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Studies demonstrate systematic underestimation of illness severity, diagnostic inaccuracies, and measurement bias affecting ethnic minority groups, rural populations, people with disabilities, people experiencing homelessness, and other groups with limited access to health care. 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 and a lack of accountability measures for adherence to regulatory frameworks within National Health Service procurement processes. In this Viewpoint, we argue that ensuring accountability in digital health care requires transparent reporting of subgroup performance, representative development of datasets, and ongoing monitoring of distributional impacts. Achieving equitable and reliable digital health care demands regulatory and methodological standards that prioritize fairness and generalizability within the commercialization sector and are monitored across the pre- and postdeployment life cycle.<\/jats:p>","DOI":"10.2196\/100654","type":"journal-article","created":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T17:55:08Z","timestamp":1785520508000},"page":"e100654-e100654","source":"Crossref","is-referenced-by-count":0,"title":["When Innovation Leaves People Behind: Reframing Accountability in Commercial Digital 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