ArticleJournal of the American Medical Informatics Association : JAMIA2020
Reporting of demographic data and representativeness in machine learning models using electronic health records.
Article 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 40 papers, 5 of them syntheses that pooled it.
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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
40 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- The Algorithmic Divide: A Systematic Review on AI-Driven Racial Disparities in Healthcare.Journal of racial and ethnic health disparities · 2026Pooled it
- Examining inclusivity: the use of AI and diverse populations in health and social care: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Pooled it
- Quality of reporting of randomised controlled trials of artificial intelligence in healthcare: a systematic review.BMJ open · 2022Pooled it
- Assessment of Adherence to Reporting Guidelines by Commonly Used Clinical Prediction Models From a Single Vendor: A Systematic Review.JAMA network open · 2022Pooled it
- Reporting of race and ethnicity in studies of artificial intelligence in pediatric urology A secondary analysis of the AI-PEDURO online repository.Canadian Urological Association journal = Journal de l'Association des urologues du Canada · 2026Review
- A generalisation study in deep learning-based segmentation of lower-limb muscles across different populations.Scientific reports · 2026Article
- Good things come in threes: evaluating clinical utility of machine learning-derived clusters.JAMIA open · 2026Review
- The Ethics of Leveraging Routinely Collected Patient Data for AI Development: Mixed Methods Study.Journal of medical Internet research · 2026Article
- A data-centric perspective on designing AI foundation models for healthcare.Frontiers in digital health · 2026Article
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- Integration of artificial intelligence and wearable technology in the management of diabetes and prediabetes.NPJ digital medicine · 2025Article
- Machine Learning Applications in Population and Public Health: Guidelines for Development, Testing, and Implementation.JMIR public health and surveillance · 2025Article
- Assessing the quality of reporting in artificial intelligence/machine learning research for cardiac amyloidosis.JAMIA open · 2025Article
- Evaluating the impact of data biases on algorithmic fairness and clinical utility of machine learning models for prolonged opioid use prediction.JAMIA open · 2025Article
- Type 2 Diabetes Prediction Model in China: A Five-Year Systematic Review.Healthcare (Basel, Switzerland) · 2025Review
- Application of machine learning algorithms and SHAP explanations to predict fertility preference among reproductive women in Somalia.Scientific reports · 2025Article
- Computational strategic recruitment for representation and coverage studied in the All of Us Research Program.NPJ digital medicine · 2025Article
- What makes clinical machine learning fair? A practical ethics framework.PLOS digital health · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe development of machine learning (ML) algorithms to address a variety of issues faced in clinical practice has increased rapidly. However, questions have arisen regarding biases in their development that can affect their applicability in specific populations. We sought to evaluate whether studies developing ML models from electronic health record (EHR) data report sufficient demographic data on the study populations to demonstrate representativeness and reproducibility. MATERIALS AND
methodsWe searched PubMed for articles applying ML models to improve clinical decision-making using EHR data. We limited our search to papers published between 2015 and 2019.
resultsAcross the 164 studies reviewed, demographic variables were inconsistently reported and/or included as model inputs. Race/ethnicity was not reported in 64%; gender and age were not reported in 24% and 21% of studies, respectively. Socioeconomic status of the population was not reported in 92% of studies. Studies that mentioned these variables often did not report if they were included as model inputs. Few models (12%) were validated using external populations. Few studies (17%) open-sourced their code. Populations in the ML studies include higher proportions of White and Black yet fewer Hispanic subjects compared to the general US population. DISCUSSION: The demographic characteristics of study populations are poorly reported in the ML literature based on EHR data. Demographic representativeness in training data and model transparency is necessary to ensure that ML models are deployed in an equitable and reproducible manner. Wider adoption of reporting guidelines is warranted to improve representativeness and reproducibility.
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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.