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Wilkinson LS, Dunbar JK, Lip G. Clinical Integration of Artificial Intelligence for Breast Imaging. Radiol Clin North Am 2024; 62:703-716. [PMID: 38777544 DOI: 10.1016/j.rcl.2023.12.006] [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: 05/25/2024]
Abstract
This article describes an approach to planning and implementing artificial intelligence products in a breast screening service. It highlights the importance of an in-depth understanding of the end-to-end workflow and effective project planning by a multidisciplinary team. It discusses the need for monitoring to ensure that performance is stable and meets expectations, as well as focusing on the potential for inadvertantly generating inequality. New cross-discipline roles and expertise will be needed to enhance service delivery.
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Affiliation(s)
- Louise S Wilkinson
- Oxford Breast Imaging Centre, Churchill Hospital, Old Road, Headington, Oxford OX3 7LE, UK.
| | - J Kevin Dunbar
- Regional Head of Screening Quality Assurance Service (SQAS) - South, NHS England, England, UK
| | - Gerald Lip
- North East Scotland Breast Screening Service, Aberdeen Royal Infirmary, Foresterhill Road, Aberdeen AB25 2XF, UK
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Moll M, Heilemann G, Georg D, Kauer-Dorner D, Kuess P. The role of artificial intelligence in informed patient consent for radiotherapy treatments-a case report. Strahlenther Onkol 2024; 200:544-548. [PMID: 38180493 DOI: 10.1007/s00066-023-02190-7] [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: 09/21/2023] [Accepted: 12/03/2023] [Indexed: 01/06/2024]
Abstract
Recent advancements in large language models (LMM; e.g., ChatGPT (OpenAI, San Francisco, California, USA)) have seen widespread use in various fields, including healthcare. This case study reports on the first use of LMM in a pretreatment discussion and in obtaining informed consent for a radiation oncology treatment. Further, the reproducibility of the replies by ChatGPT 3.5 was analyzed. A breast cancer patient, following legal consultation, engaged in a conversation with ChatGPT 3.5 regarding her radiotherapy treatment. The patient posed questions about side effects, prevention, activities, medications, and late effects. While some answers contained inaccuracies, responses closely resembled doctors' replies. In a final evaluation discussion, the patient, however, stated that she preferred the presence of a physician and expressed concerns about the source of the provided information. The reproducibility was tested in ten iterations. Future guidelines for using such models in radiation oncology should be driven by medical professionals. While artificial intelligence (AI) supports essential tasks, human interaction remains crucial.
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Affiliation(s)
- M Moll
- Department of Radiation Oncology, Comprehensive Cancer Center Vienna, Medical University Vienna, Vienna, Austria.
| | - G Heilemann
- Department of Radiation Oncology, Comprehensive Cancer Center Vienna, Medical University Vienna, Vienna, Austria
| | - Dietmar Georg
- Department of Radiation Oncology, Comprehensive Cancer Center Vienna, Medical University Vienna, Vienna, Austria
| | - D Kauer-Dorner
- Department of Radiation Oncology, Comprehensive Cancer Center Vienna, Medical University Vienna, Vienna, Austria
| | - P Kuess
- Department of Radiation Oncology, Comprehensive Cancer Center Vienna, Medical University Vienna, Vienna, Austria
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Wang TW, Hong JS, Chiu HY, Chao HS, Chen YM, Wu YT. Standalone deep learning versus experts for diagnosis lung cancer on chest computed tomography: a systematic review. Eur Radiol 2024:10.1007/s00330-024-10804-6. [PMID: 38777902 DOI: 10.1007/s00330-024-10804-6] [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: 01/14/2024] [Revised: 03/10/2024] [Accepted: 04/01/2024] [Indexed: 05/25/2024]
Abstract
PURPOSE To compare the diagnostic performance of standalone deep learning (DL) algorithms and human experts in lung cancer detection on chest computed tomography (CT) scans. MATERIALS AND METHODS This study searched for studies on PubMed, Embase, and Web of Science from their inception until November 2023. We focused on adult lung cancer patients and compared the efficacy of DL algorithms and expert radiologists in disease diagnosis on CT scans. Quality assessment was performed using QUADAS-2, QUADAS-C, and CLAIM. Bivariate random-effects and subgroup analyses were performed for tasks (malignancy classification vs invasiveness classification), imaging modalities (CT vs low-dose CT [LDCT] vs high-resolution CT), study region, software used, and publication year. RESULTS We included 20 studies on various aspects of lung cancer diagnosis on CT scans. Quantitatively, DL algorithms exhibited superior sensitivity (82%) and specificity (75%) compared to human experts (sensitivity 81%, specificity 69%). However, the difference in specificity was statistically significant, whereas the difference in sensitivity was not statistically significant. The DL algorithms' performance varied across different imaging modalities and tasks, demonstrating the need for tailored optimization of DL algorithms. Notably, DL algorithms matched experts in sensitivity on standard CT, surpassing them in specificity, but showed higher sensitivity with lower specificity on LDCT scans. CONCLUSION DL algorithms demonstrated improved accuracy over human readers in malignancy and invasiveness classification on CT scans. However, their performance varies by imaging modality, underlining the importance of continued research to fully assess DL algorithms' diagnostic effectiveness in lung cancer. CLINICAL RELEVANCE STATEMENT DL algorithms have the potential to refine lung cancer diagnosis on CT, matching human sensitivity and surpassing in specificity. These findings call for further DL optimization across imaging modalities, aiming to advance clinical diagnostics and patient outcomes. KEY POINTS Lung cancer diagnosis by CT is challenging and can be improved with AI integration. DL shows higher accuracy in lung cancer detection on CT than human experts. Enhanced DL accuracy could lead to improved lung cancer diagnosis and outcomes.
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Affiliation(s)
- Ting-Wei Wang
- Institute of Biophotonics, National Yang-Ming Chiao Tung University, Taipei, Taiwan
- School of Medicine, National Yang-Ming Chiao Tung University, Taipei, Taiwan
| | - Jia-Sheng Hong
- Institute of Biophotonics, National Yang-Ming Chiao Tung University, Taipei, Taiwan
| | - Hwa-Yen Chiu
- Institute of Biophotonics, National Yang-Ming Chiao Tung University, Taipei, Taiwan
- School of Medicine, National Yang-Ming Chiao Tung University, Taipei, Taiwan
- Department of Chest Medicine, Taipei Veteran General Hospital, Taipei, Taiwan
| | - Heng-Sheng Chao
- Department of Chest Medicine, Taipei Veteran General Hospital, Taipei, Taiwan
| | - Yuh-Min Chen
- School of Medicine, National Yang-Ming Chiao Tung University, Taipei, Taiwan
- Department of Chest Medicine, Taipei Veteran General Hospital, Taipei, Taiwan
| | - Yu-Te Wu
- Institute of Biophotonics, National Yang-Ming Chiao Tung University, Taipei, Taiwan.
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Qiu J, Mitra J, Ghose S, Dumas C, Yang J, Sarachan B, Judson MA. A Multichannel CT and Radiomics-Guided CNN-ViT (RadCT-CNNViT) Ensemble Network for Diagnosis of Pulmonary Sarcoidosis. Diagnostics (Basel) 2024; 14:1049. [PMID: 38786347 PMCID: PMC11120014 DOI: 10.3390/diagnostics14101049] [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: 04/01/2024] [Revised: 05/10/2024] [Accepted: 05/15/2024] [Indexed: 05/25/2024] Open
Abstract
Pulmonary sarcoidosis is a multisystem granulomatous interstitial lung disease (ILD) with a variable presentation and prognosis. The early accurate detection of pulmonary sarcoidosis may prevent progression to pulmonary fibrosis, a serious and potentially life-threatening form of the disease. However, the lack of a gold-standard diagnostic test and specific radiographic findings poses challenges in diagnosing pulmonary sarcoidosis. Chest computed tomography (CT) imaging is commonly used but requires expert, chest-trained radiologists to differentiate pulmonary sarcoidosis from lung malignancies, infections, and other ILDs. In this work, we develop a multichannel, CT and radiomics-guided ensemble network (RadCT-CNNViT) with visual explainability for pulmonary sarcoidosis vs. lung cancer (LCa) classification using chest CT images. We leverage CT and hand-crafted radiomics features as input channels, and a 3D convolutional neural network (CNN) and vision transformer (ViT) ensemble network for feature extraction and fusion before a classification head. The 3D CNN sub-network captures the localized spatial information of lesions, while the ViT sub-network captures long-range, global dependencies between features. Through multichannel input and feature fusion, our model achieves the highest performance with accuracy, sensitivity, specificity, precision, F1-score, and combined AUC of 0.93 ± 0.04, 0.94 ± 0.04, 0.93 ± 0.08, 0.95 ± 0.05, 0.94 ± 0.04, and 0.97, respectively, in a five-fold cross-validation study with pulmonary sarcoidosis (n = 126) and LCa (n = 93) cases. A detailed ablation study showing the impact of CNN + ViT compared to CNN or ViT alone, and CT + radiomics input, compared to CT or radiomics alone, is also presented in this work. Overall, the AI model developed in this work offers promising potential for triaging the pulmonary sarcoidosis patients for timely diagnosis and treatment from chest CT.
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Affiliation(s)
- Jianwei Qiu
- GE HealthCare, Niskayuna, NY 12309, USA; (J.Q.); (S.G.); (B.S.)
| | - Jhimli Mitra
- GE HealthCare, Niskayuna, NY 12309, USA; (J.Q.); (S.G.); (B.S.)
| | - Soumya Ghose
- GE HealthCare, Niskayuna, NY 12309, USA; (J.Q.); (S.G.); (B.S.)
| | - Camille Dumas
- Department of Medical Imaging, Albany Medical College, Albany, NY 12208, USA; (C.D.); (J.Y.)
| | - Jun Yang
- Department of Medical Imaging, Albany Medical College, Albany, NY 12208, USA; (C.D.); (J.Y.)
| | - Brion Sarachan
- GE HealthCare, Niskayuna, NY 12309, USA; (J.Q.); (S.G.); (B.S.)
| | - Marc A. Judson
- Department of Medicine, Albany Medical College, Albany, NY 12208, USA;
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Spirnak JR, Antani S. The Need for Artificial Intelligence Curriculum in Military Medical Education. Mil Med 2024; 189:954-958. [PMID: 37864817 DOI: 10.1093/milmed/usad412] [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: 09/04/2023] [Revised: 09/25/2023] [Accepted: 10/04/2023] [Indexed: 10/23/2023] Open
Abstract
The success of deep-learning algorithms in analyzing complex structured and unstructured multidimensional data has caused an exponential increase in the amount of research devoted to the applications of artificial intelligence (AI) in medicine in the past decade. Public release of large language models like ChatGPT the past year has generated an unprecedented storm of excitement and rumors of machine intelligence finally reaching or even surpassing human capability in detecting meaningful signals in complex multivariate data. Such enthusiasm, however, is met with an equal degree of both skepticism and fear over the social, legal, and moral implications of such powerful technology with relatively little safeguards or regulations on its development. The question remains in medicine of how to harness the power of AI to improve patient outcomes by increasing the diagnostic accuracy and treatment precision provided by medical professionals. Military medicine, given its unique mission and resource constraints,can benefit immensely from such technology. However, reaping such benefits hinges on the ability of the rising generations of military medical professionals to understand AI algorithms and their applications. Additionally, they should strongly consider working with them as an adjunct decision-maker and view them as a colleague to access and harness relevant information as opposed to something to be feared. Ideas expressed in this commentary were formulated by a military medical student during a two-month research elective working on a multidisciplinary team of computer scientists and clinicians at the National Library of Medicine advancing the state of the art of AI in medicine. A motivation to incorporate AI in the Military Health System is provided, including examples of applications in military medicine. Rationale is then given for inclusion of AI in education starting in medical school as well as a prudent implementation of these algorithms in a clinical workflow during graduate medical education. Finally, barriers to implementation are addressed along with potential solutions. The end state is not that rising military physicians are technical experts in AI; but rather that they understand how they can leverage its rapidly evolving capabilities to prepare for a future where AI will have a significant role in clinical care. The overall goal is to develop trained clinicians that can leverage these technologies to improve the Military Health System.
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Affiliation(s)
- Jonathan R Spirnak
- Uniformed Services University of the Health Sciences (USUHS) School of Medicine, Bethesda, MD 20814, USA
| | - Sameer Antani
- Computational Health Research Branch, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
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Stueckle CA, Haage P. The radiologist as a physician - artificial intelligence as a way to overcome tension between the patient, technology, and referring physicians - a narrative review. ROFO-FORTSCHR RONTG 2024. [PMID: 38569517 DOI: 10.1055/a-2271-0799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2024]
Abstract
BACKGROUND Large volumes of data increasing over time lead to a shortage of radiologists' time. The use of systems based on artificial intelligence (AI) offers opportunities to relieve the burden on radiologists. The AI systems are usually optimized for a radiological area. Radiologists must understand the basic features of its technical function in order to be able to assess the weaknesses and possible errors of the system and use the strengths of the system. This "explainability" creates trust in an AI system and shows its limits. METHOD Based on an expanded Medline search for the key words "radiology, artificial intelligence, referring physician interaction, patient interaction, job satisfaction, communication of findings, expectations", subjective additional relevant articles were considered for this narrative review. RESULTS The use of AI is well advanced, especially in radiology. The programmer should provide the radiologist with clear explanations as to how the system works. All systems on the market have strengths and weaknesses. Some of the optimizations are unintentionally specific, as they are often adapted too precisely to a certain environment that often does not exist in practice - this is known as "overfitting". It should also be noted that there are specific weak points in the systems, so-called "adversarial examples", which lead to fatal misdiagnoses by the AI even though these cannot be visually distinguished from an unremarkable finding by the radiologist. The user must know which diseases the system is trained for, which organ systems are recognized and taken into account by the AI, and, accordingly, which are not properly assessed. This means that the user can and must critically review the results and adjust the findings if necessary. Correctly applied AI can result in a time savings for the radiologist. If he knows how the system works, he only has to spend a short amount of time checking the results. The time saved can be used for communication with patients and referring physicians and thus contribute to higher job satisfaction. CONCLUSION Radiology is a constantly evolving specialty with enormous responsibility, as radiologists often make the diagnosis to be treated. AI-supported systems should be used consistently to provide relief and support. Radiologists need to know the strengths, weaknesses, and areas of application of these AI systems in order to save time. The time gained can be used for communication with patients and referring physicians. KEY POINTS · Explainable AI systems help to improve workflow and to save time.. · The physician must critically review AI results, under consideration of the limitations of the AI.. · The AI system will only provide useful results if it has been adapted to the data type and data origin.. · The communicating radiologist interested in the patient is important for the visibility of the discipline.. CITATION FORMAT · Stueckle CA, Haage P. The radiologist as a physician - artificial intelligence as a way to overcome tension between the patient, technology, and referring physicians - a narrative review. Fortschr Röntgenstr 2024; DOI: 10.1055/a-2271-0799.
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Affiliation(s)
| | - Patrick Haage
- Diagnostic and Interventional Radiology, HELIOS Universitätsklinikum Wuppertal, Germany
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Granata V, Fusco R, Coluccino S, Russo C, Grassi F, Tortora F, Conforti R, Caranci F. Preliminary data on artificial intelligence tool in magnetic resonance imaging assessment of degenerative pathologies of lumbar spine. LA RADIOLOGIA MEDICA 2024; 129:623-630. [PMID: 38349415 DOI: 10.1007/s11547-024-01791-1] [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: 12/02/2023] [Accepted: 01/15/2024] [Indexed: 04/17/2024]
Abstract
PURPOSE To evaluate the ability of an artificial intelligence (AI) tool in magnetic resonance imaging (MRI) assessment of degenerative pathologies of lumbar spine using radiologist evaluation as a gold standard. METHODS Patients with degenerative pathologies of lumbar spine, evaluated with MRI study, were enrolled in a retrospective study approved by local ethical committee. A comprehensive software solution (CoLumbo; SmartSoft Ltd., Varna, Bulgaria) designed to label the segments of the lumbar spine and to detect a broad spectrum of degenerative pathologies based on a convolutional neural network (CNN) was employed, utilizing an automatic segmentation. The AI tool efficacy was compared to data obtained by a senior neuroradiologist that employed a semiquantitative score. Chi-square test was used to assess the differences among groups, and Spearman's rank correlation coefficient was calculated between the grading assigned by radiologist and the grading obtained by software. Moreover, agreement was assessed between the value assigned by radiologist and software. RESULTS Ninety patients (58 men; 32 women) affected with degenerative pathologies of lumbar spine and aged from 60 to 81 years (mean 66 years) were analyzed. Significant correlations were observed between grading assigned by radiologist and the grading obtained by software for each localization. However, only when the localization was L2-L3, there was a good correlation with a coefficient value of 0.72. The best agreements were obtained in case of L1-L2 and L2-L3 localizations and were, respectively, of 81.1% and 72.2%. The lowest agreement of 51.1% was detected in case of L4-L5 locations. With regard canal stenosis and compression, the highest agreement was obtained for identification of in L5-S1 localization. CONCLUSIONS AI solution represents an efficacy and useful toll degenerative pathologies of lumbar spine to improve radiologist workflow.
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Affiliation(s)
- Vincenza Granata
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Naples, Italy
| | | | - Simone Coluccino
- Division of Radiology, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy
| | - Carmela Russo
- Unit of Neuroradiology, Department of Neurosciences, Santobono-Pausilipon Children's Hospital, Naples, Italy
| | - Francesca Grassi
- Division of Radiology, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy
| | - Fabio Tortora
- Neuroradiology Unit, Department of Advanced Biomedical Sciences, University "Federico II", Naples, Italy
| | - Renata Conforti
- Division of Radiology, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy
| | - Ferdinando Caranci
- Division of Radiology, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy
- Unit of Neuroradiology, Department of Neurosciences, Santobono-Pausilipon Children's Hospital, Naples, Italy
- Neuroradiology Unit, Department of Advanced Biomedical Sciences, University "Federico II", Naples, Italy
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, 20122, Milan, Italy
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Tyndall DA, Price JB, Gaalaas L, Spin-Neto R. Surveying the landscape of diagnostic imaging in dentistry's future: Four emerging technologies with promise. J Am Dent Assoc 2024:S0002-8177(24)00056-4. [PMID: 38520421 DOI: 10.1016/j.adaj.2024.01.005] [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: 05/24/2023] [Revised: 01/04/2024] [Accepted: 01/07/2024] [Indexed: 03/25/2024]
Abstract
BACKGROUND Advances in digital radiography for both intraoral and panoramic imaging and cone-beam computed tomography have led the way to an increase in diagnostic capabilities for the dental care profession. In this article, the authors provide information on 4 emerging technologies with promise. TYPES OF STUDIES REVIEWED The authors feature the following: artificial intelligence in the form of deep learning using convolutional neural networks, dental magnetic resonance imaging, stationary intraoral tomosynthesis, and second-generation cone-beam computed tomography sources based on carbon nanotube technology and multispectral imaging. The authors review and summarize articles featuring these technologies. RESULTS The history and background of these emerging technologies are previewed along with their development and potential impact on the practice of dental diagnostic imaging. The authors conclude that these emerging technologies have the potential to have a substantial influence on the practice of dentistry as these systems mature. The degree of influence most likely will vary, with artificial intelligence being the most influential of the 4. CONCLUSIONS AND PRACTICAL IMPLICATIONS The readers are informed about these emerging technologies and the potential effects on their practice going forward, giving them information on which to base decisions on adopting 1 or more of these technologies. The 4 technologies reviewed in this article have the potential to improve imaging diagnostics in dentistry thereby leading to better patient care and heightened professional satisfaction.
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Bharadwaj P, Nicola L, Breau-Brunel M, Sensini F, Tanova-Yotova N, Atanasov P, Lobig F, Blankenburg M. Unlocking the Value: Quantifying the Return on Investment of Hospital Artificial Intelligence. J Am Coll Radiol 2024:S1546-1440(24)00292-8. [PMID: 38499053 DOI: 10.1016/j.jacr.2024.02.034] [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: 01/08/2024] [Revised: 02/23/2024] [Accepted: 02/28/2024] [Indexed: 03/20/2024]
Abstract
PURPOSE A comprehensive return on investment (ROI) calculator was developed to evaluate the monetary and nonmonetary benefits of an artificial intelligence (AI)-powered radiology diagnostic imaging platform to inform decision makers interested in adopting AI. METHODS A calculator was constructed to calculate comparative costs, estimated revenues, and quantify the clinical value of using an AI platform compared with no use of AI in radiology workflows of a US hospital over a 5-year time horizon. Parameters were determined on the basis of expert interviews and a literature review. Scenario and deterministic sensitivity analyses were conducted to evaluate calculator drivers. RESULTS In the calculator, the introduction of an AI platform into the hospital radiology workflow resulted in labor time reductions and delivery of an ROI of 451% over a 5-year period. The ROI was increased to 791% when radiologist time savings were considered. Time savings for radiologists included more than 15 8-hour working days of waiting time, 78 days in triage time, 10 days in reading time, and 41 days in reporting time. Using the platform also provided revenue benefits for the hospital in bringing in patients for clinically beneficial follow-up scans, hospitalizations, and treatment procedures. Results were sensitive to the time horizon, health center setting, and number of scans performed. Among those, the most influential outcome was the number of additional necessary treatments performed because of AI identification of patients. CONCLUSIONS The authors demonstrate a substantial 5-year ROI of implementing an AI platform in a stroke management-accredited hospital. The ROI calculator may be useful for decision makers evaluating AI-powered radiology platforms.
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Affiliation(s)
| | - Lauren Nicola
- CEO/Partner, Triad Radiology Associates; Chair, Ultrasound Commission, ACR; Chair, Reimbursement Committee, ACR
| | | | | | | | - Petar Atanasov
- Principal Consultant, Amaris Consulting, London, United Kingdom
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Neitzel E, Grewal S, Kaur M, Sitton Z, Kang P, van Sonnenberg E. Academic radiology department subspeciality organization & fellowship offerings: A hodgepodge. Curr Probl Diagn Radiol 2024:S0363-0188(24)00055-0. [PMID: 38503598 DOI: 10.1067/j.cpradiol.2024.03.010] [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: 02/15/2024] [Accepted: 03/07/2024] [Indexed: 03/21/2024]
Abstract
INTRODUCTION As opportunities for radiologists to subspecialize have increased, many avenues to organize Radiology department subspecialties exist. This study seeks to determine how academic U.S. Radiology departments structure themselves with respect to subspecialty divisions/sections, as there are no current standards for how Radiology departments are subdivided. Additionally, the extent of Radiology fellowships offered are assessed. The websites of academic U.S. Radiology departments, a highly influential source of information, were analyzed to perform this study. MATERIALS & METHODS Radiology department websites of all allopathic U.S. medical schools (n = 148) were assessed for the following: presence/absence of Radiology department subdivisions, division/section labels, number of divisions/sections, division/section titles, presence/absence of Radiology fellowships, number of fellowships, and fellowships titles. RESULTS 114/148 (77 %) medical schools had Radiology department websites. According to their respective websites, 66/114 (58 %) academic Radiology departments had subspecialty divisions/sections, whereas 48/114 (42 %) had no divisions/sections listed. Of the departments that had divisions/sections, the median number of divisions/sections per department was nine, and ranged from two to 14. Fellowships were offered at 82/114 (72 %) academic Radiology departments that had websites, and the median number was six, ranging from one to 13. CONCLUSION There is marked heterogeneity of departmental organization across Radiology departments nationwide, likely due to the lack of current standards for how Radiology departments are subdivided into divisions/sections. Of the 77 % of medical schools that have Radiology department websites, only 58 % of departments listed divisions/sections, and 72 % posted fellowship offerings.
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Affiliation(s)
- Easton Neitzel
- University of Arizona College of Medicine - Phoenix, Phoenix, AZ, USA.
| | - Shivraj Grewal
- University of Arizona College of Medicine - Phoenix, Phoenix, AZ, USA
| | - Manroop Kaur
- University of Arizona College of Medicine - Phoenix, Phoenix, AZ, USA
| | - Zachary Sitton
- University of Arizona College of Medicine - Phoenix, Phoenix, AZ, USA
| | - Paul Kang
- University of Arizona College of Medicine - Phoenix, Phoenix, AZ, USA
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Trofimova AV, Li Y, Mazaheri P, Krishnan A, Payabvash S, Kappelhof M, Gadde JA. Young Professionals in Neuroradiology: Opportunities, Challenges, and Future Directions. AJNR Am J Neuroradiol 2024; 45:256-261. [PMID: 38388685 DOI: 10.3174/ajnr.a8147] [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: 07/21/2023] [Accepted: 09/29/2023] [Indexed: 02/24/2024]
Abstract
The Young Professionals Committee of the American Society of Neuroradiology identifies and serves the interests of young professionals in neuroradiology, defined as those still in training or within 5 years of its completion. Being a young professional is an exciting, dynamic, and demanding stage of one's professional career. As the landscape of neuroradiology practice changes, new opportunities and challenges occur for those in the early stage of their career. It is important to recognize and support the needs of young professionals because an investment in their professional development is an investment in the future of our specialty. In this article, we aimed to address the most notable developments relevant to current and future young professionals in neuroradiology as well as highlight the work done by the Young Professionals Committee of the American Society of Neuroradiology in serving the needs of these young professionals, focusing on early neuroradiology engagement, flexible work arrangements, private practice, social media, artificial intelligence, and international collaborations.
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Affiliation(s)
- Anna V Trofimova
- From the Children's Healthcare of Atlanta (A.V.T.), Emory University School of Medicine, Atlanta, Georgia
| | - Yi Li
- University of California (Y.L.), University of California, San Francisco, California
| | - Parisa Mazaheri
- Mallinckrodt Institute of Radiology (P.M.), Washington University School of Medicine, St. Louis, Missouri
| | - Arun Krishnan
- Northside Radiology Associates (A.K.), Atlanta, Georgia
| | | | - Manon Kappelhof
- Amsterdam University Medical Center (M.K.), University of Amsterdam, Amsterdam, the Netherlands
| | - Judith A Gadde
- Ann & Robert H. Lurie Children's Hospital of Chicago (J.A.G.), Northwestern University Feinberg School of Medicine, Chicago, Illinois
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Warren BE, Bilbily A, Gichoya JW, Conway A, Li B, Fawzy A, Barragán C, Jaberi A, Mafeld S. An Introductory Guide to Artificial Intelligence in Interventional Radiology: Part 1 Foundational Knowledge. Can Assoc Radiol J 2024:8465371241236376. [PMID: 38445497 DOI: 10.1177/08465371241236376] [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: 03/07/2024] Open
Abstract
Artificial intelligence (AI) is rapidly evolving and has transformative potential for interventional radiology (IR) clinical practice. However, formal training in AI may be limited for many clinicians and therefore presents a challenge for initial implementation and trust in AI. An understanding of the foundational concepts in AI may help familiarize the interventional radiologist with the field of AI, thus facilitating understanding and participation in the development and deployment of AI. A pragmatic classification system of AI based on the complexity of the model may guide clinicians in the assessment of AI. Finally, the current state of AI in IR and the patterns of implementation are explored (pre-procedural, intra-procedural, and post-procedural).
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Affiliation(s)
- Blair Edward Warren
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
- Joint Department of Medical Imaging, University Health Network, Toronto, ON, Canada
| | - Alexander Bilbily
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
- 16 Bit Inc., Toronto, ON, Canada
- Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | | | - Aaron Conway
- Prince Charles Hospital, Queensland University of Technology, Brisbane, QLD, Australia
| | - Ben Li
- Division of Vascular Surgery, Department of Surgery, University of Toronto, Toronto, ON, Canada
| | - Aly Fawzy
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
| | - Camilo Barragán
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
- Joint Department of Medical Imaging, University Health Network, Toronto, ON, Canada
| | - Arash Jaberi
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
- Joint Department of Medical Imaging, University Health Network, Toronto, ON, Canada
| | - Sebastian Mafeld
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
- Joint Department of Medical Imaging, University Health Network, Toronto, ON, Canada
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Neitzel E, vanSonnenberg E, Lynch K, Irwin C, Shah-Patel L, Mamlouk MD. Why Medical Students Pursue Radiology: A Current Longitudinal Survey on Motivations and Controversial Issues in Radiology. Acad Radiol 2024; 31:736-744. [PMID: 37852816 DOI: 10.1016/j.acra.2023.09.025] [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: 06/23/2023] [Revised: 09/11/2023] [Accepted: 09/16/2023] [Indexed: 10/20/2023]
Abstract
RATIONALE AND OBJECTIVES Radiology is an increasingly competitive specialty. Various current factors influence medical students' decision to pursue a radiology career, including artificial intelligence (AI), remote reading, and COVID-19. This study seeks to determine the decision-making factors of all alumni from our medical school who matched into a radiology residency, and to gather opinions on emerging radiology topics. MATERIALS AND METHODS A survey querying decision-making factors and opinions on current radiology topics was distributed to all alumni from our medical school (first graduating class in 2011) who previously matched into a diagnostic or interventional radiology residency program (n = 57). Wilcoxon Rank-Sum and Fisher's Exact tests were used to determine statistical significance. RESULTS Forty-three of fifty-seven responses were received (75% response rate). The most influential factor that sparked respondents' interest in radiology was a radiology elective (25/43, 58%). Students who will finish radiology training in 2023 or later were more likely to be influenced by a mentor (15/23, 65%) than those who finished radiology training before 2023 (5/20, 25%) (p = 0.04). Respondents reported a 1.6/5 concern about AI negatively impacting their future career in radiology. There was 1.7/5 concern about performing radiology procedures on patients during the COVID-19 pandemic. Respondents predicted that remote reading would have a 3.2/5 positive impact on helping them achieve their preferred lifestyle. Job satisfaction among attending radiologists is rated at 4.3/5. CONCLUSION Radiology electives had the greatest influence in piquing students' interest in radiology, while mentorship is assuming increasing influence. AI is perceived as a relatively minimal threat to negatively impact radiologists' jobs. Respondents had little concern about performing radiology procedures during the COVID-19 pandemic. Remote reading is viewed as having a moderately positive impact on lifestyle. Responding radiologists enjoy notably high job satisfaction.
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Affiliation(s)
- Easton Neitzel
- University of Arizona College of Medicine-Phoenix, HSEB C536, 475 N 5th St, Phoenix, AZ 85004 (E.N., E.v., K.L., C.I., L.S.-P.).
| | - Eric vanSonnenberg
- University of Arizona College of Medicine-Phoenix, HSEB C536, 475 N 5th St, Phoenix, AZ 85004 (E.N., E.v., K.L., C.I., L.S.-P.); Departments of Radiology & Student Affairs, University of Arizona College of Medicine - Phoenix, Phoenix, AZ (M.M., E.v., L.S.-P.)
| | - Kelly Lynch
- University of Arizona College of Medicine-Phoenix, HSEB C536, 475 N 5th St, Phoenix, AZ 85004 (E.N., E.v., K.L., C.I., L.S.-P.)
| | - Chase Irwin
- University of Arizona College of Medicine-Phoenix, HSEB C536, 475 N 5th St, Phoenix, AZ 85004 (E.N., E.v., K.L., C.I., L.S.-P.)
| | - Lisa Shah-Patel
- University of Arizona College of Medicine-Phoenix, HSEB C536, 475 N 5th St, Phoenix, AZ 85004 (E.N., E.v., K.L., C.I., L.S.-P.); Departments of Radiology & Student Affairs, University of Arizona College of Medicine - Phoenix, Phoenix, AZ (M.M., E.v., L.S.-P.)
| | - Mark D Mamlouk
- Department of Radiology, The Permanente Medical Group, Kaiser Permanente Medical Center, Santa Clara, California (M.M.); Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California (M.M.)
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14
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Shen X, He Z, Shi Y, Liu T, Yang Y, Luo J, Tang X, Chen B, Xu S, Zhou Y, Xiao J, Qin Y. Development and Validation of an Automated Classification System for Osteonecrosis of the Femoral Head Using Deep Learning Approach: A Multicenter Study. J Arthroplasty 2024; 39:379-386.e2. [PMID: 37572719 DOI: 10.1016/j.arth.2023.08.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 08/01/2023] [Accepted: 08/03/2023] [Indexed: 08/14/2023] Open
Abstract
BACKGROUND Accurate classification can facilitate the selection of appropriate interventions to delay the progression of osteonecrosis of the femoral head (ONFH). This study aimed to perform the classification of ONFH through a deep learning approach. METHODS We retrospectively sampled 1,806 midcoronal magnetic resonance images (MRIs) of 1,337 hips from 4 institutions. Of these, 1,472 midcoronal MRIs of 1,155 hips were divided into training, validation, and test datasets with a ratio of 7:1:2 to develop a convolutional neural network model (CNN). An additional 334 midcoronal MRIs of 182 hips were used to perform external validation. The predictive performance of the CNN and the review panel was also compared. RESULTS A multiclass CNN model was successfully developed. In internal validation, the overall accuracy of the CNN for predicting the severity of ONFH based on the Japanese Investigation Committee classification was 87.8%. The macroaverage values of area under the curve (AUC), precision, recall, and F-value were 0.90, 84.8, 84.8, and 84.6%, respectively. In external validation, the overall accuracy of the CNN was 83.8%. The macroaverage values of area under the curve, precision, recall, and F-value were 0.87, 79.5, 80.5, and 79.9%, respectively. In a human-machine comparison study, the CNN outperformed or was comparable to that of the deputy chief orthopaedic surgeons. CONCLUSION The CNN is feasible and robust for classifying ONFH and correctly locating the necrotic area. These findings suggest that classifying ONFH using deep learning with high accuracy and generalizability may aid in predicting femoral head collapse and clinical decision-making.
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Affiliation(s)
- Xianyue Shen
- Department of Orthopedics, The Second Hospital of Jilin University, Changchun, Jilin province, PR China
| | - Ziling He
- College of Computer Science and Technology, Jilin University, Changchun, Jilin province, PR China
| | - Yi Shi
- Department of Orthopedics, The Second Hospital of Anhui Medical University, Hefei, Anhui province, PR China
| | - Tong Liu
- Department of Orthopedics, China-Japan Union Hospital of Jilin University, Changchun, Jilin province, PR China
| | - Yuhui Yang
- Department of Orthopedics, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong province, PR China
| | - Jia Luo
- College of Computer Science and Technology, Jilin University, Changchun, Jilin province, PR China
| | - Xiongfeng Tang
- Department of Orthopedics, The Second Hospital of Jilin University, Changchun, Jilin province, PR China
| | - Bo Chen
- Department of Orthopedics, The Second Hospital of Jilin University, Changchun, Jilin province, PR China
| | - Shenghao Xu
- Department of Orthopedics, China-Japan Union Hospital of Jilin University, Changchun, Jilin province, PR China
| | - You Zhou
- College of Software, Jilin University, Changchun, Jilin province, PR China
| | - Jianlin Xiao
- Department of Orthopedics, China-Japan Union Hospital of Jilin University, Changchun, Jilin province, PR China
| | - Yanguo Qin
- Department of Orthopedics, The Second Hospital of Jilin University, Changchun, Jilin province, PR China
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Hasselgren C, Oprea TI. Artificial Intelligence for Drug Discovery: Are We There Yet? Annu Rev Pharmacol Toxicol 2024; 64:527-550. [PMID: 37738505 DOI: 10.1146/annurev-pharmtox-040323-040828] [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: 09/24/2023]
Abstract
Drug discovery is adapting to novel technologies such as data science, informatics, and artificial intelligence (AI) to accelerate effective treatment development while reducing costs and animal experiments. AI is transforming drug discovery, as indicated by increasing interest from investors, industrial and academic scientists, and legislators. Successful drug discovery requires optimizing properties related to pharmacodynamics, pharmacokinetics, and clinical outcomes. This review discusses the use of AI in the three pillars of drug discovery: diseases, targets, and therapeutic modalities, with a focus on small-molecule drugs. AI technologies, such as generative chemistry, machine learning, and multiproperty optimization, have enabled several compounds to enter clinical trials. The scientific community must carefully vet known information to address the reproducibility crisis. The full potential of AI in drug discovery can only be realized with sufficient ground truth and appropriate human intervention at later pipeline stages.
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Affiliation(s)
- Catrin Hasselgren
- Safety Assessment, Genentech, Inc., South San Francisco, California, USA
| | - Tudor I Oprea
- Expert Systems Inc., San Diego, California, USA;
- Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, USA
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16
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Gefter WB, Prokop M, Seo JB, Raoof S, Langlotz CP, Hatabu H. Human-AI Symbiosis: A Path Forward to Improve Chest Radiography and the Role of Radiologists in Patient Care. Radiology 2024; 310:e232778. [PMID: 38259206 PMCID: PMC10831473 DOI: 10.1148/radiol.232778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 12/08/2023] [Accepted: 12/18/2023] [Indexed: 01/24/2024]
Affiliation(s)
- Warren B. Gefter
- From the Department of Radiology, Penn Medicine, University of Pennsylvania, Philadelphia, Pa (W.B.G.); Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, the Netherlands (M.P.); Department of Radiology, Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea (J.B.S.); Department of Medicine and Radiology, Zucker School of Medicine, Hofstra/Northwell and Lung Institute, Lenox Hill Hospital, New York, NY (S.R.); Department of Radiology and Biomedical Informatics and Center for Artificial Intelligence in Medicine and Imaging, Stanford University, Palo Alto, Calif (C.P.L.); and Center for Pulmonary Functional Imaging, Department of Radiology, Brigham and Women’s Hospital and Harvard Medical School, 75 Francis St, Boston, MA 02215 (H.H.)
| | - Mathias Prokop
- From the Department of Radiology, Penn Medicine, University of Pennsylvania, Philadelphia, Pa (W.B.G.); Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, the Netherlands (M.P.); Department of Radiology, Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea (J.B.S.); Department of Medicine and Radiology, Zucker School of Medicine, Hofstra/Northwell and Lung Institute, Lenox Hill Hospital, New York, NY (S.R.); Department of Radiology and Biomedical Informatics and Center for Artificial Intelligence in Medicine and Imaging, Stanford University, Palo Alto, Calif (C.P.L.); and Center for Pulmonary Functional Imaging, Department of Radiology, Brigham and Women’s Hospital and Harvard Medical School, 75 Francis St, Boston, MA 02215 (H.H.)
| | - Joon Beom Seo
- From the Department of Radiology, Penn Medicine, University of Pennsylvania, Philadelphia, Pa (W.B.G.); Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, the Netherlands (M.P.); Department of Radiology, Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea (J.B.S.); Department of Medicine and Radiology, Zucker School of Medicine, Hofstra/Northwell and Lung Institute, Lenox Hill Hospital, New York, NY (S.R.); Department of Radiology and Biomedical Informatics and Center for Artificial Intelligence in Medicine and Imaging, Stanford University, Palo Alto, Calif (C.P.L.); and Center for Pulmonary Functional Imaging, Department of Radiology, Brigham and Women’s Hospital and Harvard Medical School, 75 Francis St, Boston, MA 02215 (H.H.)
| | - Suhail Raoof
- From the Department of Radiology, Penn Medicine, University of Pennsylvania, Philadelphia, Pa (W.B.G.); Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, the Netherlands (M.P.); Department of Radiology, Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea (J.B.S.); Department of Medicine and Radiology, Zucker School of Medicine, Hofstra/Northwell and Lung Institute, Lenox Hill Hospital, New York, NY (S.R.); Department of Radiology and Biomedical Informatics and Center for Artificial Intelligence in Medicine and Imaging, Stanford University, Palo Alto, Calif (C.P.L.); and Center for Pulmonary Functional Imaging, Department of Radiology, Brigham and Women’s Hospital and Harvard Medical School, 75 Francis St, Boston, MA 02215 (H.H.)
| | - Curtis P. Langlotz
- From the Department of Radiology, Penn Medicine, University of Pennsylvania, Philadelphia, Pa (W.B.G.); Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, the Netherlands (M.P.); Department of Radiology, Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea (J.B.S.); Department of Medicine and Radiology, Zucker School of Medicine, Hofstra/Northwell and Lung Institute, Lenox Hill Hospital, New York, NY (S.R.); Department of Radiology and Biomedical Informatics and Center for Artificial Intelligence in Medicine and Imaging, Stanford University, Palo Alto, Calif (C.P.L.); and Center for Pulmonary Functional Imaging, Department of Radiology, Brigham and Women’s Hospital and Harvard Medical School, 75 Francis St, Boston, MA 02215 (H.H.)
| | - Hiroto Hatabu
- From the Department of Radiology, Penn Medicine, University of Pennsylvania, Philadelphia, Pa (W.B.G.); Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, the Netherlands (M.P.); Department of Radiology, Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea (J.B.S.); Department of Medicine and Radiology, Zucker School of Medicine, Hofstra/Northwell and Lung Institute, Lenox Hill Hospital, New York, NY (S.R.); Department of Radiology and Biomedical Informatics and Center for Artificial Intelligence in Medicine and Imaging, Stanford University, Palo Alto, Calif (C.P.L.); and Center for Pulmonary Functional Imaging, Department of Radiology, Brigham and Women’s Hospital and Harvard Medical School, 75 Francis St, Boston, MA 02215 (H.H.)
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Oniani D, Hilsman J, Peng Y, Poropatich RK, Pamplin JC, Legault GL, Wang Y. Adopting and expanding ethical principles for generative artificial intelligence from military to healthcare. NPJ Digit Med 2023; 6:225. [PMID: 38042910 PMCID: PMC10693640 DOI: 10.1038/s41746-023-00965-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 11/15/2023] [Indexed: 12/04/2023] Open
Abstract
In 2020, the U.S. Department of Defense officially disclosed a set of ethical principles to guide the use of Artificial Intelligence (AI) technologies on future battlefields. Despite stark differences, there are core similarities between the military and medical service. Warriors on battlefields often face life-altering circumstances that require quick decision-making. Medical providers experience similar challenges in a rapidly changing healthcare environment, such as in the emergency department or during surgery treating a life-threatening condition. Generative AI, an emerging technology designed to efficiently generate valuable information, holds great promise. As computing power becomes more accessible and the abundance of health data, such as electronic health records, electrocardiograms, and medical images, increases, it is inevitable that healthcare will be revolutionized by this technology. Recently, generative AI has garnered a lot of attention in the medical research community, leading to debates about its application in the healthcare sector, mainly due to concerns about transparency and related issues. Meanwhile, questions around the potential exacerbation of health disparities due to modeling biases have raised notable ethical concerns regarding the use of this technology in healthcare. However, the ethical principles for generative AI in healthcare have been understudied. As a result, there are no clear solutions to address ethical concerns, and decision-makers often neglect to consider the significance of ethical principles before implementing generative AI in clinical practice. In an attempt to address these issues, we explore ethical principles from the military perspective and propose the "GREAT PLEA" ethical principles, namely Governability, Reliability, Equity, Accountability, Traceability, Privacy, Lawfulness, Empathy, and Eutonomy, for generative AI in healthcare. Furthermore, we introduce a framework for adopting and expanding these ethical principles in a practical way that has been useful in the military and can be applied to healthcare for generative AI, based on contrasting their ethical concerns and risks. Ultimately, we aim to proactively address the ethical dilemmas and challenges posed by the integration of generative AI into healthcare practice.
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Affiliation(s)
- David Oniani
- Department of Health Information Management, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jordan Hilsman
- Department of Health Information Management, University of Pittsburgh, Pittsburgh, PA, USA
| | - Yifan Peng
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Ronald K Poropatich
- Division of Pulmonary, Allergy, Critical Care & Sleep Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Center for Military Medicine Research, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jeremy C Pamplin
- Telemedicine & Advanced Technology Research Center, US Army, Fort Detrick, Frederick, MD, USA
| | - Gary L Legault
- Department of Surgery, Uniformed Services University, Bethesda, MD, USA
- Virtual Medical Center, Brooke Army Medical Center, San Antonio, TX, USA
| | - Yanshan Wang
- Department of Health Information Management, University of Pittsburgh, Pittsburgh, PA, USA.
- Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.
- Clinical and Translational Science Institute, University of Pittsburgh, Pittsburgh, PA, USA.
- University of Pittsburgh Medical Center, Pittsburgh, PA, USA.
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18
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Tremblay R. Using data as a clinical tool. THE CANADIAN VETERINARY JOURNAL = LA REVUE VETERINAIRE CANADIENNE 2023; 64:1165-1168. [PMID: 38046422 PMCID: PMC10637714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Affiliation(s)
- Robert Tremblay
- Dr. Tremblay, DVM, DVSc, Dip ACVIM (LAM), spent 40 years in professional life working in large animal veterinary medicine. For most of that time, his primary interest was food animal medicine
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19
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Judson MA, Qiu J, Dumas CL, Yang J, Sarachan B, Mitra J. An Artificial Intelligence Platform for the Radiologic Diagnosis of Pulmonary Sarcoidosis: An Initial Pilot Study of Chest Computed Tomography Analysis to Distinguish Pulmonary Sarcoidosis from a Negative Lung Cancer Screening Scan. Lung 2023; 201:611-616. [PMID: 37962584 DOI: 10.1007/s00408-023-00655-1] [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: 09/14/2023] [Accepted: 10/31/2023] [Indexed: 11/15/2023]
Abstract
PURPOSE To determine the reliability of an artificial intelligence, deep learning (AI/DL)-based method of chest computer tomography (CT) scan analysis to distinguish pulmonary sarcoidosis from negative lung cancer screening chest CT scans (Lung Imaging Reporting and Data System score 1, Lung-RADS score 1). METHODS Chest CT scans of pulmonary sarcoidosis were evaluated by a clinician experienced with sarcoidosis and a chest radiologist for clinical and radiologic evidence of sarcoidosis and exclusion of alternative or concomitant pulmonary diseases. The AI/DL based method used an ensemble network architecture combining Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). The method was applied to 126 pulmonary sarcoidosis and 96 Lung-RADS score 1 CT scans. The analytic approach of training and validation of the AI/DL method used a fivefold cross-validation technique, where 4/5th of the available data set was used to train a diagnostic model and tested on the remaining 1/5th of the data set, and repeated 4 more times with non-overlapping validation/test data. The probability values were used to generate Receiver Operating Characteristic (ROC) curves to assess the model's discriminatory power. RESULTS The sensitivity, specificity, positive and negative predictive value of the AI/DL method for the 5 folds of the training/validation sets and the entire set of CT scans were all over 94% to distinguish pulmonary sarcoidosis from LUNG-RADS score 1 chest CT scans. The area under the curve for the corresponding ROC curves were all over 97%. CONCLUSION This AL/DL model shows promise to distinguish sarcoidosis from alternative pulmonary conditions using minimal radiologic data.
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Affiliation(s)
- Marc A Judson
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Albany Medical College, MC-91; 16 New Scotland, Albany, NY, 12208, USA.
| | | | - Camille L Dumas
- Cardiovascular Division, Department of Radiology, Albany Medical College, Albany, NY, USA
| | - Jun Yang
- Albany Medical College, Albany, NY, USA
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20
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Abstract
In this Viewpoint a radiologist surveys the ascent of artificial intelligence (AI) in imaging and what the future likely holds for AI in this discipline.
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Affiliation(s)
- Saurabh Jha
- Department of Radiology, University of Pennsylvania, Philadelphia
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21
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Li MD, Little BP. Appropriate Reliance on Artificial Intelligence in Radiology Education. J Am Coll Radiol 2023; 20:1126-1130. [PMID: 37392983 DOI: 10.1016/j.jacr.2023.04.019] [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] [Received: 02/06/2023] [Revised: 03/20/2023] [Accepted: 04/06/2023] [Indexed: 07/03/2023]
Abstract
Users of artificial intelligence (AI) can become overreliant on AI, negatively affecting the performance of human-AI teams. For a future in which radiologists use interpretive AI tools routinely in clinical practice, radiology education will need to evolve to provide radiologists with the skills to use AI appropriately and wisely. In this work, we examine how overreliance on AI may develop in radiology trainees and explore how this problem can be mitigated, including through the use of AI-augmented education. Radiology trainees will still need to develop the perceptual skills and mastery of knowledge fundamental to radiology to use AI safely. We propose a framework for radiology trainees to use AI tools with appropriate reliance, drawing on lessons from human-AI interactions research.
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Affiliation(s)
- Matthew D Li
- Department of Radiology and Diagnostic Imaging, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, Alberta, Canada.
| | - Brent P Little
- Mayo Clinic College of Medicine and Science, Department of Radiology, Division of Cardiothoracic Imaging, Mayo Clinic Florida, Florida; Committee Member, ACR Appropriateness Criteria Thoracic Imaging
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22
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Chu LC, Ahmed T, Blanco A, Javed A, Weisberg EM, Kawamoto S, Hruban RH, Kinzler KW, Vogelstein B, Fishman EK. Radiologists' Expectations of Artificial Intelligence in Pancreatic Cancer Imaging: How Good Is Good Enough? J Comput Assist Tomogr 2023; 47:845-849. [PMID: 37948357 PMCID: PMC10823576 DOI: 10.1097/rct.0000000000001503] [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] [Indexed: 07/29/2023]
Abstract
BACKGROUND Existing (artificial intelligence [AI]) tools in radiology are modeled without necessarily considering the expectations and experience of the end user-the radiologist. The literature is scarce on the tangible parameters that AI capabilities need to meet for radiologists to consider them useful tools. OBJECTIVE The purpose of this study is to explore radiologists' attitudes toward AI tools in pancreatic cancer imaging and to quantitatively assess their expectations of these tools. METHODS A link to the survey was posted on the www.ctisus.com website, advertised in the www.ctisus.com email newsletter, and publicized on LinkedIn, Facebook, and Twitter accounts. This survey asked participants about their demographics, practice, and current attitudes toward AI. They were also asked about their expectations of what constitutes a clinically useful AI tool. The survey consisted of 17 questions, which included 9 multiple choice questions, 2 Likert scale questions, 4 binary (yes/no) questions, 1 rank order question, and 1 free text question. RESULTS A total of 161 respondents completed the survey, yielding a response rate of 46.3% of the total 348 clicks on the survey link. The minimum acceptable sensitivity of an AI program for the detection of pancreatic cancer chosen by most respondents was either 90% or 95% at a specificity of 95%. The minimum size of pancreatic cancer that most respondents would find an AI useful at detecting was 5 mm. Respondents preferred AI tools that demonstrated greater sensitivity over those with greater specificity. Over half of respondents anticipated incorporating AI tools into their clinical practice within the next 5 years. CONCLUSION Radiologists are open to the idea of integrating AI-based tools and have high expectations regarding the performance of these tools. Consideration of radiologists' input is important to contextualize expectations and optimize clinical adoption of existing and future AI tools.
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Affiliation(s)
- Linda C. Chu
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, Baltimore, Maryland
| | - Taha Ahmed
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, Baltimore, Maryland
| | - Alejandra Blanco
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, Baltimore, Maryland
| | - Ammar Javed
- Department of Surgery, New York University Grossman School of Medicine, New York, NY
| | - Edmund M. Weisberg
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, Baltimore, Maryland
| | - Satomi Kawamoto
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, Baltimore, Maryland
| | - Ralph H. Hruban
- Sol Goldman Pancreatic Cancer Research Center, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Kenneth W. Kinzler
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Bert Vogelstein
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Elliot K Fishman
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, Baltimore, Maryland
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Ogilvie T, Kastelic J. In the 21st century, "AI" has a new meaning for veterinarians! THE CANADIAN VETERINARY JOURNAL = LA REVUE VETERINAIRE CANADIENNE 2023; 64:897-898. [PMID: 37780470 PMCID: PMC10506353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 10/03/2023]
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24
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Ahmed MI, Spooner B, Isherwood J, Lane M, Orrock E, Dennison A. A Systematic Review of the Barriers to the Implementation of Artificial Intelligence in Healthcare. Cureus 2023; 15:e46454. [PMID: 37927664 PMCID: PMC10623210 DOI: 10.7759/cureus.46454] [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: 10/03/2023] [Indexed: 11/07/2023] Open
Abstract
Artificial intelligence (AI) is expected to improve healthcare outcomes by facilitating early diagnosis, reducing the medical administrative burden, aiding drug development, personalising medical and oncological management, monitoring healthcare parameters on an individual basis, and allowing clinicians to spend more time with their patients. In the post-pandemic world where there is a drive for efficient delivery of healthcare and manage long waiting times for patients to access care, AI has an important role in supporting clinicians and healthcare systems to streamline the care pathways and provide timely and high-quality care for the patients. Despite AI technologies being used in healthcare for some decades, and all the theoretical potential of AI, the uptake in healthcare has been uneven and slower than anticipated and there remain a number of barriers, both overt and covert, which have limited its incorporation. This literature review highlighted barriers in six key areas: ethical, technological, liability and regulatory, workforce, social, and patient safety barriers. Defining and understanding the barriers preventing the acceptance and implementation of AI in the setting of healthcare will enable clinical staff and healthcare leaders to overcome the identified hurdles and incorporate AI technologies for the benefit of patients and clinical staff.
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Affiliation(s)
- Molla Imaduddin Ahmed
- Paediatric Respiratory Medicine, University Hospitals of Leicester NHS Trust, Leicester, GBR
| | - Brendan Spooner
- Intensive Care and Anaesthesia, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, GBR
| | - John Isherwood
- Hepatobiliary and Pancreatic Surgery, University Hospitals of Leicester NHS Trust, Leicester, GBR
| | - Mark Lane
- Ophthalmology, Birmingham and Midland Eye Centre, Birmingham, GBR
| | - Emma Orrock
- Head of Clinical Senates, East and West Midlands Clinical Senate, Leicester, GBR
| | - Ashley Dennison
- Hepatobiliary and Pancreatic Surgery, University Hospitals of Leicester NHS Trust, Leicester, GBR
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Mello-Thoms C, Mello CAB. Clinical applications of artificial intelligence in radiology. Br J Radiol 2023; 96:20221031. [PMID: 37099398 PMCID: PMC10546456 DOI: 10.1259/bjr.20221031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 03/28/2023] [Accepted: 03/28/2023] [Indexed: 04/27/2023] Open
Abstract
The rapid growth of medical imaging has placed increasing demands on radiologists. In this scenario, artificial intelligence (AI) has become an attractive partner, one that may complement case interpretation and may aid in various non-interpretive aspects of the work in the radiological clinic. In this review, we discuss interpretative and non-interpretative uses of AI in the clinical practice, as well as report on the barriers to AI's adoption in the clinic. We show that AI currently has a modest to moderate penetration in the clinical practice, with many radiologists still being unconvinced of its value and the return on its investment. Moreover, we discuss the radiologists' liabilities regarding the AI decisions, and explain how we currently do not have regulation to guide the implementation of explainable AI or of self-learning algorithms.
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Affiliation(s)
| | - Carlos A B Mello
- Centro de Informática, Universidade Federal de Pernambuco, Recife, Brazil
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26
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Wagner DT, Tilmans L, Peng K, Niedermeier M, Rohl M, Ryan S, Yadav D, Takacs N, Garcia-Fraley K, Koso M, Dikici E, Prevedello LM, Nguyen XV. Artificial Intelligence in Neuroradiology: A Review of Current Topics and Competition Challenges. Diagnostics (Basel) 2023; 13:2670. [PMID: 37627929 PMCID: PMC10453240 DOI: 10.3390/diagnostics13162670] [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: 05/16/2023] [Revised: 08/07/2023] [Accepted: 08/09/2023] [Indexed: 08/27/2023] Open
Abstract
There is an expanding body of literature that describes the application of deep learning and other machine learning and artificial intelligence methods with potential relevance to neuroradiology practice. In this article, we performed a literature review to identify recent developments on the topics of artificial intelligence in neuroradiology, with particular emphasis on large datasets and large-scale algorithm assessments, such as those used in imaging AI competition challenges. Numerous applications relevant to ischemic stroke, intracranial hemorrhage, brain tumors, demyelinating disease, and neurodegenerative/neurocognitive disorders were discussed. The potential applications of these methods to spinal fractures, scoliosis grading, head and neck oncology, and vascular imaging were also reviewed. The AI applications examined perform a variety of tasks, including localization, segmentation, longitudinal monitoring, diagnostic classification, and prognostication. While research on this topic is ongoing, several applications have been cleared for clinical use and have the potential to augment the accuracy or efficiency of neuroradiologists.
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Affiliation(s)
- Daniel T. Wagner
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
| | - Luke Tilmans
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
| | - Kevin Peng
- College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | | | - Matt Rohl
- College of Arts and Sciences, The Ohio State University, Columbus, OH 43210, USA
| | - Sean Ryan
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
| | - Divya Yadav
- College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Noah Takacs
- College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Krystle Garcia-Fraley
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
| | - Mensur Koso
- College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Engin Dikici
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
| | - Luciano M. Prevedello
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
| | - Xuan V. Nguyen
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
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27
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Hussain A, Fareed A, Taseen S. Bone fracture detection-Can artificial intelligence replace doctors in orthopedic radiography analysis? Front Artif Intell 2023; 6:1223909. [PMID: 37593091 PMCID: PMC10427856 DOI: 10.3389/frai.2023.1223909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 07/20/2023] [Indexed: 08/19/2023] Open
Affiliation(s)
- Aariz Hussain
- Karachi Medical and Dental College and Abbasi Shaheed Hospital, Karachi, Sindh, Pakistan
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Perera Molligoda Arachchige AS. Large language models (LLM) and ChatGPT: a medical student perspective. Eur J Nucl Med Mol Imaging 2023; 50:2248-2249. [PMID: 37046082 DOI: 10.1007/s00259-023-06227-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 04/04/2023] [Indexed: 04/14/2023]
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29
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Raslan O, Ozturk A, Oguz KK, Sen F, Aboud O, Ivanovic V, Assadsangabi R, Hacein-Bey L. Imaging Cancer in Neuroradiology. Curr Probl Cancer 2023:100965. [PMID: 37349190 DOI: 10.1016/j.currproblcancer.2023.100965] [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] [Received: 12/27/2022] [Revised: 05/22/2023] [Accepted: 05/25/2023] [Indexed: 06/24/2023]
Abstract
Neuroimaging plays a pivotal role in the diagnosis, management, and prognostication of brain tumors. Recently, the World Health Organization published the fifth edition of the WHO Classification of Tumors of the Central Nervous System (CNS5), which places greater emphasis on tumor genetics and molecular markers to complement the existing histological and immunohistochemical approaches. Recent advances in computational power allowed modern neuro-oncological imaging to move from a strictly morphology-based discipline to advanced neuroimaging techniques with quantifiable tissue characteristics such as tumor cellularity, microstructural organization, hemodynamic, functional, and metabolic features, providing more precise tumor diagnosis and management. The aim of this review is to highlight the key imaging features of the recently published CNS5, outlining the current imaging standards and summarizing the latest advances in neuro-oncological imaging techniques and their role in complementing traditional brain tumor imaging and management.
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Affiliation(s)
- Osama Raslan
- Department of Radiology, Division of Neuroradiology, University of California Davis Medical Center, Sacramento, CA.
| | - Arzu Ozturk
- Department of Radiology, Division of Neuroradiology, University of California Davis Medical Center, Sacramento, CA
| | - Kader Karli Oguz
- Department of Radiology, Division of Neuroradiology, University of California Davis Medical Center, Sacramento, CA
| | - Fatma Sen
- Department of Radiology, Division of Nuclear Medicine, University of California Davis Medical Center, Sacramento, CA
| | - Orwa Aboud
- Department of Neurology and Neurological Surgery, UC Davis Comprehensive Cancer Center, CA
| | - Vladimir Ivanovic
- Department of Radiology, Division of Neuroradiology, Medical College of Wisconsin., Milwaukee, WI
| | - Reza Assadsangabi
- Department of Radiology, Keck School of Medicine of USC University of Southern California, Sacramento, CA
| | - Lotfi Hacein-Bey
- Department of Radiology, Division of Neuroradiology, University of California Davis Medical Center, Sacramento, CA
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Rajpurkar P, Lungren MP. The Current and Future State of AI Interpretation of Medical Images. N Engl J Med 2023; 388:1981-1990. [PMID: 37224199 DOI: 10.1056/nejmra2301725] [Citation(s) in RCA: 72] [Impact Index Per Article: 72.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Affiliation(s)
- Pranav Rajpurkar
- From the Department of Biomedical Informatics, Harvard Medical School, Boston (P.R.); the Center for Artificial Intelligence in Medicine and Imaging, Stanford University, Stanford, and the Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco - both in California (M.P.L.); and Microsoft, Redmond, Washington (M.P.L.)
| | - Matthew P Lungren
- From the Department of Biomedical Informatics, Harvard Medical School, Boston (P.R.); the Center for Artificial Intelligence in Medicine and Imaging, Stanford University, Stanford, and the Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco - both in California (M.P.L.); and Microsoft, Redmond, Washington (M.P.L.)
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Agrawal A. Digital transformation of career landscapes in radiology: personal and professional implications. FRONTIERS IN RADIOLOGY 2023; 3:1180699. [PMID: 37492377 PMCID: PMC10364979 DOI: 10.3389/fradi.2023.1180699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 04/26/2023] [Indexed: 07/27/2023]
Abstract
Millennial radiology is marked by technical disruptions. Advances in internet, digital communications and computing technology, paved way for digitalized workflow orchestration of busy radiology departments. The COVID pandemic brought teleradiology to the forefront, highlighting its importance in maintaining continuity of radiological services, making it an integral component of the radiology practice. Increasing computing power and integrated multimodal data are driving incorporation of artificial intelligence at various stages of the radiology image and reporting cycle. These have and will continue to transform the career landscape in radiology, with more options for radiologists with varied interests and career goals. The ability to work from anywhere and anytime needs to be balanced with other aspects of life. Robust communication, internal and external collaboration, self-discipline, and self-motivation are key to achieving the desired balance while practicing radiology the unconventional way.
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32
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Khosravi P, Schweitzer M. Artificial intelligence in neuroradiology: a scoping review of some ethical challenges. FRONTIERS IN RADIOLOGY 2023; 3:1149461. [PMID: 37492387 PMCID: PMC10365008 DOI: 10.3389/fradi.2023.1149461] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/22/2023] [Accepted: 04/27/2023] [Indexed: 07/27/2023]
Abstract
Artificial intelligence (AI) has great potential to increase accuracy and efficiency in many aspects of neuroradiology. It provides substantial opportunities for insights into brain pathophysiology, developing models to determine treatment decisions, and improving current prognostication as well as diagnostic algorithms. Concurrently, the autonomous use of AI models introduces ethical challenges regarding the scope of informed consent, risks associated with data privacy and protection, potential database biases, as well as responsibility and liability that might potentially arise. In this manuscript, we will first provide a brief overview of AI methods used in neuroradiology and segue into key methodological and ethical challenges. Specifically, we discuss the ethical principles affected by AI approaches to human neuroscience and provisions that might be imposed in this domain to ensure that the benefits of AI frameworks remain in alignment with ethics in research and healthcare in the future.
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Affiliation(s)
- Pegah Khosravi
- Department of Biological Sciences, New York City College of Technology, CUNY, New York City, NY, United States
| | - Mark Schweitzer
- Office of the Vice President for Health Affairs Office of the Vice President, Wayne State University, Detroit, MI, United States
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Masoumian Hosseini M, Masoumian Hosseini ST, Qayumi K, Ahmady S, Koohestani HR. The Aspects of Running Artificial Intelligence in Emergency Care; a Scoping Review. ARCHIVES OF ACADEMIC EMERGENCY MEDICINE 2023; 11:e38. [PMID: 37215232 PMCID: PMC10197918 DOI: 10.22037/aaem.v11i1.1974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Introduction Artificial Inteligence (AI) application in emergency medicine is subject to ethical and legal inconsistencies. The purposes of this study were to map the extent of AI applications in emergency medicine, to identify ethical issues related to the use of AI, and to propose an ethical framework for its use. Methods A comprehensive literature collection was compiled through electronic databases/internet search engines (PubMed, Web of Science Platform, MEDLINE, Scopus, Google Scholar/Academia, and ERIC) and reference lists. We considered studies published between 1 January 2014 and 6 October 2022. Articles that did not self-classify as studies of an AI intervention, those that were not relevant to Emergency Departments (EDs), and articles that did not report outcomes or evaluations were excluded. Descriptive and thematic analyses of data extracted from the included articles were conducted. Results A total of 137 out of the 2175 citations in the original database were eligible for full-text evaluation. Of these articles, 47 were included in the scoping review and considered for theme extraction. This review covers seven main areas of AI techniques in emergency medicine: Machine Learning (ML) Algorithms (10.64%), prehospital emergency management (12.76%), triage, patient acuity and disposition of patients (19.15%), disease and condition prediction (23.40%), emergency department management (17.03%), the future impact of AI on Emergency Medical Services (EMS) (8.51%), and ethical issues (8.51%). Conclusion There has been a rapid increase in AI research in emergency medicine in recent years. Several studies have demonstrated the potential of AI in diverse contexts, particularly when improving patient outcomes through predictive modelling. According to the synthesis of studies in our review, AI-based decision-making lacks transparency. This feature makes AI decision-making opaque.
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Affiliation(s)
| | | | - Karim Qayumi
- Centre of Excellence for Simulation Education and Innovation, Department of Surgery, University of British Columbia, Vancouver, BC, Canada
| | - Soleiman Ahmady
- Department of Medical Education, Virtual School of Medical Education & Management, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hamid Reza Koohestani
- Department of Nursing, Social Determinants of Health Research Center, Saveh University of Medical Sciences, Saveh, Iran
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Alberts IL, Mercolli L, Pyka T, Prenosil G, Shi K, Rominger A, Afshar-Oromieh A. Large language models (LLM) and ChatGPT: what will the impact on nuclear medicine be? Eur J Nucl Med Mol Imaging 2023; 50:1549-1552. [PMID: 36892666 PMCID: PMC9995718 DOI: 10.1007/s00259-023-06172-w] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 02/19/2023] [Indexed: 03/10/2023]
Affiliation(s)
- Ian L Alberts
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstr. 18, 3010, Bern, Switzerland.
| | - Lorenzo Mercolli
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstr. 18, 3010, Bern, Switzerland
| | - Thomas Pyka
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstr. 18, 3010, Bern, Switzerland
| | - George Prenosil
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstr. 18, 3010, Bern, Switzerland
| | - Kuangyu Shi
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstr. 18, 3010, Bern, Switzerland
| | - Axel Rominger
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstr. 18, 3010, Bern, Switzerland
| | - Ali Afshar-Oromieh
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstr. 18, 3010, Bern, Switzerland
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35
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Mirkin S, Albensi BC. Should artificial intelligence be used in conjunction with Neuroimaging in the diagnosis of Alzheimer's disease? Front Aging Neurosci 2023; 15:1094233. [PMID: 37187577 PMCID: PMC10177660 DOI: 10.3389/fnagi.2023.1094233] [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: 11/09/2022] [Accepted: 03/27/2023] [Indexed: 05/17/2023] Open
Abstract
Alzheimer's disease (AD) is a progressive, neurodegenerative disorder that affects memory, thinking, behavior, and other cognitive functions. Although there is no cure, detecting AD early is important for the development of a therapeutic plan and a care plan that may preserve cognitive function and prevent irreversible damage. Neuroimaging, such as magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET), has served as a critical tool in establishing diagnostic indicators of AD during the preclinical stage. However, as neuroimaging technology quickly advances, there is a challenge in analyzing and interpreting vast amounts of brain imaging data. Given these limitations, there is great interest in using artificial Intelligence (AI) to assist in this process. AI introduces limitless possibilities in the future diagnosis of AD, yet there is still resistance from the healthcare community to incorporate AI in the clinical setting. The goal of this review is to answer the question of whether AI should be used in conjunction with neuroimaging in the diagnosis of AD. To answer the question, the possible benefits and disadvantages of AI are discussed. The main advantages of AI are its potential to improve diagnostic accuracy, improve the efficiency in analyzing radiographic data, reduce physician burnout, and advance precision medicine. The disadvantages include generalization and data shortage, lack of in vivo gold standard, skepticism in the medical community, potential for physician bias, and concerns over patient information, privacy, and safety. Although the challenges present fundamental concerns and must be addressed when the time comes, it would be unethical not to use AI if it can improve patient health and outcome.
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Affiliation(s)
- Sophia Mirkin
- Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, FL, United States
| | - Benedict C. Albensi
- Barry and Judy Silverman College of Pharmacy, Nova Southeastern University, Fort Lauderdale, FL, United States
- St. Boniface Hospital Research, Winnipeg, MB, Canada
- University of Manitoba, Winnipeg, MB, Canada
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36
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Saliba T, Boitsios G. ChatGPT, a radiologist's perspective. Pediatr Radiol 2023; 53:813-815. [PMID: 37017719 DOI: 10.1007/s00247-023-05656-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 03/13/2023] [Accepted: 03/23/2023] [Indexed: 04/06/2023]
Affiliation(s)
- Thomas Saliba
- Hopital Universitaire des Enfants Reine Fabiola (HUDERF), Av. Jean Joseph Crocq 15, 1020, Brussels, Belgium.
| | - Grammatina Boitsios
- Hopital Universitaire des Enfants Reine Fabiola (HUDERF), Av. Jean Joseph Crocq 15, 1020, Brussels, Belgium
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37
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Laborie LB, Naidoo J, Pace E, Ciet P, Eade C, Wagner MW, Huisman TAGM, Shelmerdine SC. European Society of Paediatric Radiology Artificial Intelligence taskforce: a new taskforce for the digital age. Pediatr Radiol 2023; 53:576-580. [PMID: 35731260 PMCID: PMC9214669 DOI: 10.1007/s00247-022-05426-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 05/26/2022] [Accepted: 06/03/2022] [Indexed: 11/08/2022]
Abstract
A new task force dedicated to artificial intelligence (AI) with respect to paediatric radiology was created in 2021 at the International Paediatric Radiology (IPR) meeting in Rome, Italy (a joint society meeting by the European Society of Pediatric Radiology [ESPR] and the Society for Pediatric Radiology [SPR]). The concept of a separate task force dedicated to AI was borne from an ESPR-led international survey of health care professionals' opinions, expectations and concerns regarding AI integration within children's imaging departments. In this survey, the majority (> 80%) of ESPR respondents supported the creation of a task force and helped define our key objectives. These include providing educational content about AI relevant for paediatric radiologists, brainstorming ideas for future projects and collaborating on AI-related studies with respect to collating data sets, de-identifying images and engaging in multi-case, multi-reader studies. This manuscript outlines the starting point of the ESPR AI task force and where we wish to go.
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Affiliation(s)
- Lene Bjerke Laborie
- grid.412008.f0000 0000 9753 1393Department of Radiology, Section for Paediatrics, Haukeland University Hospital, Bergen, Norway
- grid.7914.b0000 0004 1936 7443Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Jaishree Naidoo
- Paediatric Diagnostic Imaging and Envisionit Deep AI, Johannesburg, South Africa
| | - Erika Pace
- grid.5072.00000 0001 0304 893XDepartment of Diagnostic Radiology, The Royal Marsden NHS Foundation Trust, London, UK
| | - Pierluigi Ciet
- grid.5645.2000000040459992XDepartment of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
- grid.5645.2000000040459992XDepartment of Pediatric Pulmonology and Allergology, Erasmus MC, Sophia’s Children’s Hospital, Rotterdam, The Netherlands
| | - Christine Eade
- grid.8391.30000 0004 1936 8024University of Exeter Medical School, Exeter, UK
| | - Matthias W. Wagner
- grid.42327.300000 0004 0473 9646Department of Diagnostic Imaging, Division of Neuroradiology, The Hospital for Sick Children, Toronto, Canada
- grid.17063.330000 0001 2157 2938Department of Medical Imaging, University of Toronto, Toronto, Ontario Canada
| | - Thierry A. G. M. Huisman
- grid.39382.330000 0001 2160 926XEdward B. Singleton Department of Radiology, Texas Children’s Hospital, Baylor College of Medicine, Houston, Texas USA
| | - Susan C. Shelmerdine
- grid.424537.30000 0004 5902 9895Department of Clinical Radiology, Great Ormond Street Hospital for Children NHS Foundation Trust, Great Ormond Street, WC1H 3JH London, UK
- grid.83440.3b0000000121901201UCL Great Ormond Street Institute of Child Health, London, UK
- grid.451056.30000 0001 2116 3923NIHR Great Ormond Street Hospital Biomedical Research Centre, London, UK
- grid.464688.00000 0001 2300 7844Department of Clinical Radiology, St. George’s Hospital, London, UK
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Cavallo JJ, de Oliveira Santo I, Mezrich JL, Forman HP. Clinical Implementation of a Combined Artificial Intelligence and Natural Language Processing Quality Assurance Program for Pulmonary Nodule Detection in the Emergency Department Setting. J Am Coll Radiol 2023; 20:438-445. [PMID: 36736547 DOI: 10.1016/j.jacr.2022.12.016] [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: 08/27/2022] [Revised: 11/18/2022] [Accepted: 12/08/2022] [Indexed: 02/05/2023]
Abstract
OBJECTIVE This quality assurance study assessed the implementation of a combined artificial intelligence (AI) and natural language processing (NLP) program for pulmonary nodule detection in the emergency department setting. The program was designed to function outside of normal reading workflows to minimize radiologist interruption. MATERIALS AND METHODS In all, 19,246 CT examinations including at least some portion of the lung anatomy performed in the emergent setting from October 1, 2021, to June 1, 2022, were processed by the combined AI-NLP program. The program used an AI algorithm trained on 6-mm to 30-mm pulmonary nodules to analyze CT images and an NLP to analyze radiological reports. Cases flagged as negative for pulmonary nodules by the NLP but positive by the AI algorithm were classified as suspected discrepancies. Discrepancies result in secondary review of examinations for possible addenda. RESULTS Out of 19,246 CT examinations, 50 examinations (0.26%) resulted in secondary review, and 34 of 50 (68%) reviews resulted in addenda. Of the 34 addenda, 20 patients received instruction for new follow-up imaging. Median time to addendum was 11 hours. The majority of reviews and addenda resulted from missed pulmonary nodules on CT examinations of the abdomen and pelvis. CONCLUSION A background quality assurance process using AI and NLP helped improve the detection of pulmonary nodules and resulted in increased numbers of patients receiving appropriate follow-up imaging recommendations. This was achieved without disrupting in-shift radiologist workflow or causing significant delays in patient follow for the diagnosed pulmonary nodule.
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Affiliation(s)
- Joseph J Cavallo
- Assistant Director of Informatics and Assistant Medical Director of Clinical Affairs, Yale Radiology, Yale Department of Radiology and Biomedical Imaging, Yale New Haven Hospital, New Haven, Connecticut; Yale Department of Radiology and Biomedical Imaging, Yale New Haven Hospital, New Haven, Connecticut.
| | - Irene de Oliveira Santo
- Yale Department of Radiology and Biomedical Imaging, Yale New Haven Hospital, New Haven, Connecticut. https://twitter.com/DixeIrene
| | - Jonathan L Mezrich
- Assistant Director of Informatics and Assistant Medical Director of Clinical Affairs, Yale Radiology, Yale Department of Radiology and Biomedical Imaging, Yale New Haven Hospital, New Haven, Connecticut; Yale Department of Radiology and Biomedical Imaging, Yale New Haven Hospital, New Haven, Connecticut
| | - Howard P Forman
- Department of Radiology and Biomedical Imaging, Yale New Haven Hospital, New Haven, Connecticut; Director, MD/MBA Program, Yale School of Management, Yale University, New Haven, Connecticut; and Director, Health Care Management Program, Yale School of Public Health, Yale University, New Haven, Connecticut. https://twitter.com/thehowie
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Motwani M, Williams MC, Nieman K, Choi AD. Great debates in cardiac computed tomography: OPINION: "Artificial intelligence is key to the future of CCTA - The great hope". J Cardiovasc Comput Tomogr 2023; 17:18-21. [PMID: 35945132 DOI: 10.1016/j.jcct.2022.07.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 07/05/2022] [Accepted: 07/16/2022] [Indexed: 10/17/2022]
Affiliation(s)
- Manish Motwani
- Manchester Heart Institute, Manchester University NHS Foundation Trust, UK; Institute of Cardiovascular Science, University of Manchester, UK
| | - Michelle C Williams
- Center for Cardiovascular Sciences, The University of Edinburgh, Edinburgh, UK
| | - Koen Nieman
- Departments of Cardiovascular Medicine and Radiology, Stanford University, Stanford, CA, USA
| | - Andrew D Choi
- Division of Cardiology and Department of Radiology, The George Washington University School of Medicine, Washington, DC, USA.
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40
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van Ginneken B. Tuberculosis Detection from Chest Radiographs: Stop Training Radiologists Now. Radiology 2023; 306:138-139. [PMID: 36066371 DOI: 10.1148/radiol.221769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Bram van Ginneken
- From the Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, the Netherlands
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41
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Hauw JJ. How and why I fell into neuropathology. FREE NEUROPATHOLOGY 2023; 4:4-21. [PMID: 38033707 PMCID: PMC10683528 DOI: 10.17879/freeneuropathology-2023-5112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 09/08/2023] [Indexed: 12/02/2023]
Abstract
I'm going to explain how and why I fell into the Department of Neuropathology at the Pitié-Salpêtrière Hospital of Paris. I'd also like to sketch the history of French neuropathology in the years 1960-2010, as seen by a naive young student, and then by a practicing neuropathologist (often still very naive). As a matter of fact, although the history of neurosciences [1-2] and the Pitié-Salpêtrière Hospital in Paris [3-4] have been the subject of numerous publications, the history of neuropathology in this hospital has been rarely documented [5-6]. I spent more than forty years strolling along the alleys of La Salpêtrière, among its old pavilions, the Saint Louis chapel, the "Pavillon des folles", the courtyard of Manon Lescaut and the guard room. I worked full-time between the Escourolle laboratory, the "Amphithéâtre des morts" and the University. It has been a real pleasure to be part of this world. I would also like to offer young doctors in training and future neuropathologists some advice that might help them in the choice and development of their future careers.
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Cellina M, Cè M, Irmici G, Ascenti V, Caloro E, Bianchi L, Pellegrino G, D’Amico N, Papa S, Carrafiello G. Artificial Intelligence in Emergency Radiology: Where Are We Going? Diagnostics (Basel) 2022; 12:diagnostics12123223. [PMID: 36553230 PMCID: PMC9777804 DOI: 10.3390/diagnostics12123223] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 12/11/2022] [Accepted: 12/16/2022] [Indexed: 12/23/2022] Open
Abstract
Emergency Radiology is a unique branch of imaging, as rapidity in the diagnosis and management of different pathologies is essential to saving patients' lives. Artificial Intelligence (AI) has many potential applications in emergency radiology: firstly, image acquisition can be facilitated by reducing acquisition times through automatic positioning and minimizing artifacts with AI-based reconstruction systems to optimize image quality, even in critical patients; secondly, it enables an efficient workflow (AI algorithms integrated with RIS-PACS workflow), by analyzing the characteristics and images of patients, detecting high-priority examinations and patients with emergent critical findings. Different machine and deep learning algorithms have been trained for the automated detection of different types of emergency disorders (e.g., intracranial hemorrhage, bone fractures, pneumonia), to help radiologists to detect relevant findings. AI-based smart reporting, summarizing patients' clinical data, and analyzing the grading of the imaging abnormalities, can provide an objective indicator of the disease's severity, resulting in quick and optimized treatment planning. In this review, we provide an overview of the different AI tools available in emergency radiology, to keep radiologists up to date on the current technological evolution in this field.
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Affiliation(s)
- Michaela Cellina
- Radiology Department, Fatebenefratelli Hospital, ASST Fatebenefratelli Sacco, Milano, Piazza Principessa Clotilde 3, 20121 Milan, Italy
- Correspondence:
| | - Maurizio Cè
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Giovanni Irmici
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Velio Ascenti
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Elena Caloro
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Lorenzo Bianchi
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Giuseppe Pellegrino
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Natascha D’Amico
- Unit of Diagnostic Imaging and Stereotactic Radiosurgery, Centro Diagnostico Italiano, Via Saint Bon 20, 20147 Milan, Italy
| | - Sergio Papa
- Unit of Diagnostic Imaging and Stereotactic Radiosurgery, Centro Diagnostico Italiano, Via Saint Bon 20, 20147 Milan, Italy
| | - Gianpaolo Carrafiello
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
- Radiology Department, Fondazione IRCCS Cà Granda, Policlinico di Milano Ospedale Maggiore, Via Sforza 35, 20122 Milan, Italy
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Mosch L, Fürstenau D, Brandt J, Wagnitz J, Klopfenstein SAI, Poncette AS, Balzer F. The medical profession transformed by artificial intelligence: Qualitative study. Digit Health 2022; 8:20552076221143903. [PMID: 36532112 PMCID: PMC9756357 DOI: 10.1177/20552076221143903] [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: 07/20/2022] [Accepted: 11/18/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Healthcaare delivery will change through the increasing use of artificial intelligence (AI). Physicians are likely to be among the professions most affected, though to what extent is not yet clear. OBJECTIVE We analyzed physicians' and AI experts' stances towards AI-induced changes. This concerned (1) physicians' tasks, (2) job replacement risk, and (3) implications for the ways of working, including human-AI interaction, changes in job profiles, and hierarchical and cross-professional collaboration patterns. METHODS We adopted an exploratory, qualitative research approach, using semi-structured interviews with 24 experts in the fields of AI and medicine, medical informatics, digital medicine, and medical education and training. Thematic analysis of the interview transcripts was performed. RESULTS Specialized tasks currently performed by physicians in all areas of medicine would likely be taken over by AI, including bureaucratic tasks, clinical decision support, and research. However, the concern that physicians will be replaced by an AI system is unfounded, according to experts; AI systems today would be designed only for a specific use case and could not replace the human factor in the patient-physician relationship. Nevertheless, the job profile and professional role of physicians would be transformed as a result of new forms of human-AI collaboration and shifts to higher-value activities. AI could spur novel, more interprofessional teams in medical practice and research and, eventually, democratization and de-hierarchization. CONCLUSIONS The study highlights changes in job profiles of physicians and outlines demands for new categories of medical professionals considering AI-induced changes of work. Physicians should redefine their self-image and assume more responsibility in the age of AI-supported medicine. There is a need for the development of scenarios and concepts for future job profiles in the health professions as well as their education and training.
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Affiliation(s)
- Lina Mosch
- Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Medical Informatics, Berlin, Germany,Department of Anesthesiology and Intensive Care Medicine, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany,Lina Mosch, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Medical Informatics, Charitéplatz 1, 10117 Berlin, Germany
| | - Daniel Fürstenau
- Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Medical Informatics, Berlin, Germany,Department of Business IT, IT University of Copenhagen, København, Denmark
| | - Jenny Brandt
- Universitätsmedizin Mainz, corporate member of Johannes Gutenberg University, Mainz, Germany
| | - Jasper Wagnitz
- Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Medical Informatics, Berlin, Germany
| | - Sophie AI Klopfenstein
- Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Medical Informatics, Berlin, Germany,Core Facility Digital Medicine and Interoperability, Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Akira-Sebastian Poncette
- Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Medical Informatics, Berlin, Germany,Department of Anesthesiology and Intensive Care Medicine, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Felix Balzer
- Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Medical Informatics, Berlin, Germany
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AI in breast screening mammography: breast screening readers' perspectives. Insights Imaging 2022; 13:186. [PMID: 36484919 PMCID: PMC9733732 DOI: 10.1186/s13244-022-01322-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 11/01/2022] [Indexed: 12/13/2022] Open
Abstract
OBJECTIVES This study surveyed the views of breast screening readers in the UK on how to incorporate Artificial Intelligence (AI) technology into breast screening mammography. METHODS An online questionnaire was circulated to the UK breast screening readers. Questions included their degree of approval of four AI implementation scenarios: AI as triage, AI as a companion reader/reader aid, AI replacing one of the initial two readers, and AI replacing all readers. They were also asked to rank five AI representation options (discrete opinion; mammographic scoring; percentage score with 100% indicating malignancy; region of suspicion; heat map) and indicate which evidence they considered necessary to support the implementation of AI into their practice among six options offered. RESULTS The survey had 87 nationally accredited respondents across the UK; 73 completed the survey in full. Respondents approved of AI replacing one of the initial two human readers and objected to AI replacing all human readers. Participants were divided on AI as triage and AI as a reader companion. A region of suspicion superimposed on the image was the preferred AI representation option. Most screen readers considered national guidelines (77%), studies using a nationally representative dataset (65%) and independent prospective studies (60%) as essential evidence. Participants' free-text comments highlighted concerns and the need for additional validation. CONCLUSIONS Overall, screen readers supported the introduction of AI as a partial replacement of human readers and preferred a graphical indication of the suspected tumour area, with further evidence and national guidelines considered crucial prior to implementation.
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Szabo L, Raisi-Estabragh Z, Salih A, McCracken C, Ruiz Pujadas E, Gkontra P, Kiss M, Maurovich-Horvath P, Vago H, Merkely B, Lee AM, Lekadir K, Petersen SE. Clinician's guide to trustworthy and responsible artificial intelligence in cardiovascular imaging. Front Cardiovasc Med 2022; 9:1016032. [PMID: 36426221 PMCID: PMC9681217 DOI: 10.3389/fcvm.2022.1016032] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 10/11/2022] [Indexed: 12/01/2023] Open
Abstract
A growing number of artificial intelligence (AI)-based systems are being proposed and developed in cardiology, driven by the increasing need to deal with the vast amount of clinical and imaging data with the ultimate aim of advancing patient care, diagnosis and prognostication. However, there is a critical gap between the development and clinical deployment of AI tools. A key consideration for implementing AI tools into real-life clinical practice is their "trustworthiness" by end-users. Namely, we must ensure that AI systems can be trusted and adopted by all parties involved, including clinicians and patients. Here we provide a summary of the concepts involved in developing a "trustworthy AI system." We describe the main risks of AI applications and potential mitigation techniques for the wider application of these promising techniques in the context of cardiovascular imaging. Finally, we show why trustworthy AI concepts are important governing forces of AI development.
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Affiliation(s)
- Liliana Szabo
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom
- Semmelweis University Heart and Vascular Center, Budapest, Hungary
| | - Zahra Raisi-Estabragh
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom
| | - Ahmed Salih
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom
| | - Celeste McCracken
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, National Institute for Health Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, University of Oxford, Oxford, United Kingdom
| | - Esmeralda Ruiz Pujadas
- Departament de Matemàtiques i Informàtica, Artificial Intelligence in Medicine Lab (BCN-AIM), Universitat de Barcelona, Barcelona, Spain
| | - Polyxeni Gkontra
- Departament de Matemàtiques i Informàtica, Artificial Intelligence in Medicine Lab (BCN-AIM), Universitat de Barcelona, Barcelona, Spain
| | - Mate Kiss
- Siemens Healthcare Hungary, Budapest, Hungary
| | - Pal Maurovich-Horvath
- Department of Radiology, Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| | - Hajnalka Vago
- Semmelweis University Heart and Vascular Center, Budapest, Hungary
| | - Bela Merkely
- Semmelweis University Heart and Vascular Center, Budapest, Hungary
| | - Aaron M. Lee
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom
| | - Karim Lekadir
- Departament de Matemàtiques i Informàtica, Artificial Intelligence in Medicine Lab (BCN-AIM), Universitat de Barcelona, Barcelona, Spain
| | - Steffen E. Petersen
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom
- Health Data Research UK, London, United Kingdom
- Alan Turing Institute, London, United Kingdom
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Artificial intelligence and machine learning in cancer imaging. COMMUNICATIONS MEDICINE 2022; 2:133. [PMID: 36310650 PMCID: PMC9613681 DOI: 10.1038/s43856-022-00199-0] [Citation(s) in RCA: 46] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2020] [Accepted: 10/06/2022] [Indexed: 11/16/2022] Open
Abstract
An increasing array of tools is being developed using artificial intelligence (AI) and machine learning (ML) for cancer imaging. The development of an optimal tool requires multidisciplinary engagement to ensure that the appropriate use case is met, as well as to undertake robust development and testing prior to its adoption into healthcare systems. This multidisciplinary review highlights key developments in the field. We discuss the challenges and opportunities of AI and ML in cancer imaging; considerations for the development of algorithms into tools that can be widely used and disseminated; and the development of the ecosystem needed to promote growth of AI and ML in cancer imaging.
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Artificial Intelligence in Orthopedic Radiography Analysis: A Narrative Review. Diagnostics (Basel) 2022; 12:diagnostics12092235. [PMID: 36140636 PMCID: PMC9498096 DOI: 10.3390/diagnostics12092235] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 09/12/2022] [Accepted: 09/13/2022] [Indexed: 11/17/2022] Open
Abstract
Artificial intelligence (AI) in medicine is a rapidly growing field. In orthopedics, the clinical implementations of AI have not yet reached their full potential. Deep learning algorithms have shown promising results in computed radiographs for fracture detection, classification of OA, bone age, as well as automated measurements of the lower extremities. Studies investigating the performance of AI compared to trained human readers often show equal or better results, although human validation is indispensable at the current standards. The objective of this narrative review is to give an overview of AI in medicine and summarize the current applications of AI in orthopedic radiography imaging. Due to the different AI software and study design, it is difficult to find a clear structure in this field. To produce more homogeneous studies, open-source access to AI software codes and a consensus on study design should be aimed for.
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Ngo B, Nguyen D, vanSonnenberg E. The Cases for and against Artificial Intelligence in the Medical School Curriculum. Radiol Artif Intell 2022; 4:e220074. [PMID: 36204540 PMCID: PMC9530767 DOI: 10.1148/ryai.220074] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Revised: 07/26/2022] [Accepted: 08/02/2022] [Indexed: 06/02/2023]
Abstract
Although artificial intelligence (AI) has immense potential to shape the future of medicine, its place in undergraduate medical education currently is unclear. Numerous arguments exist both for and against including AI in the medical school curriculum. AI likely will affect all medical specialties, perhaps radiology more so than any other. The purpose of this article is to present a balanced perspective on whether AI should be included officially in the medical school curriculum. After presenting the balanced point-counterpoint arguments, the authors provide a compromise. Keywords: Artificial Intelligence, Medical Education, Medical School Curriculum, Medical Students, Radiology, Use of AI in Education © RSNA, 2022.
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A Survey on the Use of Artificial Intelligence by Clinicians in Dentistry and Oral and Maxillofacial Surgery. MEDICINA (KAUNAS, LITHUANIA) 2022; 58:medicina58081059. [PMID: 36013526 PMCID: PMC9412897 DOI: 10.3390/medicina58081059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 07/19/2022] [Accepted: 08/03/2022] [Indexed: 11/17/2022]
Abstract
Background: Applications of artificial intelligence (AI) in medicine and dentistry have been on the rise in recent years. In dental radiology, deep learning approaches have improved diagnostics, outperforming clinicians in accuracy and efficiency. This study aimed to provide information on clinicians' knowledge and perceptions regarding AI. Methods: A 21-item questionnaire was used to study the views of dentistry professionals on AI use in clinical practice. Results: In total, 302 questionnaires were answered and assessed. Most of the respondents rated their knowledge of AI as average (37.1%), below average (22.2%) or very poor (23.2%). The participants were largely convinced that AI would improve and bring about uniformity in diagnostics (mean Likert ± standard deviation 3.7 ± 1.27). Among the most serious concerns were the responsibility for machine errors (3.7 ± 1.3), data security or privacy issues (3.5 ± 1.24) and the divestment of healthcare to large technology companies (3.5 ± 1.28). Conclusions: Within the limitations of this study, insights into the acceptance and use of AI in dentistry are revealed for the first time.
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Nair A, Ramanathan S, Sathiadoss P, Jajodia A, Macdonald DB. Dificultades en la implantación de la inteligencia artificial en la práctica radiológica: lo que el radiólogo necesita saber. RADIOLOGIA 2022. [DOI: 10.1016/j.rx.2022.04.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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