ArticleCommunications medicine2025
Physician clinical decision modification and bias assessment in a randomized controlled trial of AI assistance.
Article in Communications medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Telemedicine and artificial intelligence in family medicine practice: a systematic review and meta-analysis of barriers and enablers in routine primary care.Frontiers in digital health · 2026Pooled it
- AI-Based EMG Reporting: A Randomized Controlled Trial.Journal of neurology · 2025Trial
- Performance of large language models in ambiguous treatment scenarios of early breast cancer.Breast (Edinburgh, Scotland) · 2026Article
- AI for Health Care Quality and Patient Safety: Scoping Review of Diagnostic, Predictive, and Decision Support Applications.Journal of medical Internet research · 2026Article
- A Self-Controlled Benchmark of Retrieval-Augmented Generation for Large Language Models on Clinical Guideline Questions.Diagnostics (Basel, Switzerland) · 2026Article
- Clinical large language model centered on electronic medical records.NPJ digital medicine · 2026Article
- The evolving physician-AI relationship: a five-tier framework for integrating intelligent systems into clinical practice and medical education.ESMO real world data and digital oncology · 2026Review
- Performance Comparison of a Neuro-Symbolic Large Language Model System Versus Human Experts in Acute Cholecystitis Management.Journal of clinical medicine · 2026Article
- Is Artificial Intelligence Ready for Emergency Department Triage? A Retrospective Evaluation of Multiple Large Language Models in 39,375 Patients at a University Emergency Department.Journal of clinical medicine · 2026Article
- The dark side of Gen-AI-assisted clinical work collaboration: work alienation, trait emotional intelligence, and instigated interprofessional incivility.Frontiers in psychology · 2026Article
- Benchmarking large language models and clinicians using locally generated primary healthcare vignettes in Kenya.BMJ digital health & AI · 2026Article
- A roadmap for medical large language models: a review of foundations, applications, and challenges.Military Medical Research · 2026Review
- Artificial intelligence in cardiology: implications for healthcare outcomes.Frontiers in artificial intelligence · 2026Review
- A generative two-stage semantic intermediary framework for explainable mental health early warning in higher education.Frontiers in psychiatry · 2026Review
- Artificial intelligence in acute and critical care: current challenges and strategic solutions.Frontiers in public health · 2026Review
- Large language models for clinical decision support in gastroenterology and hepatology.Nature reviews. Gastroenterology & hepatology · 2025Review
- Article
- Artificial intelligence in maxillofacial trauma: expert ally or unreliable assistant?Medicina oral, patologia oral y cirugia bucal · 2025Article
- Framework for bias evaluation in large language models in healthcare settings.NPJ digital medicine · 2025Article
- Influences on trust in the use of AI-based triage-an interview study with primary healthcare professionals and patients in Sweden.Frontiers in digital health · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
backgroundArtificial intelligence assistance in clinical decision making shows promise, but concerns exist about potential exacerbation of demographic biases in healthcare. This study aims to evaluate how physician clinical decisions and biases are influenced by AI assistance in a chest pain triage scenario.
methodsA randomized, pre post-intervention study was conducted with 50 US-licensed physicians who reviewed standardized chest pain video vignettes featuring either a white male or Black female patient. Participants answered clinical questions about triage, risk assessment, and treatment before and after receiving GPT-4 generated recommendations. Clinical decision accuracy was evaluated against evidence-based guidelines.
resultsHere we show that physicians are willing to modify their clinical decisions based on GPT-4 assistance, leading to improved accuracy scores from 47% to 65% in the white male patient group and 63% to 80% in the Black female patient group. The accuracy improvement occurs without introducing or exacerbating demographic biases, with both groups showing similar magnitudes of improvement (18%). A post-study survey indicates that 90% of physicians expect AI tools to play a significant role in future clinical decision making.
conclusionsPhysician clinical decision making can be augmented by AI assistance while maintaining equitable care across patient demographics. These findings suggest a path forward for AI clinical decision support that improves medical care without amplifying healthcare disparities.
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.