Empirical assessment of bias in machine learning diagnostic test accuracy studies | Zendy

Ryan Crowley | Zendy; Yuan Tan | Zendy; John P. A. Ioannidis | Zendy

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Empirical assessment of bias in machine learning diagnostic test accuracy studies

Author(s) -

Ryan Crowley,

Yuan Tan,

John P. A. Ioannidis

Publication year - 2020

Publication title -

journal of the american medical informatics association

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.614

H-Index - 150

eISSN - 1527-974X

pISSN - 1067-5027

DOI - 10.1093/jamia/ocaa075

Subject(s) - generalizability theory , blinding , confidence interval , diagnostic accuracy , medicine , diagnostic odds ratio , clinical study design , test (biology) , sample size determination , research design , machine learning , artificial intelligence , computer science , statistics , medical physics , randomized controlled trial , pathology , clinical trial , radiology , mathematics , paleontology , biology

Machine learning (ML) diagnostic tools have significant potential to improve health care. However, methodological pitfalls may affect diagnostic test accuracy studies used to appraise such tools. We aimed to evaluate the prevalence and reporting of design characteristics within the literature. Further, we sought to empirically assess whether design features may be associated with different estimates of diagnostic accuracy.

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