Artificial intelligence in clinical medicine: a state-of-the-art overview of systematic reviews with methodological recommendations for improved reporting
Open accessAbstract
Medicine has become increasingly receptive to the use of artificial intelligence (AI). This overview of systematic reviews (SRs) aims to categorise current evidence about it and identify the current methodological state of the art in the field proposing a classification of AI model (CLASMOD-AI) to improve future reporting. PubMed/MEDLINE, Scopus, Cochrane library, EMBASE and Epistemonikos databases were screened by four blinded reviewers and all SRs that investigated AI tools in clinical medicine were included. 1923 articles were found, and of these, 360 articles were examined via the full-text and 161 SRs met the inclusion criteria. The search strategy, methodological, medical and risk of bias information were extracted. The CLASMOD-AI was based on input, model, data training, and performance metric of AI tools. A considerable increase in the number of SRs was observed in the last five years. The most covered field was oncology accounting for 13.9% of the SRs, with diagnosis as the predominant objective in 44.4% of the cases). The risk of bias was assessed in 49.1% of included SRs, yet only 39.2% of these used tools with specific items to assess AI metrics. This overview highlights the need for improved reporting on AI metrics, particularly regarding the training of AI models and dataset quality, as both are essential for a comprehensive quality assessment and for mitigating the risk of bias using specialized evaluation tools.
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Inconsistent evaluation standards obstruct generalisability and regulatory oversight.
“Methodological reviews11,35 identified an additional challenge: inconsistent evaluation standards obstruct generalisability and regulatory oversight.”
From Generative Artificial Intelligence in Healthcare: Automation Bias, Deskilling, and Cognitive Implications – A Systematic Review (2026) · cites this paper as [11]
The Discussion (page unavailable) identifies heterogeneity in clinical AI studies, including evaluation metrics, as a challenge for synthesising evidence. The Results > Methodological approach (page unavailable) also documents varied approaches to quality assessment. Neither passage establishes that inconsistent evaluation standards obstruct generalisability or regulatory oversight. (LLM, conf=0.83)
Experimental — accuracy not yet measuredFrameworks developed in studies enhance methodological theory by incorporating AI ethics, transparency, and bias mitigation into evidence appraisal models like PRISMA-AI and CLASMOD-AI.
“Moreover, frameworks developed in studies4,11,35 enhance methodological theory by incorporating AI ethics, transparency, and bias mitigation into evidence appraisal models like PRISMA-AI and CLASMOD-AI.”
From Generative Artificial Intelligence in Healthcare: Automation Bias, Deskilling, and Cognitive Implications – A Systematic Review (2026) · cites this paper as [11]
The Methods (Data extraction) describe CLASMOD-AI as a tool for evaluating how systematic reviews report AI data, models, training, and performance metrics; the Discussion links improved reporting to bias assessment. But the Limitations and strengths section explicitly says CLASMOD-AI does not yet consider ethical aspects, and the Introduction says PRISMA-AI was still under development. The source therefore does not support the claim that these frameworks already incorporate AI ethics into evidence appraisal. (LLM, conf=0.88)
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- Authors
- Morone, G., De Angelis, L., Martino Cinnera, A., Carbonetti, R., Bisirri, A., Ciancarelli, I., Iosa, M., Negrini, S., Kiekens, C., & Negrini, F.
- Affiliations
- University of L'Aquila (IT)
- Bambino Gesù Children's Hospital (IT)
- Fondazione Santa Lucia (IT)
- IRCCS Ospedale Galeazzi - Sant'Ambrogio (IT)
- University of Insubria (IT)
- ORCID
- Venue
- Frontiers in Digital Health
- Publisher
- Frontiers Media SA
- Published
- 2025-03-05
- Volume
- 7
- Pages
- 1550731-1550731
- ISSN
- 2673-253X (ISSN-L)
- PMC
- PMC11920125
- OpenAlex
- W4408158653
- Cited by
- 41
- References
- 55
- Citations by year
- 2026: 26 · 2025: 15
- FWCI
- 2.42
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Digital Health (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Archivio Istituzionale della Ricerca - Università degli Studi dell Aquila (submittedVersion, cc-by)
- Archivio Istituzionale della Ricerca (Universita Degli Studi Di Milano) (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- IrInSubria (University of Insubria) (submittedVersion)
- IRIS Research product catalog (Sapienza University of Rome) (submittedVersion)
- Funders
- Ministero della Salute
- Università degli Studi dell'Aquila (FFORIC24.31)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- reporting quality, risk of bias assessment, training data quality, diagnosis
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