Self-powered intelligence for personalized healthcare
Abstract Personalized healthcare is crucial for transforming medical practice, offering more precise, efficient, and patient-centred care. It not only improves individual health outcomes but also enhances the overall efficiency of healthcare systems. Wearable and implantable devices have emerged as key technologies in this transformation, offering unprecedented opportunities for real-time physiological monitoring, early disease detection, and personalized health intervention. In parallel, the rapid advancement of artificial intelligence (AI) has further accelerated the integration of data-driven intelligence into healthcare systems, making interdisciplinary research at the intersection of AI, flexible electronics, and biomedical engineering increasingly important. However, the long-term deployment of intelligent wearable and implantable healthcare systems remains fundamentally constrained by power sustainability, mechanical flexibility, and the energy cost of on-device data processing. This review focuses on self-powered intelligence as an emerging paradigm for personalized healthcare, enabling continuous, long-term and autonomous health monitoring beyond the limitations of battery-powered systems. By integrating flexible and smart electronics with low-power AI, self-powered intelligence provide a viable pathway toward intelligent, on-body and in-body healthcare platforms capable of real-time analysis and personalized health management.
Authors
- Eric M. Yeatman (ORCID: https://orcid.org/0000-0003-0487-2693)
- J C Chen
- Yeyun Cai (ORCID: https://orcid.org/0000-0003-3011-8280)
- Ruizhe Hou
- Ziyue Kong (ORCID: https://orcid.org/0009-0007-5972-8177)
- Hailing Fu
- Chengkuo Lee
- Fang Deng
- Ning Ding
- Pengfei Jin
- Xiangyang Wang
Institutions
- Beijing Institute of Technology (CN)
- National University of Singapore (SG)
- Harbin Institute of Technology (CN)
- NIHR Imperial Biomedical Research Centre (GB)
- Beijing Research Institute of Mechanical and Electrical Technology (CN)
- Imperial College London (GB)
- University of Glasgow (GB)
Publication Details
- Journal
- National Science Review
- Published
- 2026-05-27
- DOI
- https://doi.org/10.1093/nsr/nwag317
- Primary Topic
- Advanced Sensor and Energy Harvesting Materials
- Type
- article
- Field-Weighted Citation Impact
- 0.00