SynthesisJournal of the American Medical Informatics Association : JAMIA2020
Empirical assessment of bias in machine learning diagnostic test accuracy studies.
Synthesis in Journal of the American Medical Informatics Association : JAMIA, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 4 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Extracting cancer concepts from clinical notes using natural language processing: a systematic review.BMC bioinformatics · 2023Pooled it
- Trial-level characteristics associate with treatment effect estimates: a systematic review of meta-epidemiological studies.BMC medical research methodology · 2022Pooled it
- Predicting breast cancer 5-year survival using machine learning: A systematic review.PloS one · 2021Pooled it
- Pooled it
- A Systematic Review and Meta-analysis of Two-Dimensional Breast Ultrasound Radiomics: Implications for Lesion Characterization and Diagnostic Accuracy.Journal of imaging informatics in medicine · 2026Review
- Development of a Machine Learning Model for Predicting Treatment-Related Amenorrhea in Young Women with Breast Cancer.Bioengineering (Basel, Switzerland) · 2025Article
- Bayesian Model Prediction for Breast Cancer Survival: A Retrospective Analysis.European journal of breast health · 2025Article
- Machine learning for prediction of childhood mental health problems in social care.BJPsych open · 2025Article
- Beyond Step Count: Are We Ready to Use Digital Phenotyping to Make Actionable Individual Predictions in Psychiatry?Journal of medical Internet research · 2024Article
- Screening for obstructive sleep apnea in patients with cancer - a machine learning approach.Sleep advances : a journal of the Sleep Research Society · 2023Article
- Clinical Informatics Approaches to Understand and Address Cancer Disparities.Yearbook of medical informatics · 2022Review
- Machine Learning Decomposition of the Anatomy of Neuropsychological Deficit in Alzheimer's Disease and Mild Cognitive Impairment.Frontiers in aging neuroscience · 2022Article
- Clinician Preimplementation Perspectives of a Decision-Support Tool for the Prediction of Cardiac Arrhythmia Based on Machine Learning: Near-Live Feasibility and Qualitative Study.JMIR human factors · 2021Article
- Enhancing trust in AI through industry self-governance.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Extracellular MicroRNAs as Intercellular Mediators and Noninvasive Biomarkers of Cancer.Cancers · 2020Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveMachine 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. MATERIALS AND
methodsWe systematically retrieved 2 × 2 tables (n = 281) describing the performance of ML diagnostic tools, derived from 114 publications in 38 meta-analyses, from PubMed. Data extracted included test performance, sample sizes, and design features. A mixed-effects metaregression was run to quantify the association between design features and diagnostic accuracy.
resultsParticipant ethnicity and blinding in test interpretation was unreported in 90% and 60% of studies, respectively. Reporting was occasionally lacking for rudimentary characteristics such as study design (28% unreported). Internal validation without appropriate safeguards was used in 44% of studies. Several design features were associated with larger estimates of accuracy, including having unreported (relative diagnostic odds ratio [RDOR], 2.11; 95% confidence interval [CI], 1.43-3.1) or case-control study designs (RDOR, 1.27; 95% CI, 0.97-1.66), and recruiting participants for the index test (RDOR, 1.67; 95% CI, 1.08-2.59). DISCUSSION: Significant underreporting of experimental details was present. Study design features may affect estimates of diagnostic performance in the ML diagnostic test accuracy literature.
conclusionsThe present study identifies pitfalls that threaten the validity, generalizability, and clinical value of ML diagnostic tools and provides recommendations for improvement.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.