ArticleCommunications medicine2022
Mitigating the impact of biased artificial intelligence in emergency decision-making.
Article in Communications medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- AI applications in disaster governance with health approach: A scoping review.Archives of public health = Archives belges de sante publique · 2025Pooled it
- Effects of Type and Timing of Clinician-Facing AI Support on Patient Trust in Medical Consultations: 2 Vignette Experiments.Journal of medical Internet research · 2026Article
- Do AI-based contouring algorithms influence physicians in the online adaptive radiotherapy of patients with bladder cancer?Technical innovations & patient support in radiation oncology · 2026Article
- Evaluating the Potential of Reasoning Large Language Models to Perpetuate Racial and Gender Disease Stereotypes in Health Care.Journal of medical Internet research · 2026Article
- Factors influencing the effectiveness of artificial intelligence-assisted decision-making in medicine: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Warning people about the risk of AI error mitigates human acquisition of AI bias.Cognitive research: principles and implications · 2026Article
- Connecting algorithmic fairness and fair outcomes in a sociotechnical simulation case study of AI-assisted healthcare.Nature communications · 2025Article
- Artificial intelligence in African malaria control programs: opportunities and risks.Annals of medicine and surgery (2012) · 2025Review
- Impacts of cognitive forcing and need for cognition on biased AI-assisted decision making about mental health emergencies.Scientific reports · 2025Article
- Comparing Generative Artificial Intelligence and Mental Health Professionals for Clinical Decision-Making With Trauma-Exposed Populations: Vignette-Based Experimental Study.JMIR mental health · 2025Article
- Factors Influencing the Effectiveness of AI-Assisted Decision-Making in Medicine: A Scoping Review.medRxiv : the preprint server for health sciences · 2025Article
- Potential to perpetuate social biases in health care by Chinese large language models: a model evaluation study.International journal for equity in health · 2025Article
- A scoping review and evidence gap analysis of clinical AI fairness.NPJ digital medicine · 2025Article
- Clinical Impact of Artificial Intelligence-Based Triage Systems in Emergency Departments: A Systematic Review.Cureus · 2025Review
- Effectiveness of generative AI-large language models' recognition of veteran suicide risk: a comparison with human mental health providers using a risk stratification model.Frontiers in psychiatry · 2025Article
- Article
- Ethical Considerations in Human-Centered AI: Advancing Oncology Chatbots Through Large Language Models.JMIR bioinformatics and biotechnology · 2024Article
- Patient attitudes toward the AI doctor.Nature medicine · 2024Article
- Explainable AI decision support improves accuracy during telehealth strep throat screening.Communications medicine · 2024Article
- Machine learning for healthcare that matters: Reorienting from technical novelty to equitable impact.PLOS digital health · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPrior research has shown that artificial intelligence (AI) systems often encode biases against minority subgroups. However, little work has focused on ways to mitigate the harm discriminatory algorithms can cause in high-stakes settings such as medicine.
methodsIn this study, we experimentally evaluated the impact biased AI recommendations have on emergency decisions, where participants respond to mental health crises by calling for either medical or police assistance. We recruited 438 clinicians and 516 non-experts to participate in our web-based experiment. We evaluated participant decision-making with and without advice from biased and unbiased AI systems. We also varied the style of the AI advice, framing it either as prescriptive recommendations or descriptive flags.
resultsParticipant decisions are unbiased without AI advice. However, both clinicians and non-experts are influenced by prescriptive recommendations from a biased algorithm, choosing police help more often in emergencies involving African-American or Muslim men. Crucially, using descriptive flags rather than prescriptive recommendations allows respondents to retain their original, unbiased decision-making.
conclusionsOur work demonstrates the practical danger of using biased models in health contexts, and suggests that appropriately framing decision support can mitigate the effects of AI bias. These findings must be carefully considered in the many real-world clinical scenarios where inaccurate or biased models may be used to inform important decisions.
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.