Evidence map›Paper›PMID 42358606›Full record

ReviewNational science review2026

Self-powered intelligence for personalized healthcare.

Yeyun Cai, Ziyue Kong, Pengfei Jin, Xiangyang Wang, Ruizhe Hou, Ning Ding, Hailing Fu, Eric M Yeatman, Chengkuo Lee, Fang Deng and 1 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in National science review, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Yeyun CaiNational Key Lab of Autonomous Intelligent Unmanned Systems, Beijing Institute of Technology, Beijing 100081, China.ORCID https://orcid.org/0000-0003-3011-8280
Ziyue KongNational Key Lab of Autonomous Intelligent Unmanned Systems, Beijing Institute of Technology, Beijing 100081, China.
Pengfei JinNational Key Lab of Autonomous Intelligent Unmanned Systems, Beijing Institute of Technology, Beijing 100081, China.
Xiangyang WangNational Key Lab of Autonomous Intelligent Unmanned Systems, Beijing Institute of Technology, Beijing 100081, China.
Ruizhe HouNational Key Lab of Autonomous Intelligent Unmanned Systems, Beijing Institute of Technology, Beijing 100081, China.
Ning DingNational Key Lab of Autonomous Intelligent Unmanned Systems, Beijing Institute of Technology, Beijing 100081, China.
Hailing FuNational Key Lab of Autonomous Intelligent Unmanned Systems, Beijing Institute of Technology, Beijing 100081, China.
Eric M YeatmanCollege of Science and Engineering, University of Glasgow, Glasgow G12 8QQ, UK.
Chengkuo LeeDepartment of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore.
Fang DengNational Key Lab of Autonomous Intelligent Unmanned Systems, Beijing Institute of Technology, Beijing 100081, China.
Jie ChenNational Key Lab of Autonomous Intelligent Unmanned Systems, Beijing Institute of Technology, Beijing 100081, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Personalized healthcare is crucial for transforming medical practice, offering more precise, efficient, and patient-centerd 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.

Indexed as

energy harvestingflexible and smart electronicslightweight intelligencepersonalized healthcarewearable/implantable devices

Identifiers

PMID42358606
PMCPMC13292225

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.