ReviewBiomedical engineering letters2025
Artificial intelligence in Chinese healthcare: a review of applications and future prospects.
Review in Biomedical engineering letters, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning models for predicting postoperative acute kidney injury in pediatric cardiac surgery: a systematic review and meta-analysis.Frontiers in cardiovascular medicine · 2026Pooled it
- Hospital Human Resource Managers' Perspectives on Organizational Readiness for Generative AI Skills: Qualitative Descriptive Study.JMIR medical informatics · 2026Article
- From Pilot Trap to Institutional Capacity: A Governance Framework for Sustainable Clinical AI Implementation in Health Systems.Journal of medical Internet research · 2026Article
- Artificial intelligence in neurology practice: promise, perils, and a roadmap for responsible integration.Journal of neurology · 2026Review
- Clinical Artificial Intelligence Implementation in Routine Care: Real-World Operational Outcomes from a Provincial Health System in China.Risk management and healthcare policy · 2026Article
- Artificial Intelligence in Medication Adherence: A National Assessment of Knowledge, Attitudes, and Perceptions Among Chronic Disease Patients in Jordan.Patient preference and adherence · 2026Article
- AI-Driven Innovations in Transfusion Medicine: A Narrative Synthesis of Current Reviews.Medical sciences (Basel, Switzerland) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
China's healthcare infrastructure faces growing population pressure and resource gaps. This review explores how AI applications, regulatory frameworks, and commercialization pathways are reshaping China's healthcare delivery system and global innovation standards. China's AI healthcare market is expected to grow from $900 million in 2020 to $1.59 billion in 2023, and is expected to reach $18.88 billion by 2030, at a Compound Annual Growth Rate (CAGR) of 42.5%. The National Medical Products Administration (NMPA) expects to approve 59 Class III AI devices by 2023, compared with just nine in 2020. Key applications include the widespread use of AI technology in lesion identification; a telemedicine platform serving 13 million users; and AI drug development that shortens the development cycle from 4 to 18 months. Regulatory pillars include the Personal Information Protection Law, which requires explicit consent before processing health data, and NMPA guidelines, which require all AI medical software to undergo three types of review. China's unique combination of centralized health data, policy incentives, and rapid commercialization has created a globally competitive AI medical ecosystem. Continued development requires addressing issues such as algorithm transparency, cross-border data governance, and international regulatory coordination.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.