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SYSTEMATIC REVIEW article

Front. Med.
Sec. Regulatory Science
Volume 11 - 2024 | doi: 10.3389/fmed.2024.1522554
This article is part of the Research Topic Errors and Biases in Modern Healthcare: Public Health, Medico-legal and Risk Management Aspects View all 5 articles

Artificial Intelligence in Healthcare: Transforming Patient Safety with Intelligent Systems -A Systematic Review

Provisionally accepted
Francesco De Micco Francesco De Micco 1Gianmarco Di Palma Gianmarco Di Palma 1,2*Davide Ferorelli Davide Ferorelli 3Anna De Benedictis Anna De Benedictis 1Luca Tomassini Luca Tomassini 4*Tambone Vittoradolfo Tambone Vittoradolfo 5Mario Cingolani Mario Cingolani 6Roberto Scendoni Roberto Scendoni 6
  • 1 Campus Bio-Medico University Hospital, Roma, Italy
  • 2 University of Pavia, Pavia, Lombardy, Italy
  • 3 Internal Medicine Unit, Department of Interdisciplinary Medicine, University of Bari Aldo Moro, Bari, Italy
  • 4 International School of Advanced Studies, University of Camerino, Camerino, Marche, Italy
  • 5 Campus Bio-Medico University, Rome, Lazio, Italy
  • 6 Institute of Legal Medicine, Department of Law, University of Macerata, Macerata, Marche, Italy

The final, formatted version of the article will be published soon.

    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. 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. 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. 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.

    Keywords: artificial intelligence, Patient Safety, healthcare, Intelligent systems, machine learning

    Received: 04 Nov 2024; Accepted: 13 Dec 2024.

    Copyright: © 2024 De Micco, Di Palma, Ferorelli, De Benedictis, Tomassini, Vittoradolfo, Cingolani and Scendoni. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Gianmarco Di Palma, Campus Bio-Medico University Hospital, Roma, Italy
    Luca Tomassini, International School of Advanced Studies, University of Camerino, Camerino, 62032, Marche, Italy

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.