Medical student knowledge and critical appraisal of machine learning: a multicentre international cross‐sectional study. Issue 9 (19th September 2021)

• Record Type: Journal Article
• Authors: 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
• Is Part Of: Internal medicine journal. Volume 51:Issue 9(2021)
• Page Start: 1539
• Page End: 1542
• Publication Date: 2021-09-19
• Subjects: artificial intelligence -- curriculum -- deep learning -- formative examination -- medical education -- medical school; Medicine -- Periodicals; 616
• DOI: 10.1111/imj.15479
• Languages: English
• ISSNs: 1444-0903

Abstract

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.