Evidence map›Paper›PMID 40110115›Full record

ReviewFrontiers in digital health2025

Artificial intelligence in clinical medicine: a state-of-the-art overview of systematic reviews with methodological recommendations for improved reporting.

Giovanni Morone, Luigi De Angelis, Alex Martino Cinnera, Riccardo Carbonetti, Alessio Bisirri, Irene Ciancarelli, Marco Iosa, Stefano Negrini, Carlotte Kiekens, Francesco Negrini

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Frontiers in digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 3 pooled it
–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

21 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Artificial intelligence in rehabilitation: a living systematic mapping review - first release.European journal of physical and rehabilitation medicine · 2025
    Pooled it
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Article
  10. Review
  11. Review
  12. Review
  13. Article
  14. Article
  15. Article
  16. Article
  17. Review
  18. Article
  19. Article
  20. Reshaping Anesthesia with Artificial Intelligence: From Concept to Reality.Anesthesia and critical care (Houston, Tex.) · 2025
    Article
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

10 authors.

Giovanni MoroneDepartment of Life, Health and Environmental Sciences, University of L'Aquila, L'Aquila, Italy.
Luigi De AngelisDepartment of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy.
Alex Martino CinneraScientific Institute for Research, Hospitalisation and Health Care IRCCS Santa Lucia Foundation, Rome, Italy.
Riccardo CarbonettiClinical Area of Neuroscience and Neurorehabilitation, Neurofunctional Rehabilitation Unit, IRCCS "Bambino Gesù" Children's Hospital, Rome, Italy.
Alessio BisirriVilla Sandra Institute, Rome, Italy.
Irene CiancarelliDepartment of Life, Health and Environmental Sciences, University of L'Aquila, L'Aquila, Italy.
Marco IosaScientific Institute for Research, Hospitalisation and Health Care IRCCS Santa Lucia Foundation, Rome, Italy.
Stefano NegriniDepartment of Biomedical, Surgical and Dental Sciences, University 'La Statale', Milan, Italy.
Carlotte KiekensIRCCS Istituto Ortopedico Galeazzi, Milan, Italy.
Francesco NegriniDepartment of Biotechnology and Life Sciences, University of Insubria, Varese, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

artificial intelligenceclinical medicinedigital healthinformation sciencemachine learningnatural language processingneural networksrehabilitation

Identifiers

PMID40110115
PMCPMC11920125

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.