AI education for clinicians

被引：1
作者：
Schubert, Tim [1 ,2 ,8 ]
Oosterlinck, Tim [1 ,3 ]
Stevens, Robert D. [4 ,5 ]
Maxwell, Patrick H. [6 ]
van der Schaar, Mihaela [1 ,7 ]
机构：
[1] Univ Cambridge, Dept Appl Math & Theoret Phys, Cambridge, England
[2] Heidelberg Univ, Med Fac, Heidelberg, Germany
[3] Katholieke Univ Leuven, Fac Med, Leuven, Belgium
[4] Johns Hopkins Univ, Dept Anesthesiol & Crit Care Med, Dept Biomed Engn, Baltimore, MD USA
[5] Johns Hopkins Univ, Inst Computat Med, Baltimore, MD USA
[6] Univ Cambridge, Sch Clin Med, Cambridge, England
[7] Cambridge Ctr AI Med, Cambridge, England
[8] Heidelberg Univ, Inst Human Genet, Heidelberg, Germany
来源：
ECLINICALMEDICINE | 2025年 / 79卷
关键词：
Arti fi cial intelligence; Machine learning; Medical education; Clinicians; Framework; ARTIFICIAL-INTELLIGENCE;
D O I：
10.1016/j.eclinm.2024.102968
中图分类号：
R5 [内科学];
学科分类号：
1002 ; 100201 ;
摘要：
Rapid advancements in medical AI necessitate targeted educational initiatives for clinicians to ensure AI tools are safe and used effectively to improve patient outcomes. To support decision-making among stakeholders in medical education, we propose three tiers of medical AI expertise and outline the challenges for medical education at different educational stages. Additionally, we offer recommendations and examples, encouraging stakeholders to adapt and shape curricula for their specific healthcare setting using this framework.
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页数：7
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共 38 条
  • [1] Search for diphoton events with large missing transverse energy with 36 pb-1 of 7 TeV proton-proton collision data with the ATLAS detector
    Aad, G.
    Abbott, B.
    Abdallah, J.
    Abdelalim, A. A.
    Abdesselam, A.
    Abdinov, O.
    Abi, B.
    Abolins, M.
    Abramowicz, H.
    Abreu, H.
    Acerbi, E.
    Acharya, B. S.
    Adams, D. L.
    Addy, T. N.
    Adelman, J.
    Aderholz, M.
    Adomeit, S.
    Adragna, P.
    Adye, T.
    Aefsky, S.
    Aguilar-Saavedra, J. A.
    Aharrouche, M.
    Ahlen, S. P.
    Ahles, F.
    Ahmad, A.
    Ahsan, M.
    Aielli, G.
    Akdogan, T.
    Akesson, T. P. A.
    Akimoto, G.
    Akimov, A. V.
    Akiyama, A.
    Alam, M. S.
    Alam, M. A.
    Albert, J.
    Albrand, S.
    Aleksa, M.
    Aleksandrov, I. N.
    Alessandria, F.
    Alexa, C.
    Alexander, G.
    Alexandre, G.
    Alexopoulos, T.
    Alhroob, M.
    Aliev, M.
    Alimonti, G.
    Alison, J.
    Aliyev, M.
    Allport, P. P.
    Allwood-Spiers, S. E.
    [J]. EUROPEAN PHYSICAL JOURNAL C, 2011, 71 (10):
  • [2] Large language models: a new chapter in digital health
    The Lancet Digital Health
    [J]. The Lancet Digital Health, 2024, 6 (01):
  • [3] AI added to the curriculum for doctors-to-be
    Brouillette, Monique
    [J]. NATURE MEDICINE, 2019, 25 (12) : 1808 - 1809
  • [4] Chan Kai Siang, 2019, JMIR Med Educ, V5, pe13930, DOI 10.2196/13930
  • [5] Artificial Intelligence Education Programs for Health Care Professionals: Scoping Review
    Charow, Rebecca
    Jeyakumar, Tharshini
    Younus, Sarah
    Dolatabadi, Elham
    Salhia, Mohammad
    Al-Mouaswas, Dalia
    Anderson, Melanie
    Balakumar, Sarmini
    Clare, Megan
    Dhalla, Azra
    Gillan, Caitlin
    Haghzare, Shabnam
    Jackson, Ethan
    Lalani, Nadim
    Mattson, Jane
    Peteanu, Wanda
    Tripp, Tim
    Waldorf, Jacqueline
    Williams, Spencer
    Tavares, Walter
    Wiljer, David
    [J]. JMIR MEDICAL EDUCATION, 2021, 7 (04):
  • [6] AI and Medical Education - A 21st-Century Pandora's Box
    Cooper, Avraham
    Rodman, Adam
    [J]. NEW ENGLAND JOURNAL OF MEDICINE, 2023, 389 (05) : 385 - 387
  • [7] Medical students' attitude towards artificial intelligence: a multicentre survey
    dos Santos, D. Pinto
    Giese, D.
    Brodehl, S.
    Chon, S. H.
    Staab, W.
    Kleinert, R.
    Maintz, D.
    Baessler, B.
    [J]. EUROPEAN RADIOLOGY, 2019, 29 (04) : 1640 - 1646
  • [8] Utilization of Self-Diagnosis Health Chatbots in Real-World Settings: Case Study
    Fan, Xiangmin
    Chao, Daren
    Zhang, Zhan
    Wang, Dakuo
    Li, Xiaohua
    Tian, Feng
    [J]. JOURNAL OF MEDICAL INTERNET RESEARCH, 2021, 23 (01)
  • [9] Causal machine learning for predicting treatment outcomes
    Feuerriegel, Stefan
    Frauen, Dennis
    Melnychuk, Valentyn
    Schweisthal, Jonas
    Hess, Konstantin
    Curth, Alicia
    Bauer, Stefan
    Kilbertus, Niki
    Kohane, Isaac S.
    van der Schaar, Mihaela
    [J]. NATURE MEDICINE, 2024, 30 (03) : 958 - 968
  • [10] Transforming Cardiovascular Care With Artificial Intelligence: From Discovery to Practice
    Khera, Rohan
    Oikonomou, Evangelos K.
    Nadkarni, Girish N.
    Morley, Jessica R.
    Wiens, Jenna
    Butte, Atul J.
    Topol, Eric J.
    [J]. JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY, 2024, 84 (01) : 97 - 114
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