Full-Self Diagnostics (FSD): Physics-Grounded Visual Biomarker Inference from Smartphone Video via Inverse Problems and Operator Learning
arXiv:2606.19372v1 Announce Type: cross Abstract: We present Full-Self Diagnostics (FSD), a unified mathematical framework for recovering latent physiological states from unconstrained 9-second facial videos captured by consumer smartphones. The approach integrates five mutually reinforcing components: (1) a physics-based forward model derived from the radiative transfer equation and chromophore absorption that maps camera observables to biomarker concentrations; (2) an information-theoretic observability theory proving that multi-channel visual signals (spectral, pulse, respiratory, micro-exp
Advances in AI, particularly in computer vision and inverse problems, are enabling sophisticated biomarker inference from everyday devices, pushing healthcare monitoring towards widespread accessibility.
This development represents a significant step towards ubiquitous, non-invasive health monitoring, potentially democratizing access to diagnostic tools previously confined to clinical settings and enabling new preventative healthcare models.
The capability to infer complex physiological states from smartphone videos fundamentally changes how individuals can monitor their health, moving diagnostics from specialized equipment to personal devices.
- · Digital health platforms
- · Smartphone manufacturers
- · Preventative healthcare providers
- · Individuals
- · Traditional diagnostic equipment manufacturers
- · Clinic-dependent health monitoring services
Smartphones gain advanced diagnostic capabilities for vital health metrics from facial video.
The cost and accessibility of basic health monitoring decrease dramatically, leading to earlier detection of health issues.
New regulatory frameworks and ethical considerations arise concerning data privacy and diagnostic accuracy of consumer devices, potentially reshaping healthcare policy.
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