Affiliation:
1. Laboratory of Advanced Biotechnologies for Health Assessments (Lab‐HA) Biomedical Engineering Program Lassonde School of Engineering York University Toronto M3J 1P3 Canada
2. Department of Electrical Engineering and Computer Science (EECS) Lassonde School of Engineering York University Toronto ON M3J 1P3 Canada
3. Department of Biology Faculty of Science York University Toronto ON M3J 1P3 Canada
4. Department of Ophthalmology and Vision Sciences University of Toronto Ontario M5T 3A9 Canada
5. Institute of Health Policy Management and Evaluation University of Toronto Ontario M5T 3M6 Canada
Abstract
AbstractEarly‐stage disease detection, particularly in Point‐Of‐Care (POC) wearable formats, assumes pivotal role in advancing healthcare services and precision‐medicine. Public benefits of early detection extend beyond cost‐effectively promoting healthcare outcomes, to also include reducing the risk of comorbid diseases. Technological advancements enabling POC biomarker recognition empower discovery of new markers for various health conditions. Integration of POC wearables for biomarker detection with intelligent frameworks represents ground‐breaking innovations enabling automation of operations, conducting advanced large‐scale data analysis, generating predictive models, and facilitating remote and guided clinical decision‐making. These advancements substantially alleviate socioeconomic burdens, creating a paradigm shift in diagnostics, and revolutionizing medical assessments and technology development. This review explores critical topics and recent progress in development of 1) POC systems and wearable solutions for early disease detection and physiological monitoring, as well as 2) discussing current trends in adoption of smart technologies within clinical settings and in developing biological assays, and ultimately 3) exploring utilities of POC systems and smart platforms for biomarker discovery. Additionally, the review explores technology translation from research labs to broader applications. It also addresses associated risks, biases, and challenges of widespread Artificial Intelligence (AI) integration in diagnostics systems, while systematically outlining potential prospects, current challenges, and opportunities.
Funder
York University
Natural Sciences and Engineering Research Council of Canada