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...
| Publicado en: | Balkan Medical Journal Vol. 43; no. 8; pp. 418 - 434 |
|---|---|
| Autores principales: | , , , , , |
| Formato: | diagnostic images pictorial review tables/charts Journal Article |
| Publicado: |
Galenos Yayinevi Tic. LTD. STI
Aug2026
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=195788241&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 195788241 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 21463123 BM3Q jtl: Balkan Medical Journal issn: 21463123 maglogo: N pubinfo: dt: Aug2026 vid: 43 iid: 8 pid: 28155 pub: Galenos Yayinevi Tic. LTD. STI artinfo: ui: 195788241 195788241 195788241 10.4274/balkanmedj.galenos.2026.2026-6-3 195788241 ppf: 418 ppct: 16 formats: tig: atl: Bias and Fairness Across the Healthcare AI Lifecycle: A Clinician-Oriented Review. aug: au: Kocak, Burak Ponsiglione, Andrea Dang, Vien Ngoc Dietzel, Matthias Lekadir, Karim Cuocolo, Renato affil: Department of Radiology, Başakşehir Çam and Sakura City Hospital, İstanbul, Türkiye sug: subj: Health Care Industry Artificial Intelligence Ethical Issues Life Cycle Implicit Bias Clinical Reasoning Ethical Issues Physicians Psychosocial Factors Automation Workflow Models, Theoretical Mathematics Implementation Science Problem Solving Clinical Governance User-Computer Interface ab: 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. pubtype: Academic Journal doctype: diagnostic images pictorial review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|