Medical student knowledge and critical appraisal of machine learning: a multicentre international cross‐sectional study

Abstract

To utilise effectively tools that employ machine learning (ML) in clinical practice medical students and doctors will require a degree of understanding of ML models. To evaluate current levels of understanding, a formative examination and survey was conducted across three centres in Australia, New Zealand and the United States. Of the 245 individuals who participated in the study (response rate = 45.4%), the majority had difficulty with identifying weaknesses in model performance analysis. Further studies examining educational interventions addressing such ML topics are warranted.

Attitudes, knowledge, and skills towards artificial intelligence among healthcare students: A systematic review

“…However, in nine of the studies (24%), students considered AI a threat to healthcare fields, especially radiology, and had a negative attitude towards it 19,34,36,38,42,51,53,59,60 . Among these, six studies were conducted in HICs 19,34,38,53,59,60 and three studies were conducted in LMICs 36,42,51 .…”

“…Recent broad surveys report a range of positive 14,18,26–33,36,38–41,43–48,50,51,53–55,57,59 and negative 19,34,36,38,42,51,53,59,60 attitudes towards AI. Although attitudes towards AI among healthcare students were generally positive.…”

“…Therefore, if countries have a plan to develop and use AI, it is necessary to plan for its training now 68 . Also, to properly use AI technology, it is very important to provide education to healthcare students as key players in the health of society 31–33,35,59 . Additionally, to accomplish this goal, healthcare organizations, hospitals, employers, and the media must collaborate to address this shortage 37,69–74 .…”

“…58 Also, three studies were widely conducted in several countries around the world. 31,48,60 The study field for healthcare students included six fields: medicine, 19,26,27,29,[31][32][33][35][36][37][38][39][40][48][49][50][53][54][55][56]59,60 dentistry, 28,30,31,41,44,52 radiology, 34,42,43,45,46,51,57,58 medical physics, 48 ophthalmology and dermatology, 47 and others. 14 The studies' total sample size was 19,378 participants, with sample sizes ranging from 101 34 to 3312 31 participants.…”

Professionalism and clinical short answer question marking with machine learning

“…Machine learning has an important role in medical education 1 . Not only is it important that medical students learn about machine learning, 2 but machine learning may also facilitate learning in other aspects of medicine. The evaluation of medical student and physician performance, such as on written examinations, is one example of this potential.…”