Engineering innovations for precision medicine: sensors, AI biomarkers, and predictive interventions.
TL;DR
This paper summarizes recent advances in biosensor technologies, AI-derived biomarkers, and predictive frameworks used to help identify patients more accurately, monitor their progress continuously, and receive optimized therapies.
Abstract
Precision medicine unifies the latest capabilities from engineering, biotechnology, and artificial intelligence (AI) to deliver data-driven personalized healthcare. This paper summarizes recent advances in biosensor technologies, AI-derived biomarkers, and predictive frameworks used to help identify patients more accurately, monitor their progress continuously, and receive optimized therapies. Relevant peer-reviewed studies were identified in a systematic search of PubMed, Scopus, Web of Science, and Google Scholar for any articles published during the period of 1 January 2015 through 31 December 2024. Advanced biosensors provide immediate feedback regarding the molecular and physiological status of patients, allowing for the rapid definition of new biomarkers, as well as ongoing evaluations of their clinical status and response to treatments. Predictive AI models enhance the precision of patient stratification, treatment planning through adaptive therapies, and predicting individual drug responses, while also lessening the risk of adverse events from treatments. Several emerging technologies illustrate a demonstrated movement toward proactive precision medicine, such as pharmacovigilance based on smart biosensors and closed-loop delivery systems. Key barriers still exist, such as the need for interoperable standards, scientific validation of tools, legal applicability, and ethical issues; therefore, resolving these issues requires collaborative interdisciplinary teams conducting long-term clinical studies.