Semiconductor Technologies for Predictive, Preventive, and Personalized Healthcare.
9 affiliations
- 1Doctor of Business Administration Program, International American University, Los Angeles, CA, USA.
- 2Department of Marketing, Montclair State University, Montclair, NJ, USA.
- 3Miyan Research Institute, International University of Business Agriculture and Technology, Dhaka, Bangladesh.
- 4Faculty of Graduate Studies, University of Kelaniya, Colombo, Sri Lanka.
- 5Department of Business Administration, Westcliff University, Irvine, CA, USA.
- 6Department of Engineering Management, Trine University, Angola, IN, USA.
- 7Department of Science in Health Care Management, St. Francis College, Brooklyn, NY, USA.
- 8Department of Business Administration, International American University, Los Angeles, CA, USA.
- 9Department of Vehicles Engineering, University of Debrecen, Debrecen, Hungary.
Abstract
Purpose
This narrative review investigated how semiconductor technologies contribute to predictive, preventive, and personalized healthcare and considered their clinical value, technological maturity, and implementation challenges.Methods
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.Results
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.Conclusion
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.
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BibTeX
@article{toma2026semiconductor,
title = {{Semiconductor Technologies for Predictive, Preventive, and Personalized Healthcare.}},
author = {Jasmin Akter Toma and Samia Mahmuda and Moustaq Karim Khan Rony and Ashok Ghimire and S M Mainul Islam and Lamiya Rahman Mim and Md Abu Bakkar Siddique and Al Modabbir Zaman and Jakir Hossain Ridoy and Timotei István Erdei},
journal = {Journal of multidisciplinary healthcare},
year = {2026},
month = oct,
doi = {10.2147/jmdh.s647510},
url = {https://europepmc.org/article/MED/42928889},
note = {CC BY-NC},
}
RIS
TY - JOUR
TI - Semiconductor Technologies for Predictive, Preventive, and Personalized Healthcare.
AU - Jasmin Akter Toma
AU - Samia Mahmuda
AU - Moustaq Karim Khan Rony
AU - Ashok Ghimire
AU - S M Mainul Islam
AU - Lamiya Rahman Mim
AU - Md Abu Bakkar Siddique
AU - Al Modabbir Zaman
AU - Jakir Hossain Ridoy
AU - Timotei István Erdei
PY - 2026
DA - 2026/10/21
JO - Journal of multidisciplinary healthcare
DO - 10.2147/jmdh.s647510
UR - https://europepmc.org/article/MED/42928889
AB - Purpose This narrative review investigated how semiconductor technologies contribute to predictive, preventive, and personalized healthcare and considered their clinical value, technological maturity, and implementation challenges.Methods 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.Results 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.Conclusion 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.
SN - 1178-2390
ER -