Semiconductor Technologies for Predictive, Preventive, and Personalized Healthcare

This narrative review investigated how semiconductor technologies contribute to predictive, preventive, and personalized healthcare and considered their clinical value, technological maturity, and implementation challenges.

A structured narrative-review search of literature published from January 2015 through August 2026 was conducted in PubMed, Scopus, Web of Science, IEEE Xplore, and ScienceDirect. Eligible publications had to describe a semiconductor-based technology with a clear or potential healthcare application related to prediction, prevention, personalization, diagnosis, monitoring, or therapeutic decision-making. Forty-seven publications met the eligibility criteria and were critically appraised and synthesized thematically.

The evidence set included 47 publications published from 2016 to 2026. Major technology categories included wearable and flexible physiological sensors, biosensors and lab-on-chip systems, implantable devices, complementary metal oxide semiconductors and application-specific integrated-circuit platforms, edge processing hardware, and semiconductor imaging detectors. Across the evidence, semiconductor platforms supported continuous physiological monitoring, rapid biochemical and molecular detection, early risk identification, individualized treatment management, and local processing of health data. Signal instability, calibration drift, limited external validation, power constraints, biocompatibility, interoperability, cybersecurity, affordability, algorithmic bias, and unequal access remained important barriers.

Semiconductor technologies can strengthen predictive, preventive, and personalized healthcare, but technical performance alone is insufficient. Near-term translation is strongest for established sensing, imaging, and implantable platforms linked to defined care pathways, whereas flexible biochemical sensors and in-sensor computing platforms require stronger clinical validation. Wider adoption requires representative validation, long-term safety evidence, interoperable systems, equitable design, clear regulation, sustainable lifecycle planning, and demonstrated improvements in patient outcomes.