Bias and Fairness Across the Healthcare AI Lifecycle: A Clinician-Oriented Review

Clinical Snapshot

Key Findings

• Effect Size: Not applicable — narrative review; no quantitative effect sizes reported

• Primary Outcome: A structured lifecycle framework identifying fairness risks, clinician-facing red flags, and practical mitigation strategies across six stages of healthcare AI development and deployment

• Nnt Or Sensitivity: Not applicable — conceptual and educational review; no diagnostic test accuracy, therapeutic effect, or prognostic hazard ratios reported

• Confidence Interval: Not applicable — no quantitative analyses performed

Clinical Application

The lifecycle framework and red flag checklists are designed for practical clinical and institutional use without requiring deep technical expertise. Implementation feasibility is high for governance and evaluation stages but may require dedicated informatics or data science support for postdeployment monitoring and subgroup-stratified analyses. Resource constraints in smaller or regional health services may limit continuous monitoring capacity. Highly relevant to Australian practice. The Therapeutic Goods Administration (TGA) regulates AI-based Software as a Medical Device (SaMD) under the Digital Health framework, and the review's emphasis on postmarket surveillance aligns directly with TGA postmarket monitoring obligations. The Australian Commission on Safety and Quality in Health Care (ACSQHC) and RACGP have both identified AI governance as a priority area. The review's recommendations for subgroup-stratified evaluation are particularly pertinent in Australia given the need to ensure AI systems perform equitably across Aboriginal and Torres Strait Islander populations, culturally and linguistically diverse communities, and rural and remote patients — groups historically underrepresented in AI training datasets. PBS and MBS implications are indirect but relevant as AI tools increasingly support prescribing, diagnostic imaging, and pathology workflows funded under these schemes. Clinicians in any specialty who use, evaluate, procure, or govern AI-assisted clinical decision support tools; hospital administrators, clinical informatics leads, ethics committees, and quality improvement teams; medical educators developing AI literacy curricula

Abstract

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.

References

1. 1.Kocak, B., Ponsiglione, A., Dang, V. N., Dietzel, M., Lekadir, K., & Cuocolo, R. (2026). Bias and fairness across the healthcare AI lifecycle: A clinician-oriented review. Balkan Medical Journal. Advance online publication. https://doi.org/10.4274/balkanmedj.galenos.2026.2026-6-3