JMIR Publications

ABSTRACT

Digital technologies are increasingly integrated across healthcare systems, yet accumulating evidence shows that these tools often perform unevenly across population groups. Biases in artificial intelligence‑enabled tools can reinforce existing health inequities, particularly when systems are developed and validated using datasets that exclude underserved populations. Studies demonstrate systematic underestimation of illness severity, diagnostic inaccuracies, and measurement bias affecting ethnic minority groups, rural populations, people with disabilities, homeless individuals, and other groups with limited access to healthcare. These disparities highlight structural vulnerabilities in the data that inform machine learning systems, where socially patterned access to care shapes what is recorded and therefore learned. Commercial adoption patterns further exacerbate inequities due to socioeconomic gradients in digital engagement. Ensuring accountability in digital health requires transparent reporting of subgroup performance, representative development of datasets, and ongoing monitoring of distributional impacts. Achieving equitable and reliable digital healthcare demands regulatory and methodological standards that prioritise fairness and generalisability.