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Lee SB. Development of a chest X-ray machine learning convolutional neural network model on a budget and using artificial intelligence explainability techniques to analyze patterns of machine learning inference. JAMIA Open 2024; 7:ooae035. [PMID: 38699648 PMCID: PMC11064095 DOI: 10.1093/jamiaopen/ooae035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 04/03/2024] [Accepted: 04/10/2024] [Indexed: 05/05/2024] Open
Abstract
Objective Machine learning (ML) will have a large impact on medicine and accessibility is important. This study's model was used to explore various concepts including how varying features of a model impacted behavior. Materials and Methods This study built an ML model that classified chest X-rays as normal or abnormal by using ResNet50 as a base with transfer learning. A contrast enhancement mechanism was implemented to improve performance. After training with a dataset of publicly available chest radiographs, performance metrics were determined with a test set. The ResNet50 base was substituted with deeper architectures (ResNet101/152) and visualization methods used to help determine patterns of inference. Results Performance metrics were an accuracy of 79%, recall 69%, precision 96%, and area under the curve of 0.9023. Accuracy improved to 82% and recall to 74% with contrast enhancement. When visualization methods were applied and the ratio of pixels used for inference measured, deeper architectures resulted in the model using larger portions of the image for inference as compared to ResNet50. Discussion The model performed on par with many existing models despite consumer-grade hardware and smaller datasets. Individual models vary thus a single model's explainability may not be generalizable. Therefore, this study varied architecture and studied patterns of inference. With deeper ResNet architectures, the machine used larger portions of the image to make decisions. Conclusion An example using a custom model showed that AI (Artificial Intelligence) can be accessible on consumer-grade hardware, and it also demonstrated an example of studying themes of ML explainability by varying ResNet architectures.
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Affiliation(s)
- Stephen B Lee
- Division of Infectious Diseases, Department of Medicine, College of Medicine, University of Saskatchewan, Regina, S4P 0W5, Canada
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Zhou J, Cui R, Lin L. A Systematic Review of the Application of Computational Technology in Microtia. J Craniofac Surg 2024; 35:1214-1218. [PMID: 38710037 DOI: 10.1097/scs.0000000000010210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Accepted: 03/11/2024] [Indexed: 05/08/2024] Open
Abstract
Microtia is a congenital and morphological anomaly of one or both ears, which results from a confluence of genetic and external environmental factors. Up to now, extensive research has explored the potential utilization of computational methodologies in microtia and has obtained promising results. Thus, the authors reviewed the achievements and shortcomings of the research mentioned previously, from the aspects of artificial intelligence, computer-aided design and surgery, computed tomography, medical and biological data mining, and reality-related technology, including virtual reality and augmented reality. Hoping to offer novel concepts and inspire further studies within this field.
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Affiliation(s)
- Jingyang Zhou
- Ear Reconstruction Center, Plastic Surgery Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
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Lim B, Cevik J, Seth I, Sofiadellis F, Ross RJ, Rozen WM, Cuomo R. Evaluating Artificial Intelligence's Role in Teaching the Reporting and Interpretation of Computed Tomographic Angiography for Preoperative Planning of the Deep Inferior Epigastric Artery Perforator Flap. JPRAS Open 2024; 40:273-285. [PMID: 38708385 PMCID: PMC11067004 DOI: 10.1016/j.jpra.2024.03.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Accepted: 03/30/2024] [Indexed: 05/07/2024] Open
Abstract
Background Artificial intelligence (AI) has the potential to transform preoperative planning for breast reconstruction by enhancing the efficiency, accuracy, and reliability of radiology reporting through automatic interpretation and perforator identification. Large language models (LLMs) have recently advanced significantly in medicine. This study aimed to evaluate the proficiency of contemporary LLMs in interpreting computed tomography angiography (CTA) scans for deep inferior epigastric perforator (DIEP) flap preoperative planning. Methods Four prominent LLMs, ChatGPT-4, BARD, Perplexity, and BingAI, answered six questions on CTA scan reporting. A panel of expert plastic surgeons with extensive experience in breast reconstruction assessed the responses using a Likert scale. In contrast, the responses' readability was evaluated using the Flesch Reading Ease score, the Flesch-Kincaid Grade level, and the Coleman-Liau Index. The DISCERN score was utilized to determine the responses' suitability. Statistical significance was identified through a t-test, and P-values < 0.05 were considered significant. Results BingAI provided the most accurate and useful responses to prompts, followed by Perplexity, ChatGPT, and then BARD. BingAI had the greatest Flesh Reading Ease (34.7±5.5) and DISCERN (60.5±3.9) scores. Perplexity had higher Flesch-Kincaid Grade level (20.5±2.7) and Coleman-Liau Index (17.8±1.6) scores than other LLMs. Conclusion LLMs exhibit limitations in their capabilities of reporting CTA for preoperative planning of breast reconstruction, yet the rapid advancements in technology hint at a promising future. AI stands poised to enhance the education of CTA reporting and aid preoperative planning. In the future, AI technology could provide automatic CTA interpretation, enhancing the efficiency, accuracy, and reliability of CTA reports.
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Affiliation(s)
- Bryan Lim
- Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, 3199, Australia
- Faculty of Medicine, Monash University, Melbourne, Victoria, 3004, Australia
| | - Jevan Cevik
- Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, 3199, Australia
- Faculty of Medicine, Monash University, Melbourne, Victoria, 3004, Australia
| | - Ishith Seth
- Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, 3199, Australia
- Faculty of Medicine, Monash University, Melbourne, Victoria, 3004, Australia
| | - Foti Sofiadellis
- Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, 3199, Australia
| | - Richard J. Ross
- Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, 3199, Australia
| | - Warren M. Rozen
- Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, 3199, Australia
- Faculty of Medicine, Monash University, Melbourne, Victoria, 3004, Australia
| | - Roberto Cuomo
- Plastic Surgery Unit, Department of Medicine, Surgery and Neuroscience, University of Siena, 53100, Italy
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Reese A, Evancho P, Richards R, Arbel E, O'Shea A. To the Editor: An Urgent Call to Action to Integrate Artificial Intelligence Curriculum Into Medical Education. J Grad Med Educ 2024; 16:373. [PMID: 38882430 PMCID: PMC11173035 DOI: 10.4300/jgme-d-24-00282.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/18/2024] Open
Affiliation(s)
- Alyssa Reese
- is a Medical Student, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, USA
| | - Peter Evancho
- is a Medical Student and Licensed Attorney, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Raymond Richards
- is a Medical Student, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, USA
| | - Eylon Arbel
- is a Medical Student, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, USA; and
| | - Aidan O'Shea
- is a Medical Student, University of Rochester School of Medicine & Dentistry, Rochester, New York, USA
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Arango-Ibanez JP, Posso-Nuñez JA, Díaz-Solórzano JP, Cruz-Suárez G. Evidence-Based Learning Strategies in Medicine Using AI. JMIR MEDICAL EDUCATION 2024; 10:e54507. [PMID: 38801706 PMCID: PMC11144835 DOI: 10.2196/54507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Revised: 01/20/2024] [Accepted: 03/23/2024] [Indexed: 05/29/2024]
Abstract
Unlabelled Large language models (LLMs), like ChatGPT, are transforming the landscape of medical education. They offer a vast range of applications, such as tutoring (personalized learning), patient simulation, generation of examination questions, and streamlined access to information. The rapid advancement of medical knowledge and the need for personalized learning underscore the relevance and timeliness of exploring innovative strategies for integrating artificial intelligence (AI) into medical education. In this paper, we propose coupling evidence-based learning strategies, such as active recall and memory cues, with AI to optimize learning. These strategies include the generation of tests, mnemonics, and visual cues.
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Affiliation(s)
| | | | | | - Gustavo Cruz-Suárez
- Departamento de Anestesiología, Fundación Valle del Lili, Cali, Colombia
- Unidad de Inteligencia Artificial, Fundación Valle del Lili, Cali, Colombia
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Jebreen K, Radwan E, Kammoun-Rebai W, Alattar E, Radwan A, Safi W, Radwan W, Alajez M. Perceptions of undergraduate medical students on artificial intelligence in medicine: mixed-methods survey study from Palestine. BMC MEDICAL EDUCATION 2024; 24:507. [PMID: 38714993 PMCID: PMC11077786 DOI: 10.1186/s12909-024-05465-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 04/24/2024] [Indexed: 05/12/2024]
Abstract
BACKGROUND The current applications of artificial intelligence (AI) in medicine continue to attract the attention of medical students. This study aimed to identify undergraduate medical students' attitudes toward AI in medicine, explore present AI-related training opportunities, investigate the need for AI inclusion in medical curricula, and determine preferred methods for teaching AI curricula. METHODS This study uses a mixed-method cross-sectional design, including a quantitative study and a qualitative study, targeting Palestinian undergraduate medical students in the academic year 2022-2023. In the quantitative part, we recruited a convenience sample of undergraduate medical students from universities in Palestine from June 15, 2022, to May 30, 2023. We collected data by using an online, well-structured, and self-administered questionnaire with 49 items. In the qualitative part, 15 undergraduate medical students were interviewed by trained researchers. Descriptive statistics and an inductive content analysis approach were used to analyze quantitative and qualitative data, respectively. RESULTS From a total of 371 invitations sent, 362 responses were received (response rate = 97.5%), and 349 were included in the analysis. The mean age of participants was 20.38 ± 1.97, with 40.11% (140) in their second year of medical school. Most participants (268, 76.79%) did not receive formal education on AI before or during medical study. About two-thirds of students strongly agreed or agreed that AI would become common in the future (67.9%, 237) and would revolutionize medical fields (68.7%, 240). Participants stated that they had not previously acquired training in the use of AI in medicine during formal medical education (260, 74.5%), confirming a dire need to include AI training in medical curricula (247, 70.8%). Most participants (264, 75.7%) think that learning opportunities for AI in medicine have not been adequate; therefore, it is very important to study more about employing AI in medicine (228, 65.3%). Male students (3.15 ± 0.87) had higher perception scores than female students (2.81 ± 0.86) (p < 0.001). The main themes that resulted from the qualitative analysis of the interview questions were an absence of AI learning opportunities, the necessity of including AI in medical curricula, optimism towards the future of AI in medicine, and expected challenges related to AI in medical fields. CONCLUSION Medical students lack access to educational opportunities for AI in medicine; therefore, AI should be included in formal medical curricula in Palestine.
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Affiliation(s)
- Kamel Jebreen
- Department of Mathematics, Palestine Technical University - Kadoorie, Hebron, Palestine
- Department of Mathematics, An-Najah National University, Nablus, Palestine
- Unité de Recherche Clinique Saint-Louis Fernand-Widal Lariboisière, APHP, Paris, France
| | - Eqbal Radwan
- Department of Biology, Faculty of Science, Islamic University of Gaza, Gaza, Palestine.
| | | | - Etimad Alattar
- Department of Biology, Faculty of Science, Islamic University of Gaza, Gaza, Palestine
| | - Afnan Radwan
- Faculty of Education, Islamic University of Gaza, Gaza, Palestine
| | - Walaa Safi
- Department of Biotechnology, Faculty of Science, Islamic University of Gaza, Gaza, Palestine
| | - Walaa Radwan
- University College of Applied Sciences - Gaza, Gaza, Palestine
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Umer M, Naveed A, Maryam Q, Malik AR, Bashir N, Kandel K. Investigating awareness of artificial intelligence in healthcare among medical students and professionals in Pakistan: a cross-sectional study. Ann Med Surg (Lond) 2024; 86:2606-2611. [PMID: 38694316 PMCID: PMC11060211 DOI: 10.1097/ms9.0000000000001957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 03/04/2024] [Indexed: 05/04/2024] Open
Abstract
Objective The purpose of this study is to find out the level of awareness and acceptance of artificial intelligence (AI) in Pakistan's medical community so as to comment on its future in our healthcare system. Methods A survey consisting of 15 close-ended questions was conducted. The questions inquired about awareness about AI and discovered the opinions of healthcare professionals regarding its benefits and expected problems. The data were analyzed using SPSS version 26, and descriptive statistics for percentage and frequency were computed. χ2 test was used to analyze the subgroups (Significant p value <0.05). Results A total of 351 participants were included in this study. General familiarity with AI was low. Only 75 (21.3%) participants answered that they had good familiarity with AI, and only 56 (16%) of them had good familiarity with the role of AI in medicine. One hundred sixty-eight (47.9%) participants disagreed that AI would out-compete the physician in the important traits of professionalism. Only 71 (20.2%) participants believed AI to be diagnostically superior to the physician. Two hundred fourteen (61.0%) were worried about completely trusting AI in its decisions, and 204(58.1%) believed that AI systems lacking human traits would not be able to mirror the doctor-patient relationship. Two hundred sixty-one (74.4%) participants believed that AI would be useful in Administrative tasks. A majority, 162 (46.2%), do not believe that AI would replace them. Finally, a huge majority of participants [225 (64.1%)] demanded the integration of AI in Pakistan's healthcare system. Conclusion This study suggests that a majority of healthcare professionals in Pakistan do not believe that they are sufficiently aware of the role of AI in healthcare. This was corroborated by their answers to various questions regarding the capabilities of AI. This study indicates the need for a more comprehensive ascertainment of healthcare professionals' perceptions regarding the role of Artificial Intelligence in medicine and bridging the gap between doctors and technology to further promote a patient-centred approach to medicine.
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Lee YM, Kim S, Lee YH, Kim HS, Seo SW, Kim H, Kim KJ. Defining Medical AI Competencies for Medical School Graduates: Outcomes of a Delphi Survey and Medical Student/Educator Questionnaire of South Korean Medical Schools. ACADEMIC MEDICINE : JOURNAL OF THE ASSOCIATION OF AMERICAN MEDICAL COLLEGES 2024; 99:524-533. [PMID: 38207056 DOI: 10.1097/acm.0000000000005618] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/13/2024]
Abstract
PURPOSE Given the increasing significance and potential impact of artificial intelligence (AI) technology on health care delivery, there is an increasing demand to integrate AI into medical school curricula. This study aimed to define medical AI competencies and identify the essential competencies for medical graduates in South Korea. METHOD An initial Delphi survey conducted in 2022 involving 4 groups of medical AI experts (n = 28) yielded 42 competency items. Subsequently, an online questionnaire survey was carried out with 1,955 participants (1,174 students and 781 professors) from medical schools across South Korea, utilizing the list of 42 competencies developed from the first Delphi round. A subsequent Delphi survey was conducted with 33 medical educators from 21 medical schools to differentiate the essential AI competencies from the optional ones. RESULTS The study identified 6 domains encompassing 36 AI competencies essential for medical graduates: (1) understanding digital health and changes driven by AI; (2) fundamental knowledge and skills in medical AI; (3) ethics and legal aspects in the use of medical AI; (4) medical AI application in clinical practice; (5) processing, analyzing, and evaluating medical data; and (6) research and development of medical AI, as well as subcompetencies within each domain. While numerous competencies within the first 4 domains were deemed essential, a higher percentage of experts indicated responses in the last 2 domains, data science and medical AI research and development, were optional. CONCLUSIONS This medical AI framework of 6 competencies and their subcompetencies for medical graduates exhibits promising potential for guiding the integration of AI into medical curricula. Further studies conducted in diverse contexts and countries are necessary to validate and confirm the applicability of these findings. Additional research is imperative for developing specific and feasible educational models to integrate these proposed competencies into pre-existing curricula.
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Culp ML, Mahmoud S, Liu D, Haworth IS. An Artificial Intelligence-Supported Medicinal Chemistry Project: An Example for Incorporating Artificial Intelligence Within the Pharmacy Curriculum. AMERICAN JOURNAL OF PHARMACEUTICAL EDUCATION 2024; 88:100696. [PMID: 38574998 DOI: 10.1016/j.ajpe.2024.100696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 03/12/2024] [Accepted: 03/29/2024] [Indexed: 04/06/2024]
Abstract
OBJECTIVE This study aims to integrate and use AI to teach core concepts in a medicinal chemistry course and to increase the familiarity of pharmacy students with AI in pharmacy practice and drug development. Artificial intelligence (AI) is a multidisciplinary science that aims to build software tools that mimic human intelligence. AI is revolutionizing pharmaceutical research and patient care. Hence, it is important to include AI in pharmacy education to prepare a competent workforce of pharmacists with skills in this area. METHODS AI principles were introduced in a required medicinal chemistry course for first-year pharmacy students. An AI software, KNIME, was used to examine structure-activity relationships for 5 drugs. Students completed a data sheet that required comprehension of molecular structures and drug-protein interactions. These data were then used to make predictions for molecules with novel substituents using AI. The familiarity of students with AI was surveyed before and after this activity. RESULTS There was an increase in the number of students indicating familiarity with use of AI in pharmacy (before vs after: 25.3% vs 74.5%). The introduction of AI stimulated interest in the course content (> 60% of students indicated increased interest in medicinal chemistry) without compromising the learning outcomes. Almost 70% of students agreed that more AI should be taught in the PharmD curriculum. CONCLUSION This is a successful and transferable example of integrating AI in pharmacy education without changing the main learning objectives of a course. This approach is likely to stimulate student interest in AI applications in pharmacy.
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Affiliation(s)
- Megan L Culp
- University of Southern California, Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, Department of Pharmacology & Pharmaceutical Sciences, Los Angeles, CA, USA
| | - Sara Mahmoud
- University of the Pacific Thomas J. Long School of Pharmacy, Department of Pharmacy Practice, Stockton, CA, USA.
| | - Daniel Liu
- University of Southern California, Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, Department of Pharmacology & Pharmaceutical Sciences, Los Angeles, CA, USA
| | - Ian S Haworth
- University of Southern California, Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, Department of Pharmacology & Pharmaceutical Sciences, Los Angeles, CA, USA
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Hamilton A. Artificial Intelligence and Healthcare Simulation: The Shifting Landscape of Medical Education. Cureus 2024; 16:e59747. [PMID: 38840993 PMCID: PMC11152357 DOI: 10.7759/cureus.59747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/20/2024] [Indexed: 06/07/2024] Open
Abstract
The impact of artificial intelligence (AI) will be felt not only in the arena of patient care and deliverable therapies but will also be uniquely disruptive in medical education and healthcare simulation (HCS), in particular. As HCS is intertwined with computer technology, it offers opportunities for rapid scalability with AI and, therefore, will be the most practical place to test new AI applications. This will ensure the acquisition of AI literacy for graduates from the country's various healthcare professional schools. Artificial intelligence has proven to be a useful adjunct in developing interprofessional education and team and leadership skills assessments. Outcome-driven medical simulation has been extensively used to train students in image-centric disciplines such as radiology, ultrasound, echocardiography, and pathology. Allowing students and trainees in healthcare to first apply diagnostic decision support systems (DDSS) under simulated conditions leads to improved diagnostic accuracy, enhanced communication with patients, safer triage decisions, and improved outcomes from rapid response teams. However, the issue of bias, hallucinations, and the uncertainty of emergent properties may undermine the faith of healthcare professionals as they see AI systems deployed in the clinical setting and participating in diagnostic judgments. Also, the demands of ensuring AI literacy in our healthcare professional curricula will place burdens on simulation assets and faculty to adapt to a rapidly changing technological landscape. Nevertheless, the introduction of AI will place increased emphasis on virtual reality platforms, thereby improving the availability of self-directed learning and making it available 24/7, along with uniquely personalized evaluations and customized coaching. Yet, caution must be exercised concerning AI, especially as society's earlier, delayed, and muted responses to the inherent dangers of social media raise serious questions about whether the American government and its citizenry can anticipate the security and privacy guardrails that need to be in place to protect our healthcare practitioners, medical students, and patients.
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Affiliation(s)
- Allan Hamilton
- Artificial Intelligence Division, Arizona Simulation Technology and Education Center (ASTEC) University of Arizona, Tucson, USA
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Maccaro A, Stokes K, Statham L, He L, Williams A, Pecchia L, Piaggio D. Clearing the Fog: A Scoping Literature Review on the Ethical Issues Surrounding Artificial Intelligence-Based Medical Devices. J Pers Med 2024; 14:443. [PMID: 38793025 PMCID: PMC11121798 DOI: 10.3390/jpm14050443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 04/12/2024] [Accepted: 04/16/2024] [Indexed: 05/26/2024] Open
Abstract
The use of AI in healthcare has sparked much debate among philosophers, ethicists, regulators and policymakers who raised concerns about the implications of such technologies. The presented scoping review captures the progression of the ethical and legal debate and the proposed ethical frameworks available concerning the use of AI-based medical technologies, capturing key themes across a wide range of medical contexts. The ethical dimensions are synthesised in order to produce a coherent ethical framework for AI-based medical technologies, highlighting how transparency, accountability, confidentiality, autonomy, trust and fairness are the top six recurrent ethical issues. The literature also highlighted how it is essential to increase ethical awareness through interdisciplinary research, such that researchers, AI developers and regulators have the necessary education/competence or networks and tools to ensure proper consideration of ethical matters in the conception and design of new AI technologies and their norms. Interdisciplinarity throughout research, regulation and implementation will help ensure AI-based medical devices are ethical, clinically effective and safe. Achieving these goals will facilitate successful translation of AI into healthcare systems, which currently is lagging behind other sectors, to ensure timely achievement of health benefits to patients and the public.
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Affiliation(s)
- Alessia Maccaro
- Applied Biomedical Signal Processing Intelligent eHealth Lab, School of Engineering, University of Warwick, Coventry CV4 7AL, UK; (A.M.); (K.S.); (L.S.); (L.H.); (A.W.); (L.P.)
| | - Katy Stokes
- Applied Biomedical Signal Processing Intelligent eHealth Lab, School of Engineering, University of Warwick, Coventry CV4 7AL, UK; (A.M.); (K.S.); (L.S.); (L.H.); (A.W.); (L.P.)
| | - Laura Statham
- Applied Biomedical Signal Processing Intelligent eHealth Lab, School of Engineering, University of Warwick, Coventry CV4 7AL, UK; (A.M.); (K.S.); (L.S.); (L.H.); (A.W.); (L.P.)
- Warwick Medical School, University of Warwick, Coventry CV4 7AL, UK
| | - Lucas He
- Applied Biomedical Signal Processing Intelligent eHealth Lab, School of Engineering, University of Warwick, Coventry CV4 7AL, UK; (A.M.); (K.S.); (L.S.); (L.H.); (A.W.); (L.P.)
- Faculty of Engineering, Imperial College, London SW7 1AY, UK
| | - Arthur Williams
- Applied Biomedical Signal Processing Intelligent eHealth Lab, School of Engineering, University of Warwick, Coventry CV4 7AL, UK; (A.M.); (K.S.); (L.S.); (L.H.); (A.W.); (L.P.)
| | - Leandro Pecchia
- Applied Biomedical Signal Processing Intelligent eHealth Lab, School of Engineering, University of Warwick, Coventry CV4 7AL, UK; (A.M.); (K.S.); (L.S.); (L.H.); (A.W.); (L.P.)
- Intelligent Technologies for Health and Well-Being: Sustainable Design, Management and Evaluation, Faculty of Engineering, Università Campus Bio-Medico Roma, Via Alvaro del Portillo, 21, 00128 Rome, Italy
| | - Davide Piaggio
- Applied Biomedical Signal Processing Intelligent eHealth Lab, School of Engineering, University of Warwick, Coventry CV4 7AL, UK; (A.M.); (K.S.); (L.S.); (L.H.); (A.W.); (L.P.)
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Kawahara T, Sumi Y. GPT-4/4V's performance on the Japanese National Medical Licensing Examination. MEDICAL TEACHER 2024:1-8. [PMID: 38648547 DOI: 10.1080/0142159x.2024.2342545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 04/09/2024] [Indexed: 04/25/2024]
Abstract
BACKGROUND Recent advances in Artificial Intelligence (AI) are changing the medical world, and AI will likely replace many of the actions performed by medical professionals. The overall clinical ability of the AI has been evaluated by its ability to answer a text-based national medical examination. This study uniquely assesses the performance of Open AI's ChatGPT against all Japanese National Medical Licensing Examination (NMLE), including images, illustrations, and pictures. METHODS We obtained the questions of the past six years of the NMLE (112th to 117th) from the Japanese Ministry of Health, Labour and Welfare website. We converted them to JavaScript Object Notation (JSON) format. We created an application programming interface (API) to output correct answers using GPT-4 for questions without images and GPT4-V(ision) or GPT4 console for questions with images. RESULTS The percentage of image questions was 723/2400 (30.1%) over the past six years. In all years, GPT-4/4V exceeded the minimum score the examinee should score. In total, over the six years, the percentage of correct answers for basic medical knowledge questions was 665/905 (73.5%); for clinical knowledge questions, 1143/1531 (74.7%); and for image questions 497/723 (68.7%), respectively. CONCLUSIONS Regarding medical knowledge, GPT-4/4V met the minimum criteria regardless of whether the questions included images, illustrations, and pictures. Our study sheds light on the potential utility of AI in medical education.
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Affiliation(s)
- Tomoki Kawahara
- Department of Clinical Information Applied Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| | - Yuki Sumi
- Department of Clinical Information Applied Sciences, Tokyo Medical and Dental University, Tokyo, Japan
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Tozsin A, Ucmak H, Soyturk S, Aydin A, Gozen AS, Fahim MA, Güven S, Ahmed K. The Role of Artificial Intelligence in Medical Education: A Systematic Review. Surg Innov 2024:15533506241248239. [PMID: 38632898 DOI: 10.1177/15533506241248239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/19/2024]
Abstract
BACKGROUND To examine the artificial intelligence (AI) tools currently being studied in modern medical education, and critically evaluate the level of validation and the quality of evidence presented in each individual study. METHODS This review (PROSPERO ID: CRD42023410752) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement. A database search was conducted using PubMed, Embase, and Cochrane Library. Articles written in the English language between 2000 and March 2023 were reviewed retrospectively using the MeSH Terms "AI" and "medical education" A total of 4642 potentially relevant studies were found. RESULTS After a thorough screening process, 36 studies were included in the final analysis. These studies consisted of 26 quantitative studies and 10 studies investigated the development and validation of AI tools. When examining the results of studies in which Support vector machines (SVMs) were employed, it has demonstrated high accuracy in assessing students' experiences, diagnosing acute abdominal pain, classifying skilled and novice participants, and evaluating surgical training levels. Particularly in the comparison of surgical skill levels, it has achieved an accuracy rate of over 92%. CONCLUSION AI tools demonstrated effectiveness in improving practical skills, diagnosing diseases, and evaluating student performance. However, further research with rigorous validation is required to identify the most effective AI tools for medical education.
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Affiliation(s)
- Atinc Tozsin
- Department of Urology, Trakya University School of Medicine, Edirne, Turkey
| | - Harun Ucmak
- Department of Urology, Meram School of Medicine, Necmettin Erbakan University, Konya, Turkey
| | - Selim Soyturk
- Department of Urology, Meram School of Medicine, Necmettin Erbakan University, Konya, Turkey
| | - Abdullatif Aydin
- MRC Centre for Transplantation, Guy's Hospital, King's College London, London, UK
- Department of Urology, King's College Hospital NHS Foundation Trust, London, UK
| | | | - Maha Al Fahim
- Medical Education Department, Sheikh Khalifa Medical City, Abu Dhabi, UAE
| | - Selcuk Güven
- Department of Urology, Meram School of Medicine, Necmettin Erbakan University, Konya, Turkey
| | - Kamran Ahmed
- MRC Centre for Transplantation, Guy's Hospital, King's College London, London, UK
- Khalifa University, Abu Dhabi, UAE
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14
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Dsouza JM. A Student's Viewpoint on ChatGPT Use and Automation Bias in Medical Education. JMIR MEDICAL EDUCATION 2024; 10:e57696. [PMID: 38623729 PMCID: PMC11034419 DOI: 10.2196/57696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2024] [Revised: 03/03/2024] [Accepted: 03/28/2024] [Indexed: 04/17/2024]
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15
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Salih SM. Perceptions of Faculty and Students About Use of Artificial Intelligence in Medical Education: A Qualitative Study. Cureus 2024; 16:e57605. [PMID: 38707183 PMCID: PMC11069392 DOI: 10.7759/cureus.57605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/02/2024] [Indexed: 05/07/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI) implies using a computer to model intelligent behavior with minimal human intervention. With the advances of AI use in healthcare comes the need to reform medical education to produce doctors competent in AI use. Therefore, this qualitative study was conducted to explore faculty and students' perspectives on AI, their use of AI applications, and their perspective on its value and impact on medical education at a Saudi faculty of medicine. METHODS This qualitative study was conducted at the Faculty of Medicine, Jazan University in Saudi Arabia. A direct interview was held with 11 faculty members, and six focus group discussions were conducted with students from the second to sixth year (34 students). Data were collected using semi-structured open-ended interview questions based on relevant literature. FINDINGS Most respondents (91.11%) believed AI systems would positively impact medical education, especially in research, knowledge gain, assessment, and simulation. However, ethical concerns were raised about threats to academic integrity, plagiarism, privacy/confidentiality issues, and AI's lacking cultural sensitivity. Faculty and students felt a need for training on AI use (80%) and that the curriculum could adapt to integrate AI (64.44%), though resources were seen as currently needing to be improved. CONCLUSION AI's potential to enhance medical education is generally viewed positively in the study, but ethical concerns must be addressed. Integrating AI into medical education programs requires adequate resources, training, and curriculum adaptation. There is still a need for further research in this area to develop comprehensive strategies.
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Affiliation(s)
- Sarah M Salih
- Department of Community and Family Medicine, Faculty of Medicine, Jazan University, Jazan, SAU
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16
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Al-Anazi S, Al-Omari A, Alanazi S, Marar A, Asad M, Alawaji F, Alwateid S. Artificial intelligence in respiratory care: Current scenario and future perspective. Ann Thorac Med 2024; 19:117-130. [PMID: 38766378 PMCID: PMC11100474 DOI: 10.4103/atm.atm_192_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Revised: 09/08/2023] [Accepted: 09/11/2023] [Indexed: 05/22/2024] Open
Abstract
BACKGROUND This narrative review aims to explore the current state and future perspective of artificial intelligence (AI) in respiratory care. The objective is to provide insights into the potential impact of AI in this field. METHODS A comprehensive analysis of relevant literature and research studies was conducted to examine the applications of AI in respiratory care and identify areas of advancement. The analysis included studies on remote monitoring, early detection, smart ventilation systems, and collaborative decision-making. RESULTS The obtained results highlight the transformative potential of AI in respiratory care. AI algorithms have shown promising capabilities in enabling tailored treatment plans based on patient-specific data. Remote monitoring using AI-powered devices allows for real-time feedback to health-care providers, enhancing patient care. AI algorithms have also demonstrated the ability to detect respiratory conditions at an early stage, leading to timely interventions and improved outcomes. Moreover, AI can optimize mechanical ventilation through continuous monitoring, enhancing patient comfort and reducing complications. Collaborative AI systems have the potential to augment the expertise of health-care professionals, leading to more accurate diagnoses and effective treatment strategies. CONCLUSION By improving diagnosis, AI has the potential to revolutionize respiratory care, treatment planning, and patient monitoring. While challenges and ethical considerations remain, the transformative impact of AI in this domain cannot be overstated. By leveraging the advancements and insights from this narrative review, health-care professionals and researchers can continue to harness the power of AI to improve patient outcomes and enhance respiratory care practices. IMPROVEMENTS Based on the findings, future research should focus on refining AI algorithms to enhance their accuracy, reliability, and interpretability. In addition, attention should be given to addressing ethical considerations, ensuring data privacy, and establishing regulatory frameworks to govern the responsible implementation of AI in respiratory care.
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Affiliation(s)
- Saad Al-Anazi
- Lead Clincial Appliaction AzeerTrade (Lowenstein Medical Company), Riyadh, Saudi Arabia
| | - Awad Al-Omari
- Department of Intensive Care, Dr. Sulaiman Al-Habib Group Hospitals, Riyadh, Saudi Arabia
| | - Safug Alanazi
- Intensivist, Al Hammadi Hospital, Riyadh, Saudi Arabia
| | - Aqeelah Marar
- Respiratory Care Administration, King Fahad Medical City, Riyadh, Saudi Arabia
| | - Mohammed Asad
- Department of Emergency, Dr. Sulaiman Al-Habib Group Hospitals, Riyadh, Saudi Arabia
| | - Fadi Alawaji
- Ar Rass General Hospital, Qassim Health Cluster, Senior Laboratory Specialist, Rass Region, Qassim City, Saudi Arabia
| | - Salman Alwateid
- Respiratory Care Administration, King Fahad Medical City, Riyadh, Saudi Arabia
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17
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Mehmood Qadri H, Bashir M, Khan M, Amir A, Khan AYY, Safdar Z, Chaudhry H, Younas UA, Bashir A. Knowledge, Awareness and Practice of Artificial Intelligence and Types of Realities Among Healthcare Professionals: A Nationwide Survey From Pakistan. Cureus 2024; 16:e57695. [PMID: 38711703 PMCID: PMC11070734 DOI: 10.7759/cureus.57695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/05/2024] [Indexed: 05/08/2024] Open
Abstract
Background Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, enabling them to perform tasks. The advancements in AI have also improved virtual reality (VR), augmented reality (AR) and mixed reality (MR) experience allowing a greater opportunity for use in the field of medicine. Objective To evaluate the knowledge, attitude and practice of AI and types of realities among Pakistani healthcare professionals (HCPs). Materials and methods This was a prospective, nationwide study designed at the Department of Neurosurgery at Punjab Institute of Neurosciences (PINS), Lahore, was conducted between January 2024 to February 2024. More than 500 HCPs were approached, out of which 176 participated in this survey consensually. A pre-formed general questionnaire based on knowledge, attitude and practices of AI and types of realities was modified according to local conditions. Google Forms (Google Inc., USA) was used to conduct the one-time sign up response. Statistical Package for Social Sciences (IBM SPSS Statistics for Windows, Version 24, USA) was used to analyze submitted responses. Results About 69.9% respondents were male HCPs. Most of the respondents were from the fields of neurosurgery, medicine and general surgery, i.e., 10.80%, 10.20% and 4%, respectively. More than 90% HCPs used Internet and electronic devices daily. A majority of 62.50% respondents agreed that AI brings benefits for the patients, while at the same time, 45.50% agreed that they would not trust the assessment of AI more than that of HCPs. 61% HCPs feared that AI-based systems could be manipulated from the outside sources, like terrorists and hackers. Although 90% respondents knew the definition of AR and VR, a strikingly low 40% respondents could only identify the practical applications of these realities when asked in a mini-quiz. About 61.40% HCPs never used any AI-based application throughout their clinical practice, but Google Health was used by 29.50% respondents, followed by Remote Patient Monitoring AI application used by 3.4% individuals. Conclusion There is an evident under-utilization of AI and types of realities in clinical practice in Pakistan. Lack of awareness, paucity of resources and conventional clinical practices are the key reasons identified. Pakistan is on the path towards the point where the developed world is currently. There is a potential to move past the initial stages of AI implementation and into more advanced modes of adopting AI and types of realities.
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Affiliation(s)
| | - Momin Bashir
- Microbiology, New York University, New York, USA
| | - Manal Khan
- Neurological Surgery, Punjab Institute of Neurosciences, Lahore, PAK
| | - Arham Amir
- General Surgery and Surgical Oncology, Shaikh Zayed Medical Complex, Lahore, PAK
| | | | - Zainab Safdar
- General Surgery, Lahore General Hospital, Lahore, PAK
| | | | | | - Asif Bashir
- Neurological Surgery, Punjab Institute of Neurosciences, Lahore, PAK
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18
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Gordon M, Daniel M, Ajiboye A, Uraiby H, Xu NY, Bartlett R, Hanson J, Haas M, Spadafore M, Grafton-Clarke C, Gasiea RY, Michie C, Corral J, Kwan B, Dolmans D, Thammasitboon S. A scoping review of artificial intelligence in medical education: BEME Guide No. 84. MEDICAL TEACHER 2024; 46:446-470. [PMID: 38423127 DOI: 10.1080/0142159x.2024.2314198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 01/31/2024] [Indexed: 03/02/2024]
Abstract
BACKGROUND Artificial Intelligence (AI) is rapidly transforming healthcare, and there is a critical need for a nuanced understanding of how AI is reshaping teaching, learning, and educational practice in medical education. This review aimed to map the literature regarding AI applications in medical education, core areas of findings, potential candidates for formal systematic review and gaps for future research. METHODS This rapid scoping review, conducted over 16 weeks, employed Arksey and O'Malley's framework and adhered to STORIES and BEME guidelines. A systematic and comprehensive search across PubMed/MEDLINE, EMBASE, and MedEdPublish was conducted without date or language restrictions. Publications included in the review spanned undergraduate, graduate, and continuing medical education, encompassing both original studies and perspective pieces. Data were charted by multiple author pairs and synthesized into various thematic maps and charts, ensuring a broad and detailed representation of the current landscape. RESULTS The review synthesized 278 publications, with a majority (68%) from North American and European regions. The studies covered diverse AI applications in medical education, such as AI for admissions, teaching, assessment, and clinical reasoning. The review highlighted AI's varied roles, from augmenting traditional educational methods to introducing innovative practices, and underscores the urgent need for ethical guidelines in AI's application in medical education. CONCLUSION The current literature has been charted. The findings underscore the need for ongoing research to explore uncharted areas and address potential risks associated with AI use in medical education. This work serves as a foundational resource for educators, policymakers, and researchers in navigating AI's evolving role in medical education. A framework to support future high utility reporting is proposed, the FACETS framework.
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Affiliation(s)
- Morris Gordon
- School of Medicine and Dentistry, University of Central Lancashire, Preston, UK
- Blackpool Hospitals NHS Foundation Trust, Blackpool, UK
| | - Michelle Daniel
- School of Medicine, University of California, San Diego, SanDiego, CA, USA
| | - Aderonke Ajiboye
- School of Medicine and Dentistry, University of Central Lancashire, Preston, UK
| | - Hussein Uraiby
- Department of Cellular Pathology, University Hospitals of Leicester NHS Trust, Leicester, UK
| | - Nicole Y Xu
- School of Medicine, University of California, San Diego, SanDiego, CA, USA
| | - Rangana Bartlett
- Department of Cognitive Science, University of California, San Diego, CA, USA
| | - Janice Hanson
- Department of Medicine and Office of Education, School of Medicine, Washington University in Saint Louis, Saint Louis, MO, USA
| | - Mary Haas
- Department of Emergency Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Maxwell Spadafore
- Department of Emergency Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
| | | | | | - Colin Michie
- School of Medicine and Dentistry, University of Central Lancashire, Preston, UK
| | - Janet Corral
- Department of Medicine, University of Nevada Reno, School of Medicine, Reno, NV, USA
| | - Brian Kwan
- School of Medicine, University of California, San Diego, SanDiego, CA, USA
| | - Diana Dolmans
- School of Health Professions Education, Faculty of Health, Maastricht University, Maastricht, NL, USA
| | - Satid Thammasitboon
- Center for Research, Innovation and Scholarship in Health Professions Education, Baylor College of Medicine, Houston, TX, USA
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19
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Goh WW, Chia KY, Cheung MF, Kee KM, Lwin MO, Schulz PJ, Chen M, Wu K, Ng SS, Lui R, Ang TL, Yeoh KG, Chiu HM, Wu DC, Sung JJ. Risk Perception, Acceptance, and Trust of Using AI in Gastroenterology Practice in the Asia-Pacific Region: Web-Based Survey Study. JMIR AI 2024; 3:e50525. [PMID: 38875591 PMCID: PMC11041476 DOI: 10.2196/50525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 08/28/2023] [Accepted: 11/23/2023] [Indexed: 06/16/2024]
Abstract
BACKGROUND The use of artificial intelligence (AI) can revolutionize health care, but this raises risk concerns. It is therefore crucial to understand how clinicians trust and accept AI technology. Gastroenterology, by its nature of being an image-based and intervention-heavy specialty, is an area where AI-assisted diagnosis and management can be applied extensively. OBJECTIVE This study aimed to study how gastroenterologists or gastrointestinal surgeons accept and trust the use of AI in computer-aided detection (CADe), computer-aided characterization (CADx), and computer-aided intervention (CADi) of colorectal polyps in colonoscopy. METHODS We conducted a web-based questionnaire from November 2022 to January 2023, involving 5 countries or areas in the Asia-Pacific region. The questionnaire included variables such as background and demography of users; intention to use AI, perceived risk; acceptance; and trust in AI-assisted detection, characterization, and intervention. We presented participants with 3 AI scenarios related to colonoscopy and the management of colorectal polyps. These scenarios reflect existing AI applications in colonoscopy, namely the detection of polyps (CADe), characterization of polyps (CADx), and AI-assisted polypectomy (CADi). RESULTS In total, 165 gastroenterologists and gastrointestinal surgeons responded to a web-based survey using the structured questionnaire designed by experts in medical communications. Participants had a mean age of 44 (SD 9.65) years, were mostly male (n=116, 70.3%), and mostly worked in publicly funded hospitals (n=110, 66.67%). Participants reported relatively high exposure to AI, with 111 (67.27%) reporting having used AI for clinical diagnosis or treatment of digestive diseases. Gastroenterologists are highly interested to use AI in diagnosis but show different levels of reservations in risk prediction and acceptance of AI. Most participants (n=112, 72.72%) also expressed interest to use AI in their future practice. CADe was accepted by 83.03% (n=137) of respondents, CADx was accepted by 78.79% (n=130), and CADi was accepted by 72.12% (n=119). CADe and CADx were trusted by 85.45% (n=141) of respondents and CADi was trusted by 72.12% (n=119). There were no application-specific differences in risk perceptions, but more experienced clinicians gave lesser risk ratings. CONCLUSIONS Gastroenterologists reported overall high acceptance and trust levels of using AI-assisted colonoscopy in the management of colorectal polyps. However, this level of trust depends on the application scenario. Moreover, the relationship among risk perception, acceptance, and trust in using AI in gastroenterology practice is not straightforward.
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Affiliation(s)
- Wilson Wb Goh
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore, Singapore
- School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
- Center for Biomedical Informatics, Nanyang Technological University, Singapore, Singapore
| | - Kendrick Ya Chia
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore, Singapore
- School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
- Center for Biomedical Informatics, Nanyang Technological University, Singapore, Singapore
| | - Max Fk Cheung
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore, Singapore
| | - Kalya M Kee
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore, Singapore
- Wee Kim Wee School of Communication and Information, Nanyang Technological University, Singapore, Singapore
| | - May O Lwin
- Wee Kim Wee School of Communication and Information, Nanyang Technological University, Singapore, Singapore
| | - Peter J Schulz
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore, Singapore
- Wee Kim Wee School of Communication and Information, Nanyang Technological University, Singapore, Singapore
| | - Minhu Chen
- The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Kaichun Wu
- Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Simon Sm Ng
- Department of Surgery, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China (Hong Kong)
| | - Rashid Lui
- Prince of Wales Hospital, Hospital Authority, Hong Kong, China (Hong Kong)
| | - Tiing Leong Ang
- Department of Gastroenterology and Hepatology, Changi General Hospital, SingHealth, Singapore, Singapore
| | - Khay Guan Yeoh
- Department of Gastroenterology and Hepatology, National University Hospital, National University Health System, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Han-Mo Chiu
- Department of Internal Medicine, National Taiwan University Hospital, Taiwan, China
- Department of Internal Medicine, College of Medicine, National Taiwan University, Taiwan, China
| | | | - Joseph Jy Sung
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore, Singapore
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20
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Seth I, Xie Y, Hunter-Smith DJ, Seifman MA, Rozen WM. Response to: Investigating the impact of innovative AI chatbot on post-pandemic medical education and clinical assistance: a comprehensive analysis. ANZ J Surg 2024; 94:495. [PMID: 37811820 DOI: 10.1111/ans.18718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 09/19/2023] [Indexed: 10/10/2023]
Affiliation(s)
- Ishith Seth
- Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, Australia
- Faculty of Medicine, Monash University, Melbourne, Victoria, Australia
| | - Yi Xie
- Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, Australia
| | - David J Hunter-Smith
- Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, Australia
- Faculty of Medicine, Monash University, Melbourne, Victoria, Australia
| | - Marc A Seifman
- Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, Australia
- Faculty of Medicine, Monash University, Melbourne, Victoria, Australia
| | - Warren M Rozen
- Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, Australia
- Faculty of Medicine, Monash University, Melbourne, Victoria, Australia
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21
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Xu Y, Jiang Z, Ting DSW, Kow AWC, Bello F, Car J, Tham YC, Wong TY. Medical education and physician training in the era of artificial intelligence. Singapore Med J 2024; 65:159-166. [PMID: 38527300 PMCID: PMC11060639 DOI: 10.4103/singaporemedj.smj-2023-203] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 02/08/2024] [Indexed: 03/27/2024]
Abstract
ABSTRACT With the rise of generative artificial intelligence (AI) and AI-powered chatbots, the landscape of medicine and healthcare is on the brink of significant transformation. This perspective delves into the prospective influence of AI on medical education, residency training and the continuing education of attending physicians or consultants. We begin by highlighting the constraints of the current education model, challenges in limited faculty, uniformity amidst burgeoning medical knowledge and the limitations in 'traditional' linear knowledge acquisition. We introduce 'AI-assisted' and 'AI-integrated' paradigms for medical education and physician training, targeting a more universal, accessible, high-quality and interconnected educational journey. We differentiate between essential knowledge for all physicians, specialised insights for clinician-scientists and mastery-level proficiency for clinician-computer scientists. With the transformative potential of AI in healthcare and service delivery, it is poised to reshape the pedagogy of medical education and residency training.
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Affiliation(s)
- Yueyuan Xu
- Tsinghua Medicine, School of Medicine, Tsinghua University, Beijing, China
| | - Zehua Jiang
- Tsinghua Medicine, School of Medicine, Tsinghua University, Beijing, China
- School of Clinical Medicine, Beijing Tsinghua Changgung Hospital, Beijing, China
| | - Daniel Shu Wei Ting
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Eye Academic Clinical Program, Duke-NUS Medical School, Singapore
- Byers Eye Institute, Stanford University, Palo Alto, CA, USA
| | - Alfred Wei Chieh Kow
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Fernando Bello
- Technology Enhanced Learning and Innovation Department, Duke-NUS Medical School, National University of Singapore, Singapore
| | - Josip Car
- Centre for Population Health Sciences, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | - Yih-Chung Tham
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Eye Academic Clinical Program, Duke-NUS Medical School, Singapore
- Centre for Innovation and Precision Eye Health and Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Tien Yin Wong
- Tsinghua Medicine, School of Medicine, Tsinghua University, Beijing, China
- School of Clinical Medicine, Beijing Tsinghua Changgung Hospital, Beijing, China
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
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22
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Özbek Güven G, Yilmaz Ş, Inceoğlu F. Determining medical students' anxiety and readiness levels about artificial intelligence. Heliyon 2024; 10:e25894. [PMID: 38384508 PMCID: PMC10878911 DOI: 10.1016/j.heliyon.2024.e25894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 01/23/2024] [Accepted: 02/05/2024] [Indexed: 02/23/2024] Open
Abstract
The aim of this study is to determine the levels of anxiety and readiness among medical students regarding artificial intelligence (AI) and examine the relationship between these factors. The research was conducted on medical students, and the data was collected through face-to-face and online surveys between April and June 2022. The study utilized a socio-demographic information form, an AI anxiety scale, and a medical AI readiness scale. The data collected from a total of 542 students were analyzed using the Statistical Program for Social Sciences (SPSS) version 25. Cronbach's α coefficient was used for reliability analysis. A path diagram was created using AMOS 24, and structural equation modelling (SEM) analysis was applied. The findings of the study indicate that medical students have a moderate level of readiness and a high level of anxiety regarding AI. Furthermore, an inverse relationship was found between AI readiness and AI anxiety. These results highlight the importance of increasing the preparedness of medical students for AI applications and reducing their anxieties. The study suggests the inclusion of AI in the medical curriculum and the development of a standardized curriculum to facilitate its teaching.
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Affiliation(s)
- Gamze Özbek Güven
- Department of Medical History and Ethics, School of Medicine, Yuksek Ihtisas University, Ankara, Türkiye
| | - Şerife Yilmaz
- Department of Medical History and Ethics, School of Medicine, Harran University, Şanlıurfa, Türkiye
| | - Feyza Inceoğlu
- Department of Biostatistics, School of Medicine, Malatya Turgut Ozal University, Malatya, Türkiye
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23
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Arien-Zakay H. Blended learning in nursing pharmacology: elevating cognitive skills, engagement and academic outcomes. Front Pharmacol 2024; 15:1361415. [PMID: 38455960 PMCID: PMC10917888 DOI: 10.3389/fphar.2024.1361415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Accepted: 02/06/2024] [Indexed: 03/09/2024] Open
Abstract
Pharmacological education is crucial for healthcare professionals to safely manage medications and reduce errors. Traditional lecture-based learning (LBL) often struggles to address this complexity, whereas newer methods, such as flipped classrooms and problem-based learning, yield mixed results, particularly in pre-clinical contexts, owing to students' limited experience. Our nursing pharmacology course under LBL recorded a high failure rate of 37.8% and marginal passing scores across five cohorts (n = 849 students). An analysis using Bloom's taxonomy revealed significant gaps in higher-order cognitive skills. As a remedy, the course was transformed into a novel blended learning format that integrated question-based learning (QBL) to enhance critical thinking across all cognitive levels. This model blends asynchronous and synchronous learning, is tailored to individual needs in large classes, and fosters continuous, student-centric learning. The redesign markedly decreased the failure rate by approximately 2.8-fold and increased the average grade by 11.8 points among 426 students. It notably improved the pass rates in advanced cognitive categories, such as "Evaluate" and "Create" by 19.0% and 24.2%, respectively. Additionally, the blended course showed increased student engagement, reflecting a dynamic and effective learning environment that significantly elevated participation and academic outcomes at all cognitive levels. This study demonstrated the profound impact of blended learning in pharmacology. By integrating QBL with various teaching methods, it surpasses traditional lecture-based limitations, enhancing engagement and understanding of complex topics by nursing students. Notable improvements in foundational and advanced learning suggest its broader application in health professionals' education, effectively equipping students for clinical pharmacology challenges.
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Affiliation(s)
- Hadar Arien-Zakay
- The Faculty of Medicine, School of Pharmacy, Institute for Drug Research, The Hebrew University of Jerusalem, Jerusalem, Israel
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24
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Weidener L, Fischer M. Proposing a Principle-Based Approach for Teaching AI Ethics in Medical Education. JMIR MEDICAL EDUCATION 2024; 10:e55368. [PMID: 38285931 PMCID: PMC10891487 DOI: 10.2196/55368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 01/02/2024] [Accepted: 01/29/2024] [Indexed: 01/31/2024]
Abstract
The use of artificial intelligence (AI) in medicine, potentially leading to substantial advancements such as improved diagnostics, has been of increasing scientific and societal interest in recent years. However, the use of AI raises new ethical challenges, such as an increased risk of bias and potential discrimination against patients, as well as misdiagnoses potentially leading to over- or underdiagnosis with substantial consequences for patients. Recognizing these challenges, current research underscores the importance of integrating AI ethics into medical education. This viewpoint paper aims to introduce a comprehensive set of ethical principles for teaching AI ethics in medical education. This dynamic and principle-based approach is designed to be adaptive and comprehensive, addressing not only the current but also emerging ethical challenges associated with the use of AI in medicine. This study conducts a theoretical analysis of the current academic discourse on AI ethics in medical education, identifying potential gaps and limitations. The inherent interconnectivity and interdisciplinary nature of these anticipated challenges are illustrated through a focused discussion on "informed consent" in the context of AI in medicine and medical education. This paper proposes a principle-based approach to AI ethics education, building on the 4 principles of medical ethics-autonomy, beneficence, nonmaleficence, and justice-and extending them by integrating 3 public health ethics principles-efficiency, common good orientation, and proportionality. The principle-based approach to teaching AI ethics in medical education proposed in this study offers a foundational framework for addressing the anticipated ethical challenges of using AI in medicine, recommended in the current academic discourse. By incorporating the 3 principles of public health ethics, this principle-based approach ensures that medical ethics education remains relevant and responsive to the dynamic landscape of AI integration in medicine. As the advancement of AI technologies in medicine is expected to increase, medical ethics education must adapt and evolve accordingly. The proposed principle-based approach for teaching AI ethics in medical education provides an important foundation to ensure that future medical professionals are not only aware of the ethical dimensions of AI in medicine but also equipped to make informed ethical decisions in their practice. Future research is required to develop problem-based and competency-oriented learning objectives and educational content for the proposed principle-based approach to teaching AI ethics in medical education.
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Affiliation(s)
- Lukas Weidener
- UMIT TIROL - Private University for Health Sciences and Health Technology, Hall in Tirol, Austria
| | - Michael Fischer
- UMIT TIROL - Private University for Health Sciences and Health Technology, Hall in Tirol, Austria
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Nguyen T. ChatGPT in Medical Education: A Precursor for Automation Bias? JMIR MEDICAL EDUCATION 2024; 10:e50174. [PMID: 38231545 PMCID: PMC10831594 DOI: 10.2196/50174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 12/11/2023] [Indexed: 01/18/2024]
Abstract
Artificial intelligence (AI) in health care has the promise of providing accurate and efficient results. However, AI can also be a black box, where the logic behind its results is nonrational. There are concerns if these questionable results are used in patient care. As physicians have the duty to provide care based on their clinical judgment in addition to their patients' values and preferences, it is crucial that physicians validate the results from AI. Yet, there are some physicians who exhibit a phenomenon known as automation bias, where there is an assumption from the user that AI is always right. This is a dangerous mindset, as users exhibiting automation bias will not validate the results, given their trust in AI systems. Several factors impact a user's susceptibility to automation bias, such as inexperience or being born in the digital age. In this editorial, I argue that these factors and a lack of AI education in the medical school curriculum cause automation bias. I also explore the harms of automation bias and why prospective physicians need to be vigilant when using AI. Furthermore, it is important to consider what attitudes are being taught to students when introducing ChatGPT, which could be some students' first time using AI, prior to their use of AI in the clinical setting. Therefore, in attempts to avoid the problem of automation bias in the long-term, in addition to incorporating AI education into the curriculum, as is necessary, the use of ChatGPT in medical education should be limited to certain tasks. Otherwise, having no constraints on what ChatGPT should be used for could lead to automation bias.
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Affiliation(s)
- Tina Nguyen
- The University of Texas Medical Branch, Galveston, TX, United States
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Weidener L, Fischer M. Artificial Intelligence in Medicine: Cross-Sectional Study Among Medical Students on Application, Education, and Ethical Aspects. JMIR MEDICAL EDUCATION 2024; 10:e51247. [PMID: 38180787 PMCID: PMC10799276 DOI: 10.2196/51247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 10/26/2023] [Accepted: 12/02/2023] [Indexed: 01/06/2024]
Abstract
BACKGROUND The use of artificial intelligence (AI) in medicine not only directly impacts the medical profession but is also increasingly associated with various potential ethical aspects. In addition, the expanding use of AI and AI-based applications such as ChatGPT demands a corresponding shift in medical education to adequately prepare future practitioners for the effective use of these tools and address the associated ethical challenges they present. OBJECTIVE This study aims to explore how medical students from Germany, Austria, and Switzerland perceive the use of AI in medicine and the teaching of AI and AI ethics in medical education in accordance with their use of AI-based chat applications, such as ChatGPT. METHODS This cross-sectional study, conducted from June 15 to July 15, 2023, surveyed medical students across Germany, Austria, and Switzerland using a web-based survey. This study aimed to assess students' perceptions of AI in medicine and the integration of AI and AI ethics into medical education. The survey, which included 53 items across 6 sections, was developed and pretested. Data analysis used descriptive statistics (median, mode, IQR, total number, and percentages) and either the chi-square or Mann-Whitney U tests, as appropriate. RESULTS Surveying 487 medical students across Germany, Austria, and Switzerland revealed limited formal education on AI or AI ethics within medical curricula, although 38.8% (189/487) had prior experience with AI-based chat applications, such as ChatGPT. Despite varied prior exposures, 71.7% (349/487) anticipated a positive impact of AI on medicine. There was widespread consensus (385/487, 74.9%) on the need for AI and AI ethics instruction in medical education, although the current offerings were deemed inadequate. Regarding the AI ethics education content, all proposed topics were rated as highly relevant. CONCLUSIONS This study revealed a pronounced discrepancy between the use of AI-based (chat) applications, such as ChatGPT, among medical students in Germany, Austria, and Switzerland and the teaching of AI in medical education. To adequately prepare future medical professionals, there is an urgent need to integrate the teaching of AI and AI ethics into the medical curricula.
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Affiliation(s)
- Lukas Weidener
- Research Unit for Quality and Ethics in Health Care, UMIT TIROL - Private University for Health Sciences and Health Technology, Hall in Tirol, Austria
| | - Michael Fischer
- Research Unit for Quality and Ethics in Health Care, UMIT TIROL - Private University for Health Sciences and Health Technology, Hall in Tirol, Austria
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Bohler F, Aggarwal N, Peters G, Taranikanti V. Future Implications of Artificial Intelligence in Medical Education. Cureus 2024; 16:e51859. [PMID: 38327947 PMCID: PMC10848885 DOI: 10.7759/cureus.51859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/06/2024] [Indexed: 02/09/2024] Open
Abstract
Artificial intelligence has experienced explosive growth in the past year that will have implications in all aspects of our lives, including medicine. In order to train a physician workforce that understands these new advancements, medical educators must take steps now to ensure that physicians are adequately trained in medical school, residency, and fellowship programs to become proficient in the usage of artificial intelligence in medical practice. This manuscript discusses the various considerations that leadership within medical training programs should be mindful of when deciding how to best integrate artificial intelligence into their curricula.
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Affiliation(s)
- Forrest Bohler
- Foundational Medical Studies, Oakland University William Beaumont School of Medicine, Rochester, USA
| | - Nikhil Aggarwal
- Foundational Medical Studies, Oakland University William Beaumont School of Medicine, Rochester, USA
| | - Garrett Peters
- Foundational Medical Studies, Oakland University William Beaumont School of Medicine, Rochester, USA
| | - Varna Taranikanti
- Foundational Medical Studies, Oakland University William Beaumont School of Medicine, Rochester, USA
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Abdel Aziz MH, Rowe C, Southwood R, Nogid A, Berman S, Gustafson K. A scoping review of artificial intelligence within pharmacy education. AMERICAN JOURNAL OF PHARMACEUTICAL EDUCATION 2024; 88:100615. [PMID: 37914030 DOI: 10.1016/j.ajpe.2023.100615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 10/19/2023] [Accepted: 10/25/2023] [Indexed: 11/03/2023]
Abstract
OBJECTIVES This scoping review aimed to summarize the available literature on the use of artificial intelligence (AI) in pharmacy education and identify gaps where additional research is needed. FINDINGS Seven studies specifically addressing the use of AI in pharmacy education were identified. Of these 7 studies, 5 focused on AI use in the context of teaching and learning, 1 on the prediction of academic performance for admissions, and the final study focused on using AI text generation to elucidate the benefits and limitations of ChatGPT use in pharmacy education. SUMMARY There are currently a limited number of available publications that describe AI use in pharmacy education. Several challenges exist regarding the use of AI in pharmacy education, including the need for faculty expertise and time, limited generalizability of tools, limited outcomes data, and several legal and ethical concerns. As AI use increases and implementation becomes more standardized, opportunities will be created for the inclusion of AI in pharmacy education.
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Affiliation(s)
- May H Abdel Aziz
- University of Texas at Tyler, Ben and Maytee Fisch College of Pharmacy, Department of Pharmaceutical Sciences and Health Outcomes, Tyler, TX, USA.
| | - Casey Rowe
- University of Florida College of Pharmacy, Department of Pharmacotherapy and Translational Research, Orlando, FL, USA
| | - Robin Southwood
- University of Georgia, College of Pharmacy, Department of Clinical and Administrative Pharmacy, Athens, GA, USA
| | - Anna Nogid
- Fairleigh Dickinson University, School of Pharmacy and Health Sciences, Department of Pharmacy Practice, Florham Park, NJ, USA
| | - Sarah Berman
- University of the Incarnate Word, Feik School of Pharmacy, Department of Pharmacy Practice, San Antonio, TX, USA
| | - Kyle Gustafson
- Northeast Ohio Medical University, Department of Pharmacy Practice, Rootstown, OH, USA
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Syed W, Al-Rawi MBA. Community pharmacists awareness, perceptions, and opinions of artificial intelligence: A cross-sectional study in Riyadh, Saudi Arabia. Technol Health Care 2024; 32:481-493. [PMID: 37694330 DOI: 10.3233/thc-230784] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
BACKGROUND Several revolutions are currently taking place in the healthcare industry to provide accurate, reliable, and valid healthcare to patients. Among these is artificial intelligence (AI). OBJECTIVE This study aimed to assess the CP's awareness, perceptions, and opinions of AI in health care among community pharmacists. METHODS This cross-sectional survey-based study was conducted over 3 months in 2023 using structured prevalidated 34 items questionnaires. RESULTS In this study, 94.5% (n= 258) of the CPs were aware of AI, yet 25.6% (n= 70) believed that AI would eventually replace healthcare professionals. However, 63.4% (n= 173) of the CPs concurred that AI is a technology that supports healthcare workers. 12.8% of the CPs believed that there is a risk of losing their jobs if AI is widely used in Saudi Arabia, but 68.9% (n= 188) of them considered that healthcare professionals will benefit from the extensive use of AI. Eighty-four percent of CPs (n= 232) agreed or strongly agreed that AI decreases drug mistakes in clinical practice. Similarly, 86% of the CPs (n= 235) concurred that AI makes it easier for patients to access the service. In contrast, almost 58% of the CPs (n= 232) agreed that AI makes it easier for healthcare professionals to acquire information, and 87.9% of the CPs (n= 240) said that AI helps them make better decisions. CONCLUSION This study concluded that most of the CPs were aware of AI and agreed that AI is a tool that helps healthcare professionals. In addition, the majority of the CPs thought that AI adoption in healthcare practice will benefit healthcare practitioners.
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Affiliation(s)
- Wajid Syed
- Department of Clinical Pharmacy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
| | - Mahmood Basil A Al-Rawi
- Department of Optometry, College of Applied Medical Sciences, King Saud University, Riyadh, Saudi Arabia
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Al-Qerem W, Eberhardt J, Jarab A, Al Bawab AQ, Hammad A, Alasmari F, Alazab B, Husein DA, Alazab J, Al-Beool S. Exploring knowledge, attitudes, and practices towards artificial intelligence among health professions' students in Jordan. BMC Med Inform Decis Mak 2023; 23:288. [PMID: 38098095 PMCID: PMC10722664 DOI: 10.1186/s12911-023-02403-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 12/11/2023] [Indexed: 12/17/2023] Open
Abstract
INTRODUCTION The integration of Artificial Intelligence (AI) in medical education and practice is a significant development. This study examined the Knowledge, Attitudes, and Practices (KAP) of health professions' students in Jordan concerning AI, providing insights into their preparedness and perceptions. METHODS An online questionnaire was distributed to 483 Jordanian health professions' students via social media. Demographic data, AI-related KAP, and barriers were collected. Quantile regression models analyzed associations between variables and KAP scores. RESULTS Moderate AI knowledge was observed among participants, with specific understanding of data requirements and barriers. Attitudes varied, combining skepticism about AI replacing human teachers with recognition of its value. While AI tools were used for specific tasks, broader integration in medical education and practice was limited. Barriers included lack of knowledge, access, time constraints, and curriculum gaps. CONCLUSIONS This study highlights the need to enhance medical education with AI topics and address barriers. Students need to be better prepared for AI integration, in order to enable medical education to harness AI's potential for improved patient care and training.
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Affiliation(s)
- Walid Al-Qerem
- Department of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan, 11733, Amman, Jordan.
| | - Judith Eberhardt
- School of Social Sciences, Humanities and Law, Department of Psychology, Teesside University, TS1 3BX, Middlesbrough, UK
| | - Anan Jarab
- College of Pharmacy, Al Ain University, 64141, Abu Dhabi, UAE
- AAU Health and Biomedical Research Center, Al Ain University, 112612, Abu Dhabi, United Arab Emirates
- Department of Clinical Pharmacy, Faculty of Pharmacy, Jordan University of Science and Technology, 22110, Irbid, Jordan
| | - Abdel Qader Al Bawab
- Department of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan, 11733, Amman, Jordan
| | - Alaa Hammad
- Department of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan, 11733, Amman, Jordan
| | - Fawaz Alasmari
- Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, 12372, Riyadh, Saudi Arabia
| | - Badi'ah Alazab
- Department of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan, 11733, Amman, Jordan
| | - Daoud Abu Husein
- Department of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan, 11733, Amman, Jordan
| | - Jumana Alazab
- School of Medicine, The University of Jordan, 11910, Amman, Jordan
| | - Saed Al-Beool
- School of Medicine, The University of Jordan, 11910, Amman, Jordan
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Truong NM, Vo TQ, Tran HTB, Nguyen HT, Pham VNH. Healthcare students' knowledge, attitudes, and perspectives toward artificial intelligence in the southern Vietnam. Heliyon 2023; 9:e22653. [PMID: 38107295 PMCID: PMC10724669 DOI: 10.1016/j.heliyon.2023.e22653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 11/15/2023] [Accepted: 11/16/2023] [Indexed: 12/19/2023] Open
Abstract
The application of new technologies in medical education still lags behind the extraordinary advances of AI. This study examined the understanding, attitudes, and perspectives of Vietnamese medical students toward AI and its consequences, as well as their knowledge of existing AI operations in Vietnam. A cross-sectional online survey was administered to 1142 students enrolled in undergraduate medicine and pharmacy programs. Most of the participants had no understanding of AI in healthcare (1053 or 92.2 %). The majority believed that AI would benefit their careers (890 or 77.9 %) and that such innovation will be used to oversee public health and epidemic prevention on their behalf (882 or 77.2 %). The proportion of students with satisfactory knowledge significantly differed depending on gender (P < 0.001), major (P = 0.003), experience (P < 0.001), and income (P = 0.011). The percentage of respondents with positive attitudes significantly differed by year level (P = 0.008) and income (P = 0.003), and the proportion with favorable perspectives regarding AI varied considerably by age (P = 0.046) and major (P < 0.001). Most of the participants wanted to integrate AI into radiology and digital imaging training (P = 0.283), while the fifth-year students wished to learn about AI in medical genetics and genomics (P < 0.001, 4.0 ± 0.8). The male students had 1.898 times more adequate knowledge of AI than their female counterparts, and those who had attended webinars/lectures/courses on AI in healthcare had 4.864 times more adequate knowledge than those having no such experiences. The majority believed that the barrier to implementing AI in healthcare is the lack of financial resources (83.54 %) and appropriate training (81.00 %). Participants saw AI as a "partner" rather than a "competitor", but the majority of low knowledge was recorded. Future research should take into account the way to integrate AI into medical training programs for healthcare students.
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Affiliation(s)
- Nguyen Minh Truong
- Faculty of Pharmacy, Pham Ngoc Thach University of Medicine, Ho Chi Minh City, 700000, Viet Nam
| | - Trung Quang Vo
- Faculty of Pharmacy, Pham Ngoc Thach University of Medicine, Ho Chi Minh City, 700000, Viet Nam
| | - Hien Thi Bich Tran
- Faculty of Pharmacy, Pham Ngoc Thach University of Medicine, Ho Chi Minh City, 700000, Viet Nam
| | - Hiep Thanh Nguyen
- Faculty of Medicine, Pham Ngoc Thach University of Medicine, Ho Chi Minh City, 700000, Viet Nam
| | - Van Nu Hanh Pham
- Faculty of Pharmaceutical Management and Economic, Hanoi University of Pharmacy, Hanoi, 100000, Viet Nam
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Veras M, Dyer JO, Rooney M, Barros Silva PG, Rutherford D, Kairy D. Usability and Efficacy of Artificial Intelligence Chatbots (ChatGPT) for Health Sciences Students: Protocol for a Crossover Randomized Controlled Trial. JMIR Res Protoc 2023; 12:e51873. [PMID: 37999958 DOI: 10.2196/51873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 10/18/2023] [Accepted: 10/20/2023] [Indexed: 11/25/2023] Open
Abstract
BACKGROUND The integration of artificial intelligence (AI) into health sciences students' education holds significant importance. The rapid advancement of AI has opened new horizons in scientific writing and has the potential to reshape human-technology interactions. AI in education may impact critical thinking, leading to unintended consequences that need to be addressed. Understanding the implications of AI adoption in education is essential for ensuring its responsible and effective use, empowering health sciences students to navigate AI-driven technologies' evolving field with essential knowledge and skills. OBJECTIVE This study aims to provide details on the study protocol and the methods used to investigate the usability and efficacy of ChatGPT, a large language model. The primary focus is on assessing its role as a supplementary learning tool for improving learning processes and outcomes among undergraduate health sciences students, with a specific emphasis on chronic diseases. METHODS This single-blinded, crossover, randomized, controlled trial is part of a broader mixed methods study, and the primary emphasis of this paper is on the quantitative component of the overall research. A total of 50 students will be recruited for this study. The alternative hypothesis posits that there will be a significant difference in learning outcomes and technology usability between students using ChatGPT (group A) and those using standard web-based tools (group B) to access resources and complete assignments. Participants will be allocated to sequence AB or BA in a 1:1 ratio using computer-generated randomization. Both arms include students' participation in a writing assignment intervention, with a washout period of 21 days between interventions. The primary outcome is the measure of the technology usability and effectiveness of ChatGPT, whereas the secondary outcome is the measure of students' perceptions and experiences with ChatGPT as a learning tool. Outcome data will be collected up to 24 hours after the interventions. RESULTS This study aims to understand the potential benefits and challenges of incorporating AI as an educational tool, particularly in the context of student learning. The findings are expected to identify critical areas that need attention and help educators develop a deeper understanding of AI's impact on the educational field. By exploring the differences in the usability and efficacy between ChatGPT and conventional web-based tools, this study seeks to inform educators and students on the responsible integration of AI into academic settings, with a specific focus on health sciences education. CONCLUSIONS By exploring the usability and efficacy of ChatGPT compared with conventional web-based tools, this study seeks to inform educators and students about the responsible integration of AI into academic settings. TRIAL REGISTRATION ClinicalTrails.gov NCT05963802; https://clinicaltrials.gov/study/NCT05963802. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) PRR1-10.2196/51873.
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Affiliation(s)
- Mirella Veras
- Health Sciences, Carleton University, Ottawa, ON, Canada
- Centre for Interdisciplinary Research in Rehabilitation of Greater Montreal, Montréal, QC, Canada
| | - Joseph-Omer Dyer
- École de Réadaptation, Faculté de Médecine, Université de Montréal, Montréal, QC, Canada
- Groupe Interdisciplinaire de Recherche sur la Cognition et le Raisonnement Professionnel, Faculty of Medicine, Université de Montréal, Montréal, QC, Canada
| | - Morgan Rooney
- Teaching and Learning Services, Carleton University, Ottawa, ON, Canada
| | | | - Derek Rutherford
- School of Physiotherapy, Dalhousie University, Halifax, NS, Canada
| | - Dahlia Kairy
- Centre for Interdisciplinary Research in Rehabilitation of Greater Montreal, Montréal, QC, Canada
- École de Réadaptation, Faculté de Médecine, Université de Montréal, Montréal, QC, Canada
- Institut Universitaire sur la Réadaptation en Déficience Physique de Montréal, Centre Intégré Universitaire de Santé et Services Sociaux du Centre-Sud-de-l'Île-de-Montréal, Montréal, QC, Canada
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Li LT, Haley LC, Boyd AK, Bernstam EV. Technical/Algorithm, Stakeholder, and Society (TASS) barriers to the application of artificial intelligence in medicine: A systematic review. J Biomed Inform 2023; 147:104531. [PMID: 37884177 DOI: 10.1016/j.jbi.2023.104531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 09/14/2023] [Accepted: 10/22/2023] [Indexed: 10/28/2023]
Abstract
INTRODUCTION The use of artificial intelligence (AI), particularly machine learning and predictive analytics, has shown great promise in health care. Despite its strong potential, there has been limited use in health care settings. In this systematic review, we aim to determine the main barriers to successful implementation of AI in healthcare and discuss potential ways to overcome these challenges. METHODS We conducted a literature search in PubMed (1/1/2001-1/1/2023). The search was restricted to publications in the English language, and human study subjects. We excluded articles that did not discuss AI, machine learning, predictive analytics, and barriers to the use of these techniques in health care. Using grounded theory methodology, we abstracted concepts to identify major barriers to AI use in medicine. RESULTS We identified a total of 2,382 articles. After reviewing the 306 included papers, we developed 19 major themes, which we categorized into three levels: the Technical/Algorithm, Stakeholder, and Social levels (TASS). These themes included: Lack of Explainability, Need for Validation Protocols, Need for Standards for Interoperability, Need for Reporting Guidelines, Need for Standardization of Performance Metrics, Lack of Plan for Updating Algorithm, Job Loss, Skills Loss, Workflow Challenges, Loss of Patient Autonomy and Consent, Disturbing the Patient-Clinician Relationship, Lack of Trust in AI, Logistical Challenges, Lack of strategic plan, Lack of Cost-effectiveness Analysis and Proof of Efficacy, Privacy, Liability, Bias and Social Justice, and Education. CONCLUSION We identified 19 major barriers to the use of AI in healthcare and categorized them into three levels: the Technical/Algorithm, Stakeholder, and Social levels (TASS). Future studies should expand on barriers in pediatric care and focus on developing clearly defined protocols to overcome these barriers.
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Affiliation(s)
- Linda T Li
- Department of Surgery, Division of Pediatric Surgery, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029, United States; McWilliams School of Biomedical Informatics at UT Health Houston, 7000 Fannin St, Suite 600, Houston, TX 77030, United States.
| | - Lauren C Haley
- McGovern Medical School at the University of Texas Health Science Center at Houston, 6431 Fannin St, Houston, TX 77030, United States.
| | - Alexandra K Boyd
- McGovern Medical School at the University of Texas Health Science Center at Houston, 6431 Fannin St, Houston, TX 77030, United States.
| | - Elmer V Bernstam
- McWilliams School of Biomedical Informatics at UT Health Houston, 7000 Fannin St, Suite 600, Houston, TX 77030, United States; McGovern Medical School at the University of Texas Health Science Center at Houston, 6431 Fannin St, Houston, TX 77030, United States.
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Preiksaitis C, Rose C. Opportunities, Challenges, and Future Directions of Generative Artificial Intelligence in Medical Education: Scoping Review. JMIR MEDICAL EDUCATION 2023; 9:e48785. [PMID: 37862079 PMCID: PMC10625095 DOI: 10.2196/48785] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Revised: 07/28/2023] [Accepted: 09/28/2023] [Indexed: 10/21/2023]
Abstract
BACKGROUND Generative artificial intelligence (AI) technologies are increasingly being utilized across various fields, with considerable interest and concern regarding their potential application in medical education. These technologies, such as Chat GPT and Bard, can generate new content and have a wide range of possible applications. OBJECTIVE This study aimed to synthesize the potential opportunities and limitations of generative AI in medical education. It sought to identify prevalent themes within recent literature regarding potential applications and challenges of generative AI in medical education and use these to guide future areas for exploration. METHODS We conducted a scoping review, following the framework by Arksey and O'Malley, of English language articles published from 2022 onward that discussed generative AI in the context of medical education. A literature search was performed using PubMed, Web of Science, and Google Scholar databases. We screened articles for inclusion, extracted data from relevant studies, and completed a quantitative and qualitative synthesis of the data. RESULTS Thematic analysis revealed diverse potential applications for generative AI in medical education, including self-directed learning, simulation scenarios, and writing assistance. However, the literature also highlighted significant challenges, such as issues with academic integrity, data accuracy, and potential detriments to learning. Based on these themes and the current state of the literature, we propose the following 3 key areas for investigation: developing learners' skills to evaluate AI critically, rethinking assessment methodology, and studying human-AI interactions. CONCLUSIONS The integration of generative AI in medical education presents exciting opportunities, alongside considerable challenges. There is a need to develop new skills and competencies related to AI as well as thoughtful, nuanced approaches to examine the growing use of generative AI in medical education.
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Affiliation(s)
- Carl Preiksaitis
- Department of Emergency Medicine, Stanford University School of Medicine, Palo Alto, CA, United States
| | - Christian Rose
- Department of Emergency Medicine, Stanford University School of Medicine, Palo Alto, CA, United States
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Weidener L, Fischer M. Teaching AI Ethics in Medical Education: A Scoping Review of Current Literature and Practices. PERSPECTIVES ON MEDICAL EDUCATION 2023; 12:399-410. [PMID: 37868075 PMCID: PMC10588522 DOI: 10.5334/pme.954] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 10/03/2023] [Indexed: 10/24/2023]
Abstract
Introduction The increasing use of Artificial Intelligence (AI) in medicine has raised ethical concerns, such as patient autonomy, bias, and transparency. Recent studies suggest a need for teaching AI ethics as part of medical curricula. This scoping review aimed to represent and synthesize the literature on teaching AI ethics as part of medical education. Methods The PRISMA-SCR guidelines and JBI methodology guided a literature search in four databases (PubMed, Embase, Scopus, and Web of Science) for the past 22 years (2000-2022). To account for the release of AI-based chat applications, such as ChatGPT, the literature search was updated to include publications until the end of June 2023. Results 1384 publications were originally identified and, after screening titles and abstracts, the full text of 87 publications was assessed. Following the assessment of the full text, 10 publications were included for further analysis. The updated literature search identified two additional relevant publications from 2023 were identified and included in the analysis. All 12 publications recommended teaching AI ethics in medical curricula due to the potential implications of AI in medicine. Anticipated ethical challenges such as bias were identified as the recommended basis for teaching content in addition to basic principles of medical ethics. Case-based teaching using real-world examples in interactive seminars and small groups was recommended as a teaching modality. Conclusion This scoping review reveals a scarcity of literature on teaching AI ethics in medical education, with most of the available literature being recent and theoretical. These findings emphasize the importance of more empirical studies and foundational definitions of AI ethics to guide the development of teaching content and modalities. Recognizing AI's significant impact of AI on medicine, additional research on the teaching of AI ethics in medical education is needed to best prepare medical students for future ethical challenges.
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Affiliation(s)
- Lukas Weidener
- UMIT TIROL – Private University for Health Sciences and Health Technology, Eduard-Wallnöfer-Zentrum 1, 6060 Hall in Tirol, Austria
| | - Michael Fischer
- Head of the Research Unit for Quality and Ethics in Health Care, UMIT TIROL – Private University for Health Sciences and Health Technology, Austria
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Zhong NN, Wang HQ, Huang XY, Li ZZ, Cao LM, Huo FY, Liu B, Bu LL. Enhancing head and neck tumor management with artificial intelligence: Integration and perspectives. Semin Cancer Biol 2023; 95:52-74. [PMID: 37473825 DOI: 10.1016/j.semcancer.2023.07.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 07/11/2023] [Accepted: 07/15/2023] [Indexed: 07/22/2023]
Abstract
Head and neck tumors (HNTs) constitute a multifaceted ensemble of pathologies that primarily involve regions such as the oral cavity, pharynx, and nasal cavity. The intricate anatomical structure of these regions poses considerable challenges to efficacious treatment strategies. Despite the availability of myriad treatment modalities, the overall therapeutic efficacy for HNTs continues to remain subdued. In recent years, the deployment of artificial intelligence (AI) in healthcare practices has garnered noteworthy attention. AI modalities, inclusive of machine learning (ML), neural networks (NNs), and deep learning (DL), when amalgamated into the holistic management of HNTs, promise to augment the precision, safety, and efficacy of treatment regimens. The integration of AI within HNT management is intricately intertwined with domains such as medical imaging, bioinformatics, and medical robotics. This article intends to scrutinize the cutting-edge advancements and prospective applications of AI in the realm of HNTs, elucidating AI's indispensable role in prevention, diagnosis, treatment, prognostication, research, and inter-sectoral integration. The overarching objective is to stimulate scholarly discourse and invigorate insights among medical practitioners and researchers to propel further exploration, thereby facilitating superior therapeutic alternatives for patients.
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Affiliation(s)
- Nian-Nian Zhong
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Han-Qi Wang
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Xin-Yue Huang
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Zi-Zhan Li
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Lei-Ming Cao
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Fang-Yi Huo
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Bing Liu
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China; Department of Oral & Maxillofacial - Head Neck Oncology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China.
| | - Lin-Lin Bu
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China; Department of Oral & Maxillofacial - Head Neck Oncology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China.
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37
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Caruso PF, Greco M, Ebm C, Angelotti G, Cecconi M. Implementing Artificial Intelligence: Assessing the Cost and Benefits of Algorithmic Decision-Making in Critical Care. Crit Care Clin 2023; 39:783-793. [PMID: 37704340 DOI: 10.1016/j.ccc.2023.03.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/15/2023]
Abstract
This article provides an overview of the most useful artificial intelligence algorithms developed in critical care, followed by a comprehensive outline of the benefits and limitations. We begin by describing how nurses and physicians might be aided by these new technologies. We then move to the possible changes in clinical guidelines with personalized medicine that will allow tailored therapies and probably will increase the quality of the care provided to patients. Finally, we describe how artificial intelligence models can unleash researchers' minds by proposing new strategies, by increasing the quality of clinical practice, and by questioning current knowledge and understanding.
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Affiliation(s)
- Pier Francesco Caruso
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, Milan 20072, Italy; Department of Anesthesiology and Intensive Care, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, Milan 20089, Italy
| | - Massimiliano Greco
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, Milan 20072, Italy; Department of Anesthesiology and Intensive Care, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, Milan 20089, Italy.
| | - Claudia Ebm
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, Milan 20072, Italy
| | - Giovanni Angelotti
- Artificial Intelligence Center, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, Milan 20089, Italy
| | - Maurizio Cecconi
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, Milan 20072, Italy; Department of Anesthesiology and Intensive Care, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, Milan 20089, Italy
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Chinnadurai S, Mahadevan S, Navaneethakrishnan B, Mamadapur M. Decoding Applications of Artificial Intelligence in Rheumatology. Cureus 2023; 15:e46164. [PMID: 37905264 PMCID: PMC10613315 DOI: 10.7759/cureus.46164] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/27/2023] [Indexed: 11/02/2023] Open
Abstract
Artificial intelligence (AI) is not a newcomer in medicine. It has been employed for image analysis, disease diagnosis, drug discovery, and improving overall patient care. ChatGPT (Chat Generative Pre-trained Transformer, Inc., Delaware) has renewed interest and enthusiasm in artificial intelligence. Algorithms, machine learning, deep learning, and data analysis are some of the complex terminologies often encountered when health professionals try to learn AI. In this article, we try to review the practical applications of artificial intelligence in vernacular language in the fields of medicine and rheumatology in particular. From the standpoint of the everyday physician, we have endeavored to encapsulate the influence of AI on the cutting edge of medical practice and the potential revolutionary shift in the realm of rheumatology.
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Affiliation(s)
- Saranya Chinnadurai
- Rheumatology, Sri Ramachandra Institute of Higher Education and Research, Chennai, IND
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Ghorashi N, Ismail A, Ghosh P, Sidawy A, Javan R. AI-Powered Chatbots in Medical Education: Potential Applications and Implications. Cureus 2023; 15:e43271. [PMID: 37692629 PMCID: PMC10492519 DOI: 10.7759/cureus.43271] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/10/2023] [Indexed: 09/12/2023] Open
Abstract
Artificial intelligence (AI) is anticipated to have a considerable impact on the routine practice of medicine, spanning from medical education to clinical practice across specialties and, ultimately, patient care. With the imminent widespread adoption of AI in medical practice, it is imperative that medical schools adapt to the use of these advanced technologies in their curriculum to produce future healthcare professionals who can seamlessly integrate these tools into practice. Chatbots, AI systems programmed to process and generate human language, are currently being evaluated for various tasks in medical education. This paper explores the potential applications and implications of chatbots in medical education, specifically in learning and research. With their capability to summarize, simplify complex concepts, automate the creation of memory aids, and serve as an interactive tutor and point-of-care medical reference, chatbots have the potential to enhance students' comprehension, retention, and application of medical knowledge in real-time. While the integration of AI-powered chatbots in medical education presents numerous advantages, it is crucial for students to use these tools as assistive tools rather than relying on them entirely. Chatbots should be programmed to reference evidence-based medical resources and produce precise and trustworthy content that adheres to medical science standards, scientific writing guidelines, and ethical considerations.
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Affiliation(s)
- Nima Ghorashi
- Department of Radiology, George Washington University School of Medicine and Health Sciences, Washington, USA
| | - Ahmed Ismail
- Department of Radiology, George Washington University School of Medicine and Health Sciences, Washington, USA
| | - Pritha Ghosh
- Department of Neurology, George Washington University School of Medicine and Health Sciences, Washington, USA
| | - Anton Sidawy
- Department of Surgery, George Washington University School of Medicine and Health Sciences, Washington, USA
| | - Ramin Javan
- Department of Radiology, George Washington University School of Medicine and Health Sciences, Washington, USA
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Affiliation(s)
- Bruce C Dobey
- Bruce C. Dobey, MHS, PA-C, is an assistant professor and assessment & evaluation coordinator for the Department of PA Medicine, Michigan State University College of Osteopathic Medicine, East Lansing, Michigan
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Davoud SC, Kovacheva VP. On the Horizon: Specific Applications of Automation and Artificial Intelligence in Anesthesiology. CURRENT ANESTHESIOLOGY REPORTS 2023; 13:31-40. [PMID: 38106626 PMCID: PMC10722862 DOI: 10.1007/s40140-023-00558-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/19/2023] [Indexed: 04/08/2023]
Abstract
Purpose of Review The purpose of this review is to summarize the current research and critically examine artificial intelligence (AI) technologies and their applicability to the daily practice of anesthesiologists. Recent Findings Novel AI tools are developed using data from electronic health records, imaging, waveforms, clinical notes, and wearables. These tools can accurately predict the perioperative risk for adverse outcomes, the need for blood transfusion, and the risk of difficult intubation. Intraoperatively, AI models can assist with technical skill augmentation, patient monitoring, and management. Postoperatively, AI technology can aid in preventing complications and discharge planning. While further prospective validation is needed, these early applications demonstrate promise in every area of perioperative care. Summary The practice of anesthesiology is at a precipice fueled by technological innovation. The clinical AI implementation would enable personalized and safer patient care by offering actionable insights from the wealth of perioperative data.
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Affiliation(s)
- Sherwin C. Davoud
- Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women’s Hospital, Harvard Medical School, 75 Francis St., L1, Boston, MA, USA
| | - Vesela P. Kovacheva
- Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women’s Hospital, Harvard Medical School, 75 Francis St., L1, Boston, MA, USA
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Huston JC, Kaminski N. A Picture Worth a Thousand Words, Created with One Sentence: Using Artificial Intelligence-created Art to Enhance Medical Education. ATS Sch 2023; 4:145-151. [PMID: 37533539 PMCID: PMC10391737 DOI: 10.34197/ats-scholar.2022-0141ps] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 04/11/2023] [Indexed: 08/04/2023] Open
Abstract
With the advancement of digital technology, the medium in which medical education is delivered has evolved from chalk talks, to the use of overhead projectors, and now to a digital format. Although the old modus operandi of a good chalk talk can still seize the attention of pupils and inspire, new methods continue to emerge. In recent years, artificial intelligence has materialized as a tool to advance the medical field, and medical education is no exception. The purpose of this perspective is to introduce a new, powerful instrument to the medical educator: artificial intelligence-generated art. This tool can be leveraged to improve medical education, both in narrative medicine and in the creation of educational imagery.
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Affiliation(s)
- John C Huston
- Pulmonary, Critical Care, and Sleep Medicine, Yale School of Medicine, New Haven, Connecticut
| | - Naftali Kaminski
- Pulmonary, Critical Care, and Sleep Medicine, Yale School of Medicine, New Haven, Connecticut
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Buabbas AJ, Miskin B, Alnaqi AA, Ayed AK, Shehab AA, Syed-Abdul S, Uddin M. Investigating Students' Perceptions towards Artificial Intelligence in Medical Education. Healthcare (Basel) 2023; 11:healthcare11091298. [PMID: 37174840 PMCID: PMC10178742 DOI: 10.3390/healthcare11091298] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 04/26/2023] [Accepted: 04/29/2023] [Indexed: 05/15/2023] Open
Abstract
Implementing a reform in medical education requires students' awareness regarding the importance of artificial intelligence (AI) in modern medical practice. The objective of this study was to investigate students' perceptions of AI in medical education. A cross-sectional survey was conducted from June 2021 to November 2021 using an online questionnaire to collect data from medical students in the Faculty of Medicine at Kuwait University, Kuwait. The response rate for the survey was 51%, with a sample size of 352. Most students (349 (99.1%)) agreed that AI would play an important role in healthcare. More than half of the students (213 (60.5%)) understood the basic principles of AI, and (329 (93.4%)) students showed comfort with AI terminology. Many students (329 (83.5%)) believed that learning about AI would benefit their careers, and (289 (82.1%)) believed that medical students should receive AI teaching or training. The study revealed that most students had positive perceptions of AI. Undoubtedly, the role of AI in the future of medicine will be significant, and AI-based medical practice is required. There was a strong consensus that AI will not replace doctors but will drastically transform healthcare practices.
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Affiliation(s)
- Ali Jasem Buabbas
- Department of Community Medicine and Behavioral Sciences, Faculty of Medicine, Kuwait University, Jabriya 046300, Kuwait
| | - Brouj Miskin
- Ministry of Health, Jamal Abdel Nasser Street, Sulaibkhat, Kuwait City 13001, Kuwait
| | - Amar Ali Alnaqi
- Department of Surgery, Faculty of Medicine, Kuwait University, Jabriya 046300, Kuwait
| | - Adel K Ayed
- Department of Surgery, Faculty of Medicine, Kuwait University, Jabriya 046300, Kuwait
| | - Abrar Abdulmohsen Shehab
- Department of Immunology, Mubarak Alkabeer Hospital, Hawalli Health Region, Ministry of Health, Jabriya 047060, Kuwait
| | - Shabbir Syed-Abdul
- Graduate Institute of Bioinformatics, School of Gerontology and Long-Term Care, Taipei Medical University, Taipei 100-116, Taiwan
- International Center for Health Information Technology, Taipei Medical University, Taipei 100-116, Taiwan
| | - Mohy Uddin
- Research Quality Management Section, King Abdullah International Medical Research Center, King Saud bin Abdulaziz University for Health Sciences, Ministry of National Guard-Health Affairs, Riyadh 11481, Saudi Arabia
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Weidener L, Fischer M. Artificial Intelligence Teaching as Part of Medical Education: Qualitative Analysis of Expert Interviews. JMIR MEDICAL EDUCATION 2023; 9:e46428. [PMID: 36946094 PMCID: PMC10167581 DOI: 10.2196/46428] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Revised: 03/21/2023] [Accepted: 03/21/2023] [Indexed: 05/11/2023]
Abstract
BACKGROUND The use of artificial intelligence (AI) in medicine is expected to increase significantly in the upcoming years. Advancements in AI technology have the potential to revolutionize health care, from aiding in the diagnosis of certain diseases to helping with treatment decisions. Current literature suggests the integration of the subject of AI in medicine as part of the medical curriculum to prepare medical students for the opportunities and challenges related to the use of the technology within the clinical context. OBJECTIVE We aimed to explore the relevant knowledge and understanding of the subject of AI in medicine and specify curricula teaching content within medical education. METHODS For this research, we conducted 12 guideline-based expert interviews. Experts were defined as individuals who have been engaged in full-time academic research, development, or teaching in the field of AI in medicine for at least 5 years. As part of the data analysis, we recorded, transcribed, and analyzed the interviews using qualitative content analysis. We used the software QCAmap and inductive category formation to analyze the data. RESULTS The qualitative content analysis led to the formation of three main categories ("Knowledge," "Interpretation," and "Application") with a total of 9 associated subcategories. The experts interviewed cited knowledge and an understanding of the fundamentals of AI, statistics, ethics, and privacy and regulation as necessary basic knowledge that should be part of medical education. The analysis also showed that medical students need to be able to interpret as well as critically reflect on the results provided by AI, taking into account the associated risks and data basis. To enable the application of AI in medicine, medical education should promote the acquisition of practical skills, including the need for basic technological skills, as well as the development of confidence in the technology and one's related competencies. CONCLUSIONS The analyzed expert interviews' results suggest that medical curricula should include the topic of AI in medicine to develop the knowledge, understanding, and confidence needed to use AI in the clinical context. The results further imply an imminent need for standardization of the definition of AI as the foundation to identify, define, and teach respective content on AI within medical curricula.
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Affiliation(s)
- Lukas Weidener
- Research Unit for Quality and Ethics in Health Care, UMIT TIROL - Private University for Health Sciences and Health Technology, Hall in Tirol, Austria
| | - Michael Fischer
- Research Unit for Quality and Ethics in Health Care, UMIT TIROL - Private University for Health Sciences and Health Technology, Hall in Tirol, Austria
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Catalina QM, Fuster-Casanovas A, Vidal-Alaball J, Escalé-Besa A, Marin-Gomez FX, Femenia J, Solé-Casals J. Knowledge and perception of primary care healthcare professionals on the use of artificial intelligence as a healthcare tool. Digit Health 2023; 9:20552076231180511. [PMID: 37361442 PMCID: PMC10286543 DOI: 10.1177/20552076231180511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 05/19/2023] [Indexed: 06/28/2023] Open
Abstract
Objective The rapid digitisation of healthcare data and the sheer volume being generated means that artificial intelligence (AI) is becoming a new reality in the practice of medicine. For this reason, describing the perception of primary care (PC) healthcare professionals on the use of AI as a healthcare tool and its impact in radiology is crucial to ensure its successful implementation. Methods Observational cross-sectional study, using the validated Shinners Artificial Intelligence Perception survey, aimed at all PC medical and nursing professionals in the health region of Central Catalonia. Results The survey was sent to 1068 health professionals, of whom 301 responded. And 85.7% indicated that they understood the concept of AI but there were discrepancies in the use of this tool; 65.8% indicated that they had not received any AI training and 91.4% that they would like to receive training. The mean score for the professional impact of AI was 3.62 points out of 5 (standard deviation (SD) = 0.72), with a higher score among practitioners who had some prior knowledge of and interest in AI. The mean score for preparedness for AI was 2.76 points out of 5 (SD = 0.70), with higher scores for nursing and those who use or do not know if they use AI. Conclusions The results of this study show that the majority of professionals understood the concept of AI, perceived its impact positively, and felt prepared for its implementation. In addition, despite being limited to a diagnostic aid, the implementation of AI in radiology was a high priority for these professionals.
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Affiliation(s)
- Queralt Miró Catalina
- Unitat de Suport a la Recerca de la Catalunya Central, Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina, Sant Fruitós de Bages, Spain
- Health Promotion in Rural Areas Research Group, Gerència Territorial de la Catalunya Central, Institut Català de la Salut, Sant Fruitós de Bages, Spain
| | - Aïna Fuster-Casanovas
- Unitat de Suport a la Recerca de la Catalunya Central, Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina, Sant Fruitós de Bages, Spain
- Health Promotion in Rural Areas Research Group, Gerència Territorial de la Catalunya Central, Institut Català de la Salut, Sant Fruitós de Bages, Spain
| | - Josep Vidal-Alaball
- Unitat de Suport a la Recerca de la Catalunya Central, Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina, Sant Fruitós de Bages, Spain
- Health Promotion in Rural Areas Research Group, Gerència Territorial de la Catalunya Central, Institut Català de la Salut, Sant Fruitós de Bages, Spain
- Faculty of Medicine, University of Vic-Central University of Catalonia, Vic, Spain
| | - Anna Escalé-Besa
- Unitat de Suport a la Recerca de la Catalunya Central, Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina, Sant Fruitós de Bages, Spain
- Health Promotion in Rural Areas Research Group, Gerència Territorial de la Catalunya Central, Institut Català de la Salut, Sant Fruitós de Bages, Spain
| | - Francesc X Marin-Gomez
- Unitat de Suport a la Recerca de la Catalunya Central, Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina, Sant Fruitós de Bages, Spain
- Health Promotion in Rural Areas Research Group, Gerència Territorial de la Catalunya Central, Institut Català de la Salut, Sant Fruitós de Bages, Spain
| | - Joaquim Femenia
- Faculty of Medicine, University of Vic-Central University of Catalonia, Vic, Spain
| | - Jordi Solé-Casals
- Data and Signal Processing group, Faculty of Science, Technology and Engineering, University of Vic-Central University of Catalonia, Vic, Spain
- Department of Psychiatry, University of Cambridge, Cambridge, UK
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