Context-aware monitoring: rethinking comprehensive screening in the era of AI

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Authors: David A. Shaywitz, Nathan D. Price, Daniel K. Sodickson
Cited by: 1
References: 20

Abstract

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. We describe early illustrations from imaging and molecular diagnostics and discuss challenges including cost, anxiety, liability, and equity.

Fields of Study & Subjects

Artificial Intelligence in Healthcare and Education COVID-19 diagnosis using AI Machine Learning in Healthcare

Publication details

Journal / source
npj Digital Medicine
Publisher
Springer Science and Business Media LLC
Volume
9
Issue
1
Publication date
12 August 2026
Language
EN