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

Autor:	Blacketer, Charlotte, Parnis, Roger, B. Franke, Kyle, Wagner, Morganne, Wang, David, Tan, Yiran, Oakden‐Rayner, Luke, Gallagher, Steve, Perry, Seth W., Licinio, Julio, Symonds, Ian, Thomas, Josephine, Duggan, Paul, Bacchi, Stephen
Předmět:	NATIONAL competency-based educational tests MEDICAL students HEALTH occupations students CROSS-sectional method MACHINE learning TASK performance
Zdroj:	Internal Medicine Journal; Sep2021, Vol. 51 Issue 9, p1539-1542, 4p
Abstrakt:	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. [ABSTRACT FROM AUTHOR]
Databáze:	Complementary Index
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