Journal of Educational Evaluation for Health Professions · 2019 · 195 citations · 12 references
MedicineClinical SciencesMedical EducationArtificial IntelligenceClinical Decision SupportClinical MedicineClinical Decision-makingClinical ReasoningEvidence-based MedicineMedical CurriculumMedical Education ApplicationsAi LiteracyArtificial Intelligence LiteracyClinical Decision Making EducationClinical Reasoning EducationMedical Student EducationMedical TrainingTrustworthy Ai
Artificial intelligence promises substantial improvements in healthcare but is surrounded by hype, requiring medical professionals to address patient concerns and ensure its beneficial use, making AI literacy a high priority for medical students. The study aims to equip medical students with the skills to discern accurate AI information from hype and to generate validated, trustworthy content for patients and the public. This involves learning beyond programming, encompassing basic and clinical medicine, data science, biostatistics, and evidence-based medicine.
Artificial intelligence (AI) is expected to affect various fields of medicine substantially and has the potential to improve many aspects of healthcare. However, AI has been creating much hype, too. In applying AI technology to patients, medical professionals should be able to resolve any anxiety, confusion, and questions that patients and the public may have. Also, they are responsible for ensuring that AI becomes a technology beneficial for patient care. These make the acquisition of sound knowledge and experience about AI a task of high importance for medical students. Preparing for AI does not merely mean learning information technology such as computer programming. One should acquire sufficient knowledge of basic and clinical medicines, data science, biostatistics, and evidence-based medicine. As a medical student, one should not passively accept stories related to AI in medicine in the media and on the Internet. Medical students should try to develop abilities to distinguish correct information from hype and spin and even capabilities to create thoroughly validated, trustworthy information for patients and the public.
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Daniel Shu Wei Ting, Carol Y. Cheung, Gilbert Lim et al. · JAMA · 2017 · 2.3K citations · Full text
Questions for Artificial Intelligence in Health Care
Thomas M. Maddox, John S. Rumsfeld, Philip Payne · JAMA · 2018 · 383 citations