Evidence mapPaperPMID 39845830Full record

SynthesisFrontiers in medicine2024

Artificial intelligence in healthcare: transforming patient safety with intelligent systems-A systematic review.

Francesco De Micco, Gianmarco Di Palma, Davide Ferorelli, Anna De Benedictis, Luca Tomassini, Vittoradolfo Tambone, Mariano Cingolani, Roberto Scendoni

Abstract readSystematic Review
In one paragraph

Synthesis in Frontiers in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing 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

18 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Revealing the hidden harms in end-of-life care: a mixed-methods characterisation of reported safety incidents involving injectable symptom control medication.The British journal of general practice : the journal of the Royal College of General Practitioners · 2026
    Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Review
  12. Article
  13. Review
  14. Article
  15. Review
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  18. Review
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

8 authors.

Francesco De MiccoResearch Unit of Bioethics and Humanities, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Rome, Italy.
Gianmarco Di PalmaResearch Unit of Bioethics and Humanities, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Rome, Italy.
Davide FerorelliInterdisciplinary Department of Medicine (DIM), Section of Legal Medicine, University of Bari "Aldo Moro", Bari, Italy.
Anna De BenedictisResearch Unit of Bioethics and Humanities, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Rome, Italy.
Luca TomassiniInternational School of Advanced Studies, University of Camerino, Camerino, Italy.
Vittoradolfo TamboneResearch Unit of Bioethics and Humanities, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Rome, Italy.
Mariano CingolaniDepartment of Law, Institute of Legal Medicine, University of Macerata, Macerata, Italy.
Roberto ScendoniDepartment of Law, Institute of Legal Medicine, University of Macerata, Macerata, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Adverse events in hospitals significantly compromise patient safety and trust in healthcare systems, with medical errors being a leading cause of death globally. Despite efforts to reduce these errors, reporting remains low, and effective system changes are rare. This systematic review explores the potential of artificial intelligence (AI) in clinical risk management. Methods: The systematic review was conducted using the PRISMA Statement 2020 guidelines to ensure a comprehensive and transparent approach. We utilized the online tool Rayyan for efficient screening and selection of relevant studies from three different online bibliographic. Results: AI systems, including machine learning and natural language processing, show promise in detecting adverse events, predicting medication errors, assessing fall risks, and preventing pressure injuries. Studies reveal that AI can improve incident reporting accuracy, identify high-risk incidents, and automate classification processes. However, challenges such as socio-technical issues, implementation barriers, and the need for standardization persist. Discussion: The review highlights the effectiveness of AI in various applications but underscores the necessity for further research to ensure safe and consistent integration into clinical practices. Future directions involve refining AI tools through continuous feedback and addressing regulatory standards to enhance patient safety and care quality.

Indexed as

artificial intelligencehealthcareintelligent systemsmachine learningpatient safety

Identifiers

PMID39845830
PMCPMC11750995

What Socratic 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 Socratic graph.