected length of stay ≥ 72 h by trained nursing staff and families. Additionally at discharge, the medical and nursing records are analyzed by an AI software, transformed into understandable, empathic diary entries, and printed as diary. Based on an appointment with patients within 3 months, diaries are read in randomized order by trained clinicians with the patients and families. Patients and families will be interviewed about their experiences of reading both diaries. In addition, usability of diaries will be evaluated by a questionnaire. EXPECTED FINDINGS AND RESULTS Patients and families describe the similarities and differences of language and the content of the different diaries. In addition, concerns can be expressed about the generation and data processing by AI. IMPLICATIONS FOR PRACTICE Professional nursing involves empathic communication, patient-centered care, and evidence-based interventions. Diaries, beneficial for ICU patients and families, could potentially be generated by Artificial Intelligence, raising ethical and professional considerations about AI's role in complementing or substituting nurses in diary writing. CONCLUSIONS Generating AI-based entries for ICU diaries is feasible, but raises serious questions about nursing ethics, empathy, data protection, and values of professional nurses. Researchers and developers shall discuss these questions in detail, before starting such projects and opening Pandora's box, that can never be closed afterwards.
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Affiliation(s)
- Ella Peschel
- University Hospital of Schleswig-Holstein, Kiel, Germany
| | | | | | - Peter Nydahl
- University Hospital of Schleswig-Holstein, Nursing Research and Development, Kiel, Germany; Nursing Science and Development, Paracelsus Medical University, Salzburg, Austria.
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Pathak A, Verma N, Tripathi S, Mishra A, Poluri KM. Nanosensor based approaches for quantitative detection of heparin. Talanta 2024; 273:125873. [PMID: 38460425 DOI: 10.1016/j.talanta.2024.125873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Revised: 02/23/2024] [Accepted: 03/03/2024] [Indexed: 03/11/2024]
Abstract
Heparin, being a widely employed anticoagulant in numerus clinical complications, requires strict quantification and qualitative screening to ensure the safety of patients from potential threat of thrombocytopenia. However, the intricacy of heparin's chemical structures and low abundance hinders the precise monitoring of its level and quality in clinical settings. Conventional laboratory assays have limitations in sensitivity and specificity, necessitating the development of innovative approaches. In this context, nanosensors emerged as a promising solution due to enhanced sensitivity, selectivity, and ability to detect heparin even at low concentrations. This review delves into a range of sensing approaches including colorimetric, fluorometric, surface-enhanced Raman spectroscopy, and electrochemical techniques using different types of nanomaterials, thus providing insights of its principles, capabilities, and limitations. Moreover, integration of smart-phone with nanosensors for point of care diagnostics has also been explored. Additionally, recent advances in nanopore technologies, artificial intelligence (AI) and machine learning (ML) have been discussed offering specificity against contaminants present in heparin to ensure its quality. By consolidating current knowledge and highlighting the potential of nanosensors, this review aims to contribute to the advancement of efficient, reliable, and economical heparin detection methods providing improved patient care.
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Affiliation(s)
- Aakanksha Pathak
- Centre for Nanotechnology, Indian Institute of Technology Roorkee, Roorkee, 247667, Uttarakhand, India
| | - Nishchay Verma
- Centre for Nanotechnology, Indian Institute of Technology Roorkee, Roorkee, 247667, Uttarakhand, India
| | - Shweta Tripathi
- Department of Biosciences and Bioengineering, Indian Institute of Technology Roorkee, Roorkee, 247667, Uttarakhand, India
| | - Amit Mishra
- Cellular and Molecular Neurobiology Unit, Indian Institute of Technology Jodhpur, Jodhpur, 342011, Rajasthan, India
| | - Krishna Mohan Poluri
- Centre for Nanotechnology, Indian Institute of Technology Roorkee, Roorkee, 247667, Uttarakhand, India; Department of Biosciences and Bioengineering, Indian Institute of Technology Roorkee, Roorkee, 247667, Uttarakhand, India.
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Zhang Z, Yang S, Wang X. Schistocyte detection in artificial intelligence age. Int J Lab Hematol 2024; 46:427-433. [PMID: 38472155 DOI: 10.1111/ijlh.14260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 02/13/2024] [Indexed: 03/14/2024]
Abstract
Schistocytes are fragmented red blood cells produced as a result of mechanical damage to erythrocytes, usually due to microangiopathic thrombotic diseases or mechanical factors. The early laboratory detection of schistocytes has a critical impact on the timely diagnosis, effective treatment, and positive prognosis of diseases such as thrombocytopenic purpura and hemolytic uremic syndrome. Due to the rapid development of science and technology, laboratory hematology has also advanced. The accuracy and efficiency of tests performed by fully automated hematology analyzers and fully automated morphology analyzers have been considerably improved. In recent years, substantial improvements in computing power and machine learning (ML) algorithm development have dramatically extended the limits of the potential of autonomous machines. The rapid development of machine learning and artificial intelligence (AI) has led to the iteration and upgrade of automated detection of schistocytes. However, along with significantly facilitated operation processes, AI has brought challenges. This review summarizes the progress in laboratory schistocyte detection, the relationship between schistocytes and clinical diseases, and the progress of AI in the detection of schistocytes. In addition, current challenges and possible solutions are discussed, as well as the great potential of AI techniques for schistocyte testing in peripheral blood.
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Affiliation(s)
- Zeng Zhang
- Department of Clinical Laboratory, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Zhejiang, Hangzhou, China
- Key Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province, Zhejiang, Hangzhou, China
| | - Su Yang
- Department of Clinical Laboratory, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Zhejiang, Hangzhou, China
- Key Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province, Zhejiang, Hangzhou, China
| | - Xiuhong Wang
- Department of Clinical Laboratory, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Zhejiang, Hangzhou, China
- Key Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province, Zhejiang, Hangzhou, China
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Qin Y, Qin X, Zhang J, Guo X. Artificial intelligence: The future for multimodality imaging of right ventricle. Int J Cardiol 2024; 404:131970. [PMID: 38490268 DOI: 10.1016/j.ijcard.2024.131970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Revised: 03/05/2024] [Accepted: 03/12/2024] [Indexed: 03/17/2024]
Abstract
The crucial pathophysiological and prognostic roles of the right ventricle in various diseases have been well-established. Nonetheless, conventional cardiovascular imaging modalities are frequently associated with intrinsic limitations when evaluating right ventricular (RV) morphology and function. The integration of artificial intelligence (AI) in multimodality imaging presents a promising avenue to circumvent these obstacles, paving the way for future fully automated imaging paradigms. This review aimed to address the current challenges faced by clinicians and researchers in integrating RV imaging and AI technology, to provide a comprehensive overview of the current applications of AI in RV imaging, and to offer insights into future directions, opportunities, and potential challenges in this rapidly advancing field.
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Affiliation(s)
- Yuhan Qin
- Department of Cardiology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Xiaohan Qin
- Department of Cardiology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Jing Zhang
- Department of Cardiology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Xiaoxiao Guo
- Department of Cardiology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.
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Makhlouf E, Alenezi A, Shokr EA. Effectiveness of designing a knowledge-based artificial intelligence chatbot system into a nursing training program: A quasi-experimental design. Nurse Education Today 2024; 137:106159. [PMID: 38493588 DOI: 10.1016/j.nedt.2024.106159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Revised: 03/05/2024] [Accepted: 03/10/2024] [Indexed: 03/19/2024]
Abstract
BACKGROUND Chatbots have gained popularity in the healthcare industry due to their ability to provide prompt and accurate responses to a wide range of inquiries. This has been particularly beneficial for nurses who often require quick access to information and may have questions related to patient care. PURPOSE To evaluate the effectiveness of designing a knowledge-based artificial intelligence chatbot system for a nursing training program. METHODS The study utilized a Quasi-experimental design and collected data from a purposive sample of 73 nurses using Google Forms. The tools used in the study included 1) a structured questionnaire for nurses (a) Demographic data (b) Nurses' knowledge regarding chatbots, 2) Nurses' knowledge about artificial intelligence, 3) Nurses' perception of the application of chatbots in nursing, and 4) Nurses' opinions about the use of nursing chatbots or traditional methods of education. RESULTS There was a highly statistically significant improvement in nurses' knowledge-based chatbot systems post-intervention (p = 0.001). CONCLUSION Integrating an artificial intelligence chatbot system into a nursing training program provides nurses with easy access to reliable and evidence-based knowledge. The chatbot offers immediate answers, explanations, and up-to-date resources, empowering nurses to make informed decisions, stay updated, and served as a communication platform connecting nurses through a common language and enhance their practice. RECOMMENDATION Incorporating a knowledge-based chatbot system into a nursing training program. Furthermore, further research should be conducted to explore the long-term impact of chatbot systems on nursing practices and education.
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Affiliation(s)
- Entesar Makhlouf
- Maternity and Child Health Nursing Department, College of Nursing, Al Dawadmi, Shaqra University, 11911, Saudi Arabia.
| | - Atallah Alenezi
- Mental Health Nursing, College of Applied Medical Sciences, Shaqra University, Al Dawadmi 11911, Saudi Arabia.
| | - Eman A Shokr
- Community Health Nursing, Faculty of Nursing, Menoufia University, Egypt.
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Stogiannos N, Litosseliti L, O'Regan T, Scurr E, Barnes A, Kumar A, Malik R, Pogose M, Harvey H, McEntee MF, Malamateniou C. Black box no more: A cross-sectional multi-disciplinary survey for exploring governance and guiding adoption of AI in medical imaging and radiotherapy in the UK. Int J Med Inform 2024; 186:105423. [PMID: 38531254 DOI: 10.1016/j.ijmedinf.2024.105423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 03/12/2024] [Accepted: 03/20/2024] [Indexed: 03/28/2024]
Abstract
BACKGROUND Medical Imaging and radiotherapy (MIRT) are at the forefront of artificial intelligence applications. The exponential increase of these applications has made governance frameworks necessary to uphold safe and effective clinical adoption. There is little information about how healthcare practitioners in MIRT in the UK use AI tools, their governance and associated challenges, opportunities and priorities for the future. METHODS This cross-sectional survey was open from November to December 2022 to MIRT professionals who had knowledge or made use of AI tools, as an attempt to map out current policy and practice and to identify future needs. The survey was electronically distributed to the participants. Statistical analysis included descriptive statistics and inferential statistics on the SPSS statistical software. Content analysis was employed for the open-ended questions. RESULTS Among the 245 responses, the following were emphasised as central to AI adoption: governance frameworks, practitioner training, leadership, and teamwork within the AI ecosystem. Prior training was strongly correlated with increased knowledge about AI tools and frameworks. However, knowledge of related frameworks remained low, with different professionals showing different affinity to certain frameworks related to their respective roles. Common challenges and opportunities of AI adoption were also highlighted, with recommendations for future practice.
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Affiliation(s)
- Nikolaos Stogiannos
- Department of Radiography, City, University of London, UK; Magnitiki Tomografia Kerkyras, Greece.
| | - Lia Litosseliti
- School of Health & Psychological Sciences, City, University of London, UK.
| | - Tracy O'Regan
- The Society and College of Radiographers, London, UK.
| | | | - Anna Barnes
- King's Technology Evaluation Centre (KiTEC), School of Biomedical Engineering & Imaging Science, King's College London, UK.
| | | | | | | | | | - Mark F McEntee
- Discipline of Medical Imaging and Radiation Therapy, University College Cork, Ireland.
| | - Christina Malamateniou
- Department of Radiography, City, University of London, UK; European Society of Medical Imaging Informatics, Vienna, Austria.
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Gautam K, Pandey N, Yadav D, Parthasarathi R, Turner A, Anbumani S, Jha AN. Ecotoxicological impacts of landfill sites: Towards risk assessment, mitigation policies and the role of artificial intelligence. Sci Total Environ 2024; 927:171804. [PMID: 38513865 DOI: 10.1016/j.scitotenv.2024.171804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 03/14/2024] [Accepted: 03/16/2024] [Indexed: 03/23/2024]
Abstract
Waste disposal in landfills remains a global concern. Despite technological developments, landfill leachate poses a hazard to ecosystems and human health since it acts as a secondary reservoir for legacy and emerging pollutants. This study provides a systematic and scientometric review of the nature and toxicity of pollutants generated by landfills and means of assessing their potential risks. Regarding human health, unregulated waste disposal and pathogens in leachate are the leading causes of diseases reported in local populations. Both in vitro and in vivo approaches have been employed in the ecotoxicological risk assessment of landfill leachate, with model organisms ranging from bacteria to birds. These studies demonstrate a wide range of toxic effects that reflect the complex composition of leachate and geographical variations in climate, resource availability and management practices. Based on bioassay (and other) evidence, categories of persistent chemicals of most concern include brominated flame retardants, per- and polyfluorinated chemicals, pharmaceuticals and alkyl phenol ethoxylates. However, the emerging and more general literature on microplastic toxicity suggests that these particles might also be problematic in leachate. Various mitigation strategies have been identified, with most focussing on improving landfill design or leachate treatment, developing alternative disposal methods and reducing waste volume through recycling or using more sustainable materials. The success of these efforts will rely on policies and practices and their enforcement, which is seen as a particular challenge in developing nations and at the international (and transboundary) level. Artificial intelligence and machine learning afford a wide range of options for evaluating and reducing the risks associated with leachates and gaseous emissions from landfills, and various approaches tested or having potential are discussed. However, addressing the limitations in data collection, model accuracy, real-time monitoring and our understanding of environmental impacts will be critical for realising this potential.
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Affiliation(s)
- Krishna Gautam
- Ecotoxicology Laboratory, REACT Division, CSIR-Indian Institute of Toxicology Research, CRK Campus, Lucknow 226008, Uttar Pradesh, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Namrata Pandey
- Ecotoxicology Laboratory, REACT Division, CSIR-Indian Institute of Toxicology Research, CRK Campus, Lucknow 226008, Uttar Pradesh, India
| | - Dhvani Yadav
- Computational Toxicology Facility, CSIR-Indian Institute of Toxicology Research, Vishvigyan Bhawan, 31, Mahatma Gandhi Marg, Lucknow 226001, Uttar Pradesh, India
| | - Ramakrishnan Parthasarathi
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India; Computational Toxicology Facility, CSIR-Indian Institute of Toxicology Research, Vishvigyan Bhawan, 31, Mahatma Gandhi Marg, Lucknow 226001, Uttar Pradesh, India
| | - Andrew Turner
- School of Geography, Earth and Environmental Sciences, University of Plymouth, Drake Circus, Plymouth PL4 8AA, UK
| | - Sadasivam Anbumani
- Ecotoxicology Laboratory, REACT Division, CSIR-Indian Institute of Toxicology Research, CRK Campus, Lucknow 226008, Uttar Pradesh, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Awadhesh N Jha
- School of Biological and Marine Sciences, University of Plymouth, Drake Circus, Plymouth PL4 8AA, UK.
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Lu L, Wang L, Liu R, Zhang Y, Zheng X, Lu J, Wang X, Ye J. An efficient artificial intelligence algorithm for predicting the sensory quality of green and black teas based on the key chemical indices. Food Chem 2024; 441:138341. [PMID: 38176147 DOI: 10.1016/j.foodchem.2023.138341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 12/20/2023] [Accepted: 12/29/2023] [Indexed: 01/06/2024]
Abstract
The key components dominating the quality of green tea and black tea are still unclear. Here, we respectively produced green and black teas in March and June, and investigated the correlations between sensory quality and chemical compositions of dry teas by multivariate statistics, bioinformatics and artificial intelligence algorithm. The key chemical indices were screened out to establish tea sensory quality-prediction models based on the result of OPLS-DA and random forest, namely 4 flavonol glycosides of green tea and 8 indices of black tea (4 pigments, epigallocatechin, kaempferol-3-O-rhamnosyl-glucoside, ratios of caffeine/total catechins and epi/non-epi catechins). Compared with OPLS-DA and random forest, the support vector machine model had good sensory quality-prediction performance for both green tea and black tea (F1-score > 0.92), even based on the indices of fresh tea leaves. Our study explores the potential of artificial intelligence algorithm in classification and prediction of tea products with different sensory quality.
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Affiliation(s)
- Lu Lu
- Zhejiang University Tea Research Institute, 866 Yuhangtang Road, Hangzhou 310058, China
| | - Lu Wang
- Key Laboratory of Biology, Genetics and Breeding of Special Economic Animals and Plants, Ministry of Agriculture and Rural Affairs, National Center for Tea Plant Improvement, Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China
| | - Ruyi Liu
- Zhejiang University Tea Research Institute, 866 Yuhangtang Road, Hangzhou 310058, China
| | - Yingbin Zhang
- Key Laboratory of Biology, Genetics and Breeding of Special Economic Animals and Plants, Ministry of Agriculture and Rural Affairs, National Center for Tea Plant Improvement, Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China
| | - Xinqiang Zheng
- Zhejiang University Tea Research Institute, 866 Yuhangtang Road, Hangzhou 310058, China
| | - Jianliang Lu
- Zhejiang University Tea Research Institute, 866 Yuhangtang Road, Hangzhou 310058, China
| | - Xinchao Wang
- Key Laboratory of Biology, Genetics and Breeding of Special Economic Animals and Plants, Ministry of Agriculture and Rural Affairs, National Center for Tea Plant Improvement, Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China.
| | - Jianhui Ye
- Zhejiang University Tea Research Institute, 866 Yuhangtang Road, Hangzhou 310058, China.
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Rowan NJ. Digital technologies to unlock safe and sustainable opportunities for medical device and healthcare sectors with a focus on the combined use of digital twin and extended reality applications: A review. Sci Total Environ 2024; 926:171672. [PMID: 38485014 DOI: 10.1016/j.scitotenv.2024.171672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Revised: 03/09/2024] [Accepted: 03/10/2024] [Indexed: 03/26/2024]
Abstract
Medical devices have increased in complexity where there is a pressing need to consider design thinking and specialist training for manufacturers, healthcare and sterilization providers, and regulators. Appropriately addressing this consideration will positively inform end-to-end supply chain and logistics, production, processing, sterilization, safety, regulation, education, sustainability and circularity. There are significant opportunities to innovate and to develop appropriate digital tools to help unlock efficiencies in these important areas. This constitutes the first paper to create an awareness of and to define different digital technologies for informing and enabling medical device production from a holistic end-to-end life cycle perspective. It describes the added-value of using digital innovations to meet emerging opportunities for many disposable and reusable medical devices. It addresses the value of accessing and using integrated multi-actor HUBs that combine academia, industry, healthcare, regulators and society to help meet these opportunities. Such as cost-effective access to specialist pilot facilities and expertise that converges digital innovation, material science, biocompatibility, sterility assurance, business model and sustainability. It highlights the marked gap in academic R&D activities (PRISMA review of best publications conducted between January 2010 and January 2024) and the actual list of U.S. FDA's approved and marketed artificial intelligence/machine learning (AI/ML), and augmented reality/virtual reality (AR/VR) enabled-medical devices for different healthcare applications. Bespoke examples of benefits underlying future use of digital tools includes potential implementation of machine learning for supporting and enabling parametric release of sterilized products through efficient monitoring of critical process data (complying with ISO 11135:2014) that would benefit stakeholders. This paper also focuses on the transformative potential of combining digital twin with extended reality innovations to inform efficiencies in medical device design thinking, supply chain and training to inform patient safety, circularity and sustainability.
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Affiliation(s)
- Neil J Rowan
- Centre for Sustainable Disinfection and Sterilization, Technological University of the Shannon, Midlands Campus, Ireland; CURAM SFI Research Centre for Medical Devices, University of Galway, Ireland.
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Liu P, Dong Y, Li X, Zhang Y, Liu Z, Lu Y, Peng X, Zhai R, Chen Y. Multilayered Fe 3O 4@(ZIF-8) 3 combined with a computer-vision-enhanced immunosensor for chloramphenicol enrichment and detection. J Hazard Mater 2024; 470:134150. [PMID: 38552394 DOI: 10.1016/j.jhazmat.2024.134150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Revised: 03/24/2024] [Accepted: 03/26/2024] [Indexed: 04/25/2024]
Abstract
The misuse and overuse of chloramphenicol poses severe threats to food safety and human health. In this work, we developed a magnetic solid-phase extraction (MSPE) pretreatment material coated with a multilayered metal-organic framework (MOF), Fe3O4 @ (ZIF-8)3, for the separation and enrichment of chloramphenicol from fish. Furthermore, we designed an artificial-intelligence-enhanced single microsphere immunosensor. The inherent ultra-high porosity of the MOF and the multilayer assembly strategy allowed for efficient chloramphenicol enrichment (4.51 mg/g within 20 min). Notably, Fe3O4 @ (ZIF-8)3 exhibits a 39.20% increase in adsorption capacity compared to Fe3O4 @ZIF-8. Leveraging the remarkable decoding abilities of artificial intelligence, we achieved the highly sensitive detection of chloramphenicol using a straightforward procedure without the need for specialized equipment, obtaining a notably low detection limit of 46.42 pM. Furthermore, the assay was successfully employed to detect chloramphenicol in fish samples with high accuracy. The developed immunosensor offers a robust point-of-care testing tool for safeguarding food safety and public health.
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Affiliation(s)
- Puyue Liu
- College of Food Science and Technology, Huazhong Agricultural University, Wuhan 430070, Hubei, China
| | - Yiming Dong
- College of Food Science and Technology, Huazhong Agricultural University, Wuhan 430070, Hubei, China
| | - Xiaoxuan Li
- College of Food Science and Technology, Huazhong Agricultural University, Wuhan 430070, Hubei, China
| | - Yu Zhang
- College of Food Science and Technology, Huazhong Agricultural University, Wuhan 430070, Hubei, China
| | - Zhi Liu
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, Hubei, China
| | - Yingying Lu
- College of Food Science and Technology, Huazhong Agricultural University, Wuhan 430070, Hubei, China
| | - Xuewen Peng
- College of Food Science and Technology, Huazhong Agricultural University, Wuhan 430070, Hubei, China
| | - Ruifang Zhai
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, Hubei, China.
| | - Yiping Chen
- Academy of Food Interdisciplinary Science, School of Food Science and Technology, Dalian Polytechnic University, Dalian 116034, Liaoning, China; College of Food Science and Technology, Huazhong Agricultural University, Wuhan 430070, Hubei, China; State Key Laboratory of Marine Food Processing and Safety Control, Dalian Polytechnic University, Dalian 116034, Liaoning, China.
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40
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Li H, Gui X, Wang P, Yue Y, Li H, Fan X, Li X, Liu R. Research on rapid quality identification method of Panax notoginseng powder based on artificial intelligence sensory technology and multi-source information fusion technology. Food Chem 2024; 440:138210. [PMID: 38118320 DOI: 10.1016/j.foodchem.2023.138210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Revised: 11/13/2023] [Accepted: 12/11/2023] [Indexed: 12/22/2023]
Abstract
Panax notoginseng powder (PNP) has high medicinal value and is widely used in the medical and health food industries. However, the adulteration of PNP in the market has dramatically reduced its efficacy. Therefore, this study intends to use artificial intelligence sensory (AIS) and multi-source information fusion (MIF) technology to try to establish a quality evaluation system for different grades of PNP and adulterated Panax notoginseng powder (AD-PNP). The highest accuracy rate reached 100% in identifying PNP grade and adulteration. In the prediction of adulteration ratio and total saponin content, the optimal determination coefficients of the test set were 0.9965 and 0.9948, respectively, and the root mean square errors were 0.0109 and 0.0123, respectively. Therefore, the grade identification method of PNP and the evaluation system of AD-PNP based on AIS and MIF technology can rapidly and accurately evaluate the quality of PNP.
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Affiliation(s)
- Haiyang Li
- Henan University of Chinese Medicine, Zhengzhou, China
| | - Xinjing Gui
- Henan University of Chinese Medicine, Zhengzhou, China; The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China; Henan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Zhengzhou, China; Henan Key Laboratory for Clinical Pharmacy of Traditional Chinese Medicine, Zhengzhou, China
| | - Panpan Wang
- The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China; Henan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Zhengzhou, China; Henan Key Laboratory for Clinical Pharmacy of Traditional Chinese Medicine, Zhengzhou, China
| | - Yousong Yue
- Henan University of Chinese Medicine, Zhengzhou, China
| | - Han Li
- Henan University of Chinese Medicine, Zhengzhou, China
| | - Xuehua Fan
- Henan University of Chinese Medicine, Zhengzhou, China
| | - Xuelin Li
- The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China; Henan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Zhengzhou, China; Henan Key Laboratory for Clinical Pharmacy of Traditional Chinese Medicine, Zhengzhou, China; Co-construction Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases by Henan & Education Ministry of P.R. China, Henan University of Chinese Medicine, Zhengzhou, China
| | - Ruixin Liu
- The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China; Henan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Zhengzhou, China; Henan Key Laboratory for Clinical Pharmacy of Traditional Chinese Medicine, Zhengzhou, China; Co-construction Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases by Henan & Education Ministry of P.R. China, Henan University of Chinese Medicine, Zhengzhou, China.
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Wang S, Qiu Y, Zhu F. An updated review of functional ingredients of Manuka honey and their value-added innovations. Food Chem 2024; 440:138060. [PMID: 38211407 DOI: 10.1016/j.foodchem.2023.138060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 11/19/2023] [Accepted: 11/20/2023] [Indexed: 01/13/2024]
Abstract
Manuka honey (MH) is a highly prized natural product from the nectar of Leptospermum scoparium flowers. Increased competition on the global market drives MH product innovations. This review updates comparative and non-comparative studies to highlight nutritional, therapeutic, bioengineering, and cosmetic values of MH. MH is a good source of phenolics and unique chemical compounds, such as methylglyoxal, dihydroxyacetone, leptosperin glyoxal, methylsyringate and leptosin. Based on the evidence from in vitro, in vivo and clinical studies, multifunctional bioactive compounds of MH have exhibited anti-oxidative, anti-inflammatory, immunomodulatory, anti-microbial, and anti-cancer activities. There are controversial topics related to MH, such as MH grading, safety/efficacy, implied benefits, and maximum levels of contaminants concerned. Artificial intelligence can optimize MH studies related to chemical analysis, toxicity prediction, multi-functional mechanism exploration and product innovation.
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Affiliation(s)
- Sunan Wang
- Canadian Food and Wine Institute, Niagara College, 135 Taylor Road, Niagara-on-the-Lake, Ontario L0S 1J0, Canada; School of Chemical Sciences, The University of Auckland, Private Bag 92019, Auckland 1142, New Zealand
| | - Yi Qiu
- Division of Engineering Science, Faculty of Applied Science and Engineering, University of Toronto, 35 St. George Street, Toronto, Ontario M5S 1A4, Canada
| | - Fan Zhu
- School of Chemical Sciences, The University of Auckland, Private Bag 92019, Auckland 1142, New Zealand.
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Khan AA, Yunus R, Sohail M, Rehman TA, Saeed S, Bu Y, Jackson CD, Sharkey A, Mahmood F, Matyal R. Artificial Intelligence for Anesthesiology Board-Style Examination Questions: Role of Large Language Models. J Cardiothorac Vasc Anesth 2024; 38:1251-1259. [PMID: 38423884 DOI: 10.1053/j.jvca.2024.01.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 01/24/2024] [Accepted: 01/29/2024] [Indexed: 03/02/2024]
Abstract
New artificial intelligence tools have been developed that have implications for medical usage. Large language models (LLMs), such as the widely used ChatGPT developed by OpenAI, have not been explored in the context of anesthesiology education. Understanding the reliability of various publicly available LLMs for medical specialties could offer insight into their understanding of the physiology, pharmacology, and practical applications of anesthesiology. An exploratory prospective review was conducted using 3 commercially available LLMs--OpenAI's ChatGPT GPT-3.5 version (GPT-3.5), OpenAI's ChatGPT GPT-4 (GPT-4), and Google's Bard--on questions from a widely used anesthesia board examination review book. Of the 884 eligible questions, the overall correct answer rates were 47.9% for GPT-3.5, 69.4% for GPT-4, and 45.2% for Bard. GPT-4 exhibited significantly higher performance than both GPT-3.5 and Bard (p = 0.001 and p < 0.001, respectively). None of the LLMs met the criteria required to secure American Board of Anesthesiology certification, according to the 70% passing score approximation. GPT-4 significantly outperformed GPT-3.5 and Bard in terms of overall performance, but lacked consistency in providing explanations that aligned with scientific and medical consensus. Although GPT-4 shows promise, current LLMs are not sufficiently advanced to answer anesthesiology board examination questions with passing success. Further iterations and domain-specific training may enhance their utility in medical education.
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Affiliation(s)
- Adnan A Khan
- Department of Anesthesia, Critical Care, and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School Boston, MA
| | - Rayaan Yunus
- Department of Anesthesia, Critical Care, and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School Boston, MA
| | - Mahad Sohail
- Department of Anesthesia, Critical Care, and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School Boston, MA
| | - Taha A Rehman
- Department of Anesthesia, Critical Care, and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School Boston, MA
| | - Shirin Saeed
- Department of Anesthesia, Critical Care, and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School Boston, MA
| | - Yifan Bu
- Department of Anesthesia, Critical Care, and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School Boston, MA
| | - Cullen D Jackson
- Department of Anesthesia, Critical Care, and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School Boston, MA
| | - Aidan Sharkey
- Department of Anesthesia, Critical Care, and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School Boston, MA
| | - Feroze Mahmood
- Department of Anesthesia, Critical Care, and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School Boston, MA
| | - Robina Matyal
- Department of Anesthesia, Critical Care, and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School Boston, MA.
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Zhang F, Zhan J, Wang Y, Cheng J, Wang M, Chen P, Ouyang J, Li J. Enhancing thalassemia gene carrier identification in non-anemic populations using artificial intelligence erythrocyte morphology analysis and machine learning. Eur J Haematol 2024; 112:692-700. [PMID: 38154920 DOI: 10.1111/ejh.14160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 12/06/2023] [Accepted: 12/07/2023] [Indexed: 12/30/2023]
Abstract
BACKGROUND Non-anemic thalassemia trait (TT) accounted for a high proportion of TT cases in South China. OBJECTIVE To use artificial intelligence (AI) analysis of erythrocyte morphology and machine learning (ML) to identify TT gene carriers in a non-anemic population. METHODS Digital morphological data from 76 TT gene carriers and 97 controls were collected. The AI technology-based Mindray MC-100i was used to quantitatively analyze the percentage of abnormal erythrocytes. Further, ML was used to construct a prediction model. RESULTS Non-anemic TT carriers accounted for over 60% of the TT cases. Random Forest was selected as the prediction model and named TT@Normal. The TT@Normal algorithm showed outstanding performance in the training, validation, and external validation sets and could efficiently identify TT carriers in the non-anemic population. The top three weights in the TT@Normal model were the target cells, microcytes, and teardrop cells. Elevated percentages of abnormal erythrocytes should raise a strong suspicion of being a TT gene carrier. TT@Normal could be promoted and used as a visualization and sharing tool. It is accessible through a URL link and can be used by medical staff online to predict the possibility of TT gene carriage in a non-anemic population. CONCLUSIONS The ML-based model TT@Normal could efficiently identify TT carriers in non-anemic people. Elevated percentages of target cells, microcytes, and teardrop cells should raise a strong suspicion of being a TT gene carrier.
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Affiliation(s)
- Fan Zhang
- Department of Laboratory Science, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Jieyu Zhan
- Department of Pediatric, Baiyun District Maternal and Child Healthcare Centre, Guangzhou, China
| | - Yang Wang
- Department of Laboratory Science, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Jing Cheng
- Department of Laboratory Science, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Meinan Wang
- IVD Domestic Clinical Application Department, Mindray Biomedical Electronics Co., Ltd, Shenzhen City, China
| | - Peisong Chen
- Department of Laboratory Science, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Juan Ouyang
- Department of Laboratory Science, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Junxun Li
- Department of Laboratory Science, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
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Fidon L, Aertsen M, Kofler F, Bink A, David AL, Deprest T, Emam D, Guffens F, Jakab A, Kasprian G, Kienast P, Melbourne A, Menze B, Mufti N, Pogledic I, Prayer D, Stuempflen M, Van Elslander E, Ourselin S, Deprest J, Vercauteren T. A Dempster-Shafer Approach to Trustworthy AI With Application to Fetal Brain MRI Segmentation. IEEE Trans Pattern Anal Mach Intell 2024; 46:3784-3795. [PMID: 38198270 DOI: 10.1109/tpami.2023.3346330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2024]
Abstract
Deep learning models for medical image segmentation can fail unexpectedly and spectacularly for pathological cases and images acquired at different centers than training images, with labeling errors that violate expert knowledge. Such errors undermine the trustworthiness of deep learning models for medical image segmentation. Mechanisms for detecting and correcting such failures are essential for safely translating this technology into clinics and are likely to be a requirement of future regulations on artificial intelligence (AI). In this work, we propose a trustworthy AI theoretical framework and a practical system that can augment any backbone AI system using a fallback method and a fail-safe mechanism based on Dempster-Shafer theory. Our approach relies on an actionable definition of trustworthy AI. Our method automatically discards the voxel-level labeling predicted by the backbone AI that violate expert knowledge and relies on a fallback for those voxels. We demonstrate the effectiveness of the proposed trustworthy AI approach on the largest reported annotated dataset of fetal MRI consisting of 540 manually annotated fetal brain 3D T2w MRIs from 13 centers. Our trustworthy AI method improves the robustness of four backbone AI models for fetal brain MRIs acquired across various centers and for fetuses with various brain abnormalities.
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45
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Lechien JR, Carroll TL, Huston MN, Naunheim MR. ChatGPT-4 accuracy for patient education in laryngopharyngeal reflux. Eur Arch Otorhinolaryngol 2024; 281:2547-2552. [PMID: 38492008 DOI: 10.1007/s00405-024-08560-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Accepted: 02/13/2024] [Indexed: 03/18/2024]
Abstract
INTRODUCTION Chatbot Generative Pre-trained Transformer (ChatGPT) is an artificial intelligence-powered language model chatbot able to help otolaryngologists in practice and research. The ability of ChatGPT in generating patient-centered information related to laryngopharyngeal reflux disease (LPRD) was evaluated. METHODS Twenty-five questions dedicated to definition, clinical presentation, diagnosis, and treatment of LPRD were developed from the Dubai definition and management of LPRD consensus and recent reviews. Questions about the four aforementioned categories were entered into ChatGPT-4. Four board-certified laryngologists evaluated the accuracy of ChatGPT-4 with a 5-point Likert scale. Interrater reliability was evaluated. RESULTS The mean scores (SD) of ChatGPT-4 answers for definition, clinical presentation, additional examination, and treatments were 4.13 (0.52), 4.50 (0.72), 3.75 (0.61), and 4.18 (0.47), respectively. Experts reported high interrater reliability for sub-scores (ICC = 0.973). The lowest performances of ChatGPT-4 were on answers about the most prevalent LPR signs, the most reliable objective tool for the diagnosis (hypopharyngeal-esophageal multichannel intraluminal impedance-pH monitoring (HEMII-pH)), and the criteria for the diagnosis of LPR using HEMII-pH. CONCLUSION ChatGPT-4 may provide adequate information on the definition of LPR, differences compared to GERD (gastroesophageal reflux disease), and clinical presentation. Information provided upon extra-laryngeal manifestations and HEMII-pH may need further optimization. Regarding the recent trends identifying increasing patient use of internet sources for self-education, the findings of the present study may help draw attention to ChatGPT-4's accuracy on the topic of LPR.
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Affiliation(s)
- Jerome R Lechien
- Research Committee, Young Otolaryngologists of the International Federation of Otorhinolaryngological Societies (IFOS), Paris, France.
- Division of Laryngology and Broncho-Esophagology, Department of Otolaryngology-Head Neck Surgery, EpiCURA Hospital, UMONS Research Institute for Health Sciences and Technology, University of Mons (UMons), Mons, Belgium.
- Department of Otorhinolaryngology and Head and Neck Surgery, Foch Hospital, School of Medicine, Phonetics and Phonology Laboratory (UMR 7018 CNRS, Université Sorbonne Nouvelle/Paris 3), Paris, France.
- Polyclinique Elsan de Poitiers, Poitiers, France.
| | - Thomas L Carroll
- Division of Otolaryngology-Head and Neck Surgery, Brigham and Women's Hospital, Department of Otolaryngology-Head and Neck Surgery, Harvard Medical School, Boston, MA, USA
| | - Molly N Huston
- Department of Otolaryngology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Matthew R Naunheim
- Research Committee, Young Otolaryngologists of the International Federation of Otorhinolaryngological Societies (IFOS), Paris, France
- Department of Otolaryngology-Head and Neck Surgery, Harvard Medical School, Boston, MA, USA
- Division of Laryngology, Massachusetts Eye and Ear, Boston, MA, USA
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Qu J, Xiao X, Wei X, Qian X. A causality-inspired generalized model for automated pancreatic cancer diagnosis. Med Image Anal 2024; 94:103154. [PMID: 38552527 DOI: 10.1016/j.media.2024.103154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Revised: 02/29/2024] [Accepted: 03/20/2024] [Indexed: 04/16/2024]
Abstract
Pancreatic cancer (PC) is a severely malignant cancer variant with high mortality. Since PC has no obvious symptoms, most PC patients are belatedly diagnosed at advanced disease stages. Recently, artificial intelligence (AI) approaches have demonstrated promising prospects for early diagnosis of pancreatic cancer. However, certain non-causal factors (such as intensity and texture appearance variations, also called confounders) tend to induce spurious correlation with PC diagnosis. This undermines the generalization performance and the clinical applicability of the AI-based PC diagnosis approaches. Therefore, we propose a causal intervention based automated method for pancreatic cancer diagnosis with contrast-enhanced computerized tomography (CT) images, where a confounding effects reduction scheme is developed for alleviating spurious correlations to achieve unbiased learning, thereby improving the generalization performance. Specifically, a continuous image generation strategy was developed to simulate wide variations of intensity differences caused by imaging heterogeneities, where Monte Carlo sampling is added to further enhance the continuity of simulated images. Then, to enhance the pancreatic texture variability, a texture diversification method was introduced in conjunction with gradient-based data augmentation. Finally, a causal intervention strategy was proposed to alleviate the adverse confounding effects by decoupling the causal and non-causal factors and combining them randomly. Extensive experiments showed remarkable diagnosis performance on a cross-validation dataset. Also, promising generalization performance with an average accuracy of 0.87 was attained on three independent test sets of a total of 782 subjects. Therefore, the proposed method shows high clinical feasibility and applicability for pancreatic cancer diagnosis.
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Affiliation(s)
- Jiaqi Qu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, PR China
| | - Xiang Xiao
- Department of Medical Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, PR China
| | - Xunbin Wei
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, PR China; Peking University Cancer Hospital & Institute, Beijing, 100142, PR China; Biomedical Engineering Department, Peking University, Beijing, 100081, PR China; Institute of Medical Technology, Peking University Health Science Center, Beijing, 100191, PR China; International Cancer Institute, Peking University, Beijing 100191, PR China.
| | - Xiaohua Qian
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, PR China.
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Fournier A, Fallet C, Sadeghipour F, Perrottet N. Assessing the applicability and appropriateness of ChatGPT in answering clinical pharmacy questions. Ann Pharm Fr 2024; 82:507-513. [PMID: 37992892 DOI: 10.1016/j.pharma.2023.11.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 11/16/2023] [Accepted: 11/16/2023] [Indexed: 11/24/2023]
Abstract
OBJECTIVES Clinical pharmacists rely on different scientific references to ensure appropriate, safe, and cost-effective drug use. Tools based on artificial intelligence (AI) such as ChatGPT (Generative Pre-trained Transformer) could offer valuable support. The objective of this study was to assess ChatGPT's capacity to correctly respond to clinical pharmacy questions asked by healthcare professionals in our university hospital. MATERIAL AND METHODS ChatGPT's capacity to respond correctly to the last 100 consecutive questions recorded in our clinical pharmacy database was assessed. Questions were copied from our FileMaker Pro database and pasted into ChatGPT March 14 version online platform. The generated answers were then copied verbatim into an Excel file. Two blinded clinical pharmacists reviewed all the questions and the answers given by the software. In case of disagreements, a third blinded pharmacist intervened to decide. RESULTS Documentation-related issues (n=36) and drug administration mode (n=30) were preponderantly recorded. Among 69 applicable questions, the rate of correct answers varied from 30 to 57.1% depending on questions type with a global rate of 44.9%. Regarding inappropriate answers (n=38), 20 were incorrect, 18 gave no answers and 8 were incomplete with 8 answers belonging to 2 different categories. No better answers than the pharmacists were observed. CONCLUSIONS ChatGPT demonstrated a mitigated performance in answering clinical pharmacy questions. It should not replace human expertise as a high rate of inappropriate answers was highlighted. Future studies should focus on the optimization of ChatGPT for specific clinical pharmacy questions and explore the potential benefits and limitations of integrating this technology into clinical practice.
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Affiliation(s)
- A Fournier
- Service of Pharmacy, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - C Fallet
- Service of Pharmacy, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - F Sadeghipour
- Service of Pharmacy, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland; School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, Geneva, Switzerland; Center for Research and Innovation in Clinical Pharmaceutical Sciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - N Perrottet
- Service of Pharmacy, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland; School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, Geneva, Switzerland.
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Elahmedi M, Sawhney R, Guadagno E, Botelho F, Poenaru D. The State of Artificial Intelligence in Pediatric Surgery: A Systematic Review. J Pediatr Surg 2024; 59:774-782. [PMID: 38418276 DOI: 10.1016/j.jpedsurg.2024.01.044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/14/2024] [Accepted: 01/22/2024] [Indexed: 03/01/2024]
Abstract
BACKGROUND Artificial intelligence (AI) has been recently shown to improve clinical workflows and outcomes - yet its potential in pediatric surgery remains largely unexplored. This systematic review details the use of AI in pediatric surgery. METHODS Nine medical databases were searched from inception until January 2023, identifying articles focused on AI in pediatric surgery. Two authors reviewed full texts of eligible articles. Studies were included if they were original investigations on the development, validation, or clinical application of AI models for pediatric health conditions primarily managed surgically. Studies were excluded if they were not peer-reviewed, were review articles, editorials, commentaries, or case reports, did not focus on pediatric surgical conditions, or did not employ at least one AI model. Extracted data included study characteristics, clinical specialty, AI method and algorithm type, AI model (algorithm) role and performance metrics, key results, interpretability, validation, and risk of bias using PROBAST and QUADAS-2. RESULTS Authors screened 8178 articles and included 112. Half of the studies (50%) reported predictive models (for adverse events [25%], surgical outcomes [16%] and survival [9%]), followed by diagnostic (29%) and decision support models (21%). Neural networks (44%) and ensemble learners (36%) were the most commonly used AI methods across application domains. The main pediatric surgical subspecialties represented across all models were general surgery (31%) and neurosurgery (25%). Forty-four percent of models were interpretable, and 6% were both interpretable and externally validated. Forty percent of models had a high risk of bias, and concerns over applicability were identified in 7%. CONCLUSIONS While AI has wide potential clinical applications in pediatric surgery, very few published AI algorithms were externally validated, interpretable, and unbiased. Future research needs to focus on developing AI models which are prospectively validated and ultimately integrated into clinical workflows. LEVEL OF EVIDENCE 2A.
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Affiliation(s)
- Mohamed Elahmedi
- Harvey E. Beardmore Division of Pediatric Surgery, The Montreal Children's Hospital, McGill University Health Centre, Montreal, Quebec, Canada
| | - Riya Sawhney
- Harvey E. Beardmore Division of Pediatric Surgery, The Montreal Children's Hospital, McGill University Health Centre, Montreal, Quebec, Canada
| | - Elena Guadagno
- Harvey E. Beardmore Division of Pediatric Surgery, The Montreal Children's Hospital, McGill University Health Centre, Montreal, Quebec, Canada
| | - Fabio Botelho
- Harvey E. Beardmore Division of Pediatric Surgery, The Montreal Children's Hospital, McGill University Health Centre, Montreal, Quebec, Canada
| | - Dan Poenaru
- Harvey E. Beardmore Division of Pediatric Surgery, The Montreal Children's Hospital, McGill University Health Centre, Montreal, Quebec, Canada.
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Zhou K, Gattinger G. The Evolving Regulatory Paradigm of AI in MedTech: A Review of Perspectives and Where We Are Today. Ther Innov Regul Sci 2024; 58:456-464. [PMID: 38528278 DOI: 10.1007/s43441-024-00628-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 02/04/2024] [Indexed: 03/27/2024]
Abstract
Artificial intelligence (AI)-enabled technologies in the MedTech sector hold the promise to transform healthcare delivery by improving access, quality, and outcomes. As the regulatory contours of these technologies are being defined, there is a notable lack of literature on the key stakeholders such as the organizations and interest groups that have a significant input in shaping the regulatory framework. This article explores the perspectives and contributions of these stakeholders in shaping the regulatory paradigm of AI-enabled medical technologies. The formation of an AI regulatory framework requires the convergence of ethical, regulatory, technical, societal, and practical considerations. These multiple perspectives contribute to the various dimensions of an evolving regulatory paradigm. From the global governance guidelines set by the World Health Organization (WHO) to national regulations, the article sheds light not just on these multiple perspectives but also on their interconnectedness in shaping the regulatory landscape of AI.
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Affiliation(s)
- Karen Zhou
- Northeastern University, Toronto, ON, Canada.
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50
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Papastratis I, Stergioulas A, Konstantinidis D, Daras P, Dimitropoulos K. Can ChatGPT provide appropriate meal plans for NCD patients? Nutrition 2024; 121:112291. [PMID: 38359704 DOI: 10.1016/j.nut.2023.112291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 10/30/2023] [Indexed: 02/17/2024]
Abstract
OBJECTIVES Dietary habits significantly affect health conditions and are closely related to the onset and progression of non-communicable diseases (NCDs). Consequently, a well-balanced diet plays an important role in lessening the effects of various disorders, including NCDs. Several artificial intelligence recommendation systems have been developed to propose healthy and nutritious diets. Most of these systems use expert knowledge and guidelines to provide tailored diets and encourage healthier eating habits. However, new advances in large language models such as ChatGPT, with their ability to produce human-like responses, have led individuals to search for advice in several tasks, including diet recommendations. This study aimed to determine the ability of ChatGPT models to generate appropriate personalized meal plans for patients with obesity, cardiovascular diseases, and type 2 diabetes. METHODS Using a state-of-the-art knowledge-based recommendation system as a reference, we assessed the meal plans generated by two large language models in terms of energy intake, nutrient accuracy, and meal variability. RESULTS Experimental results with different user profiles revealed the potential of ChatGPT models to provide personalized nutritional advice. CONCLUSION Additional supervision and guidance by nutrition experts or knowledge-based systems are required to ensure meal appropriateness for users with NCDs.
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Affiliation(s)
- Ilias Papastratis
- The Visual Computing Lab, Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Central Macedonia, Greece.
| | - Andreas Stergioulas
- The Visual Computing Lab, Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Central Macedonia, Greece
| | - Dimitrios Konstantinidis
- The Visual Computing Lab, Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Central Macedonia, Greece
| | - Petros Daras
- The Visual Computing Lab, Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Central Macedonia, Greece
| | - Kosmas Dimitropoulos
- The Visual Computing Lab, Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Central Macedonia, Greece
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