ArticleNPJ digital medicine2025
Mitigating the risk of health inequity exacerbated by large language models.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed.
- Assessing and mitigating demographic bias in large language models for diagnostic radiology.Japanese journal of radiology · 2026Article
- Large language models in oncology: promise, pitfalls, and the path to real-world adoption.ESMO real world data and digital oncology · 2026Article
- Social Status and Clinical Resource Allocation by a Large Language Model: An Evaluation of 30,618 Decisions.Journal of personalized medicine · 2026Article
- A Locally Executable AI System for Improving Preoperative Patient Communication: Multidomain Clinical Evaluation.JMIR medical informatics · 2026Article
- An Acceptance Criteria Framework for Determining the Implementation Fit of Custom Large Language Models in Public Health Interventions.Journal of medical Internet research · 2026Article
- BRIDGE: benchmarking large language models for understanding real-world clinical practice texts.Nature biomedical engineering · 2026Article
- Ethical Governance of Large Language Models in Health Care: Trust, Responsibility, and Equity in Routine Use.Journal of medical Internet research · 2026Article
- The Double-Edged Role of Social Media in Modern Healthcare: Awareness, Impact, and Misinformation.Journal of immunotherapy and precision oncology · 2026Review
- Impact of Query Language on the Structure and Guideline Alignment of AI-Generated Rehabilitation Programs in Chronic Kidney Disease.Journal of clinical medicine · 2026Article
- Physicians as context engineers in the era of generative AI.Nature medicine · 2026Article
- A data-centric approach to detecting and mitigating demographic bias in pediatric mental health text.Communications medicine · 2026Article
- AI vs. MD: Benchmarking ChatGPT and Gemini for Complex Wound Management.Journal of clinical medicine · 2025Article
- Predicting hospice eligibility among dementia patients using language models.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Evaluating anti-LGBTQIA+ medical bias in large language models.PLOS digital health · 2025Article
- Challenges of Implementing LLMs in Clinical Practice: Perspectives.Journal of clinical medicine · 2025Article
- The Role of Large Language Models (LLMs) in Hepato-Pancreato-Biliary Surgery: Opportunities and Challenges.Cureus · 2025Article
- Towards Safe AI Clinicians: A Comprehensive Study on Large Language Model Jailbreaking in Healthcare.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Recent advancements in large language models (LLMs) have demonstrated their potential in numerous medical applications, particularly in automating clinical trial matching for translational research and enhancing medical question-answering for clinical decision support. However, our study shows that incorporating non-decisive socio-demographic factors, such as race, sex, income level, LGBT+ status, homelessness, illiteracy, disability, and unemployment, into the input of LLMs can lead to incorrect and harmful outputs. These discrepancies could worsen existing health disparities if LLMs are broadly implemented in healthcare. To address this issue, we introduce EquityGuard, a novel framework designed to detect and mitigate the risk of health inequities in LLM-based medical applications. Our evaluation demonstrates its effectiveness in promoting equitable outcomes across diverse populations.
Identifiers
What OpenQuestion holds
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.