AI-Enabled Self-Testing and Wearables: The Future of Diagnosis and Health Monitoring

Self-testing and wearable sensing are extending health assessment beyond the clinic. Their convergence with artificial intelligence (AI) raises a fundamental question: how can increasingly autonomous technologies strengthen human agency while delivering clinically meaningful data and/or care? This presentation critically examines that transition through infectious-disease diagnostics, with comparative insights from chronic-disease monitoring and implications for public-health resilience.

Drawing on evidence from HIV and hepatitis C self-testing, respiratory-virus diagnostics and wearable devicesstudies, the discussion distinguishes established clinical applications from emerging capabilities. AI-assisted smartphone interpretation of rapid tests can support consistent reading and digital reporting, while algorithms applied to wearable physiological data may identify deviations from an individual’s baseline and prompt targeted testing. These functions remain distinct: an algorithmic alert is not a pathogen-specific diagnosis, and reactive HIV or hepatitis C self-tests require further testing within validated diagnostic pathways. Continuous glucose monitoring provides a complementary example of how longitudinal measurements can inform ongoing care.

The presentation proposes an integrated pathway connecting home testing and physiological monitoring with validated interpretation, appropriate diagnostic confirmation, clinical assessment and follow-up. Its value should be judged through time to diagnosis and treatment, successful linkage to care, patient experience and equitable access. Particular attention is given to diagnostic window periods, false reassurance, false alerts and the effect of disease prevalence on predictive value. Prospective evaluation in diverse populations is essential before promising detection performance can be translated into claims of clinical benefit or population-level impact.

Within the conference’s themes of human-machine co-evolution, biological resilience and algorithmic governance, this pathway also exposes a redistribution of responsibility and power. Individuals may gain privacy and control through self-testing while becoming dependent on opaque algorithms and commercial data infrastructures. Meaningful consent, proportionate data use and accountable clinical oversight must therefore accompany technical validation. Affordable access and support for limited digital or health literacy are necessary to prevent innovation from widening existing inequalities.
The central argument is that the future of diagnosis depends on connecting reliable measurements to accessible care while preserving the person’s authority over health decisions and data. A model of hybrid intelligence, combining patient knowledge, laboratory evidence and clinical judgement with validated computational support, offers a practical basis for more participatory healthcare and resilient health systems in the posthuman age.

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