review · Journal of Personalized Medicine
Artificial intelligence is increasingly enhancing wearable sensors and point-of-care testing kits to support personalised healthcare. By integrating intelligent algorithms with medical devices, these systems enable continuous physiological tracking, rapid diagnostics, and real-time data analysis. Non-invasive wearable sensors track biological signals to support early disease detection and tailored treatment strategies. Concurrently, intelligent point-of-care diagnostics provide accurate, rapid testing that remains accessible in resource-limited environments. Key technological developments include advanced data processing, sensor fusion, and multivariate analytics applied across diverse clinical scenarios. However, the wider adoption of these intelligent medical technologies faces substantial hurdles, particularly regarding data privacy, regulatory approvals, and integration into existing healthcare frameworks. Continued innovation is necessary to navigate these regulatory and technical barriers and fully implement smart diagnostic tools in everyday medical practice.
Combining smart algorithms with wearable devices and portable diagnostic kits can transform patient care by making vital health tracking continuous and accessible outside traditional hospitals. This approach allows earlier detection of illnesses and provides accurate diagnostic capabilities in remote or low-resource settings, ultimately helping healthcare providers deliver faster, tailored treatments whilst improving overall system efficiency.
The technologies described offer practical applications in non-invasive patient monitoring and decentralised medical testing, serving clinical practitioners and community healthcare workers. Because this overview highlights unresolved challenges around regulatory approvals, data privacy, and hospital system integration, the technologies range from applied research prototypes to early-stage deployments that require formal regulatory compliance before widespread commercial use in mainstream healthcare environments.
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Artificial intelligence (AI) techniques offer great potential to advance point-of-care testing (POCT) and wearable sensors for personalized medicine applications. This review explores the recent advances and the transformative potential of the use of AI in improving wearables and POCT. The integration of AI significantly contributes to empowering these tools and enables continuous monitoring, real-time analysis, and rapid diagnostics, thus enhancing patient outcomes and healthcare efficiency. Wearable sensors powered by AI models offer tremendous opportunities for precise and non-invasive tracking of physiological conditions that are essential for early disease detection and personalized treatments. AI-empowered POCT facilitates rapid, accurate diagnostics, making these medical testing kits accessible and available even in resource-limited settings. This review discusses the key advances in AI applications for data processing, sensor fusion, and multivariate analytics, highlighting case examples that exhibit their impact in different medical scenarios. In addition, the challenges associated with data privacy, regulatory approvals, and technology integrations into the existing healthcare system have been overviewed. The outlook emphasizes the urgent need for continued innovation in AI-driven health technologies to overcome these challenges and to fully achieve the potential of these techniques to revolutionize personalized medicine.
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DOI: 10.3390/jpm14111088
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