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Cited by 14Artificial Intelligence in Healthcare and EducationElectronic Health Records SystemsArtificial Intelligence in Healthcare

Artificial Intelligence Platform Architecture for Hospital Systems: Systematic Review

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Maimaitiaili, Musitapa, Jiamaliding, Yiershatijiang, Dai, Guangle, Xiao, Hui, Kuerbanjiang, Warisijiang, Yi, Yuexiong·2025·Journal of Medical Internet Research
DOI 10.2196/79788PMC PMC12710730
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Abstract

Background: The construction of artificial intelligence (AI) platforms in hospitals is the backbone of the revolution in health care. While traditional hospital information systems have facilitated digitalization, they are still limited by data silos, fragmented workflows, and insufficient clinical intelligence that impede organizations from realizing the promise of data-led decision-making. Objective: This study aimed to derive a hospital-specific 5-layer architecture (infrastructure, data, algorithm, application, and security and compliance) and to systematically review the evidence mapped onto the 5-layer framework to assess its applicability. Methods: A systematic literature search was performed in Web of Science, Embase, PubMed, and Scopus from inception to May 2025. The review followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Studies were screened and selected for full-text review by two independent reviewers. We included peer-reviewed empirical studies describing hospital-based AI implementations across clinical domains. Reviews, commentaries, and purely technical bench studies without hospital context and non-English literature were excluded. Quality assessment of the identified papers was conducted using the Critical Appraisal Skills Programme tool. Using a 0 to 5 point ordinal maturity scale of 5 layers, we conducted a structured mapping with quantitative mapping, weighted co-occurrence analysis, weighted Jaccard similarity, and thematic synthesis with examples. Results: In total, 29 studies met the eligibility criteria and included work specifically in emergency, radiology, routine imaging, chronic disease, and multihospital platform work, conducted in 11 countries. On average, the application (mean 3.17, SD 0.85) and data (mean 3.00, SD 0.76) layers demonstrated the highest maturity, followed by algorithm (mean 2.79, SD 0.77) and infrastructure (mean 2.79, SD 1.70). The security and compliance layer showed the lowest and most variable maturity (mean 1.69, SD 1.89). Weighted co-occurrence and Jaccard analyses revealed strong interconnections among data, algorithm, and application (Jaccard=0.80‐0.89), forming a technical core, whereas security and compliance exhibited weak alignment (0.43‐0.46). Conclusions: Our review excluded non-English and gray literature, which may limit comprehensiveness. The ordinal maturity scoring may still simplify the contextual complexity of hospital AI implementations. Our synthesis validates a 5-layer hospital AI platform architecture, grounded in both theoretical frameworks and empirical evidence. The findings highlight that while clinical feasibility is achievable, sustainable hospital-wide AI requires stronger investment in infrastructure, data governance, and compliance.

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Cited by

Sorted by citations

Authors
Maimaitiaili, M., Jiamaliding, Y., Dai, G., Xiao, H., Kuerbanjiang, W., & Yi, Y.
Affiliations
  • Wuhan University (CN)
  • Zhongnan Hospital of Wuhan University (CN)
Corresponding author
Yi, Yuexiong
ORCID
Venue
Journal of Medical Internet Research
Publisher
JMIR Publications Inc.
Published
2025-12-17
Volume
27
Pages
e79788-e79788
ISSN
1438-8871 (ISSN-L)
DOI
10.2196/79788
PMC
PMC12710730
OpenAlex
W4415701823
Cited by
14
References
8
Citations by year
2026: 14
FWCI
0.83
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Journal of Medical Internet Research (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
hospital information systems, data maturity, security and compliance, clinical decision support, emergency medicine, radiology, chronic disease management, data silos, clinical workflow integration, digitalization of healthcare, data-driven decision making
MeSH terms
Algorithms, Artificial Intelligence, Hospital Information Systems, Humans

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