ArticleNature medicine2021
An algorithmic approach to reducing unexplained pain disparities in underserved populations.
Article in Nature medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 110 papers, 1 of them a synthesis that pooled it.
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Who cites it
110 citing papers in PubMed, 1 synthesis or guideline pooled it, 259 citations in OpenAlex.
- Demystifying Artificial Intelligence: A Systematic Review of Explainable Artificial Intelligence in Medical Imaging.Sensors (Basel, Switzerland) · 2026Pooled it
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- Representational Veracity in Data Science Health Research: Targets, Proxies, Labels, and Descriptors.Journal of medical Internet research · 2026Article
- Localizing Knee Pain via Explainable Bayesian Generative Models and Counterfactual MRI: Data from the Osteoarthritis Initiative.Journal of imaging informatics in medicine · 2026Article
- Artificial Intelligence in Rheumatology: A Comprehensive Bibliometric Analysis and Current Scientific Mapping Research.Mediterranean journal of rheumatology · 2026Review
- Strategies for mitigating artificial intelligence bias in healthcare: a systematic review.JAMIA open · 2026Review
- WiseMind: a knowledge-guided multi-agent framework for accurate and empathetic psychiatric diagnosis.NPJ digital medicine · 2026Article
- Artificial intelligence in primary care: innovation at a crossroads.The Lancet. Primary care · 2026Review
- Assessing the Impact of Sociodemographic Factors on Artificial Intelligence Models in Predicting Dementia: Retrospective Cohort Study.JMIR medical informatics · 2026Article
- How public involvement can improve the science of AI.Proceedings of the National Academy of Sciences of the United States of America · 2025Review
- Article
- Discrimination and wellbeing are differentially related to pain severity for the racially marginalized.Pain medicine (Malden, Mass.) · 2025Article
- Explainable Artificial Intelligence: A Perspective on Drug Discovery.Pharmaceutics · 2025Review
- Evaluating Equity in Usage and Effectiveness of the CONCERN Early Warning System.Applied clinical informatics · 2025Article
- Re-identification of patients from imaging features extracted by foundation models.NPJ digital medicine · 2025Article
- Pain Assessment in Osteoarthritis: Present Practices and Future Prospects Including the Use of Biomarkers and Wearable Technologies, and AI-Driven Personalized Medicine.Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2025Review
- A scoping review and evidence gap analysis of clinical AI fairness.NPJ digital medicine · 2025Article
- The need for epistemic humility in AI-assisted pain assessment.Medicine, health care, and philosophy · 2025Article
- Can AI reveal the next generation of high-impact bone genomics targets?Bone reports · 2025Review
50 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors at 5 institutions in 1 country.
Funding
Abstract
Underserved populations experience higher levels of pain. These disparities persist even after controlling for the objective severity of diseases like osteoarthritis, as graded by human physicians using medical images, raising the possibility that underserved patients' pain stems from factors external to the knee, such as stress. Here we use a deep learning approach to measure the severity of osteoarthritis, by using knee X-rays to predict patients' experienced pain. We show that this approach dramatically reduces unexplained racial disparities in pain. Relative to standard measures of severity graded by radiologists, which accounted for only 9% (95% confidence interval (CI), 3-16%) of racial disparities in pain, algorithmic predictions accounted for 43% of disparities, or 4.7× more (95% CI, 3.2-11.8×), with similar results for lower-income and less-educated patients. This suggests that much of underserved patients' pain stems from factors within the knee not reflected in standard radiographic measures of severity. We show that the algorithm's ability to reduce unexplained disparities is rooted in the racial and socioeconomic diversity of the training set. Because algorithmic severity measures better capture underserved patients' pain, and severity measures influence treatment decisions, algorithmic predictions could potentially redress disparities in access to treatments like arthroplasty.
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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.