Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 3 appraisals
Journal of the American Medical Informatics Association : JAMIA
Sociodemographic bias in large language model clinical trial screening
OBJECTIVE: To assess whether large language model (LLM)-based clinical trial screening judgments vary by patient sociodemographic characteristics. MATERIALS AND METHODS: We conducted a cross-sectional evaluation of Phase II-III US adult randomized controlled trial (RCT) protocols (2023-2024). Physician-validated clinical vignettes were evaluated in a control version and 33 sociodemographic identity variants differing only by labels. Nine LLMs assessed eligibility and related domains. Mixed-effects models estimated adjusted differences vs control. RESULTS: Across 58 protocols and 5.3 million evaluations, eligibility judgments were largely stable across identities. Race and ethnicity showed minimal effects after accounting for socioeconomic status. Homelessness produced the largest negative eligibility shift and pronounced effects in adherence, resources, and trust. DISCUSSION AND CONCLUSION: LLMs applied explicit eligibility criteria consistently, but disparities emerged in domains requiring inference about behavior or resources, underscoring the need for careful deployment to promote fair trial access.
2 Aug 2026
Read appraisal →Balkan medical journal
Bias and Fairness Across the Healthcare AI Lifecycle: A Clinician-Oriented Review
Artificial intelligence (AI) is increasingly being investigated and, in selected clinical settings, implemented to support diagnosis, triage, and workflow optimization. Although these systems have the potential to improve access, consistency, and efficiency, they may also reproduce or amplify health inequities when bias is introduced during development, evaluation, implementation, or postdeployment use. This clinician-oriented narrative review adopts a practical lifecycle approach to explain how algorithmic unfairness becomes clinically relevant, how clinicians can recognize it, and how institutions can mitigate its impact. We first outline the ethical, clinical, and mathematical dimensions of fairness. We then examine fairness risks and sources of bias across six stages of the healthcare AI lifecycle: problem formulation, data generation, model development, evaluation, implementation, and postdeployment monitoring and governance. Key mechanisms include biased proxy outcomes, unrepresentative or error-prone data and labels, model shortcut learning, hidden stratification, distribution shift, and human-AI interaction effects (e.g., automation bias and alert fatigue), all of which can create feedback loops and contribute to fairness drift over time. For each stage, we identify clinician-facing red flags and practical mitigation strategies, including defining clinically meaningful outcomes, using representative and well-documented datasets, conducting subgroup-stratified evaluations, performing external and prospective validation, justifying decision thresholds, implementing safeguards for human-AI interactions, and maintaining continuous postdeployment monitoring, including postmarket surveillance for regulated medical devices. Fairness cannot be ensured through a single metric, publication, regulatory clearance, or one-time validation. Instead, equitable healthcare AI requires transparent design, rigorous evaluation, local governance, and ongoing monitoring across diverse populations, clinical sites, devices, workflows, and time. Fairness should therefore be regarded as a continuous clinical and institutional responsibility rather than a downstream technical consideration.
1 Aug 2026
Read appraisal →Journal of the American Heart Association
Social Determinants of Health and Cardiovascular Disease-Related Outcome Disparities Among Breast Cancer Survivors: A Systematic Review
BACKGROUND: Breast cancer is the most commonly diagnosed malignancy worldwide. Breast cancer survivors face an increased risk of cardiovascular disease (CVD), a leading cause of death in this group. Social determinants of health (SDoHs), operationalized as economic, environmental, and psychosocial factors, play an important role in CVD disparities. However, few studies have examined how SDoHs are associated with CVD disparities in this population, and no systematic review has addressed their multilevel influences. METHODS: This systematic review summarizes the current evidence on the relationships between SDoHs at different levels and CVD disparities among breast cancer survivors. Using the 2024 American College of Cardiology/American Heart Association framework, SDoHs were categorized at the individual, interpersonal, or community levels. Studies published in peer-reviewed journals between January 1, 2010, and November 30, 2025, were identified through searching electronic databases (PubMed, Embase, Cumulative Index to Nursing and Allied Health Literature, Web of Science, PsycINFO) and citations. RESULTS: Of 6550 unique records, 37 articles that addressed the impact of SDoHs on CVD outcomes were selected. Most (n=31) were conducted in the United States and used retrospective designs (n=29). Most (n=30) focused on individual-level SDoHs such as race, income, or education; 2 on interpersonal-level SDoHs (ie, psychosocial stress); and 13 on community-level SDoHs, including neighborhood socioeconomic status and residential area. Black race, lower neighborhood socioeconomic status, and rural residence were associated with a higher incidence of CVD and increased cardiovascular death. CONCLUSIONS: This review highlights the urgent need to address SDoHs and emphasizes the importance of multilevel interventions to reduce CVD disparities among breast cancer survivors.
9 July 2026
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