Context-aware monitoring: rethinking comprehensive screening in the era of AI
David A. Shaywitz, Nathan D. Price, Daniel K. Sodickson · 2026
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Description
Applying advanced measurement technologies proactively in asymptomatic populations predictably yields false positives, as a consequence of Bayes’ Theorem. Yet the same Bayesian arithmetic suggests a remedy: adding context. Serial and multimodal measurements, integrated using context-aware AI that prioritizes within-person change over population norms, can reduce false positive rates while preserving sensitivity.
Catalog record
| CREATOR | David A. Shaywitz, Nathan D. Price, Daniel K. Sodickson |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 19 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |