Artificial intelligence in healthcare: transforming patient safety with intelligent systems—A systematic review
IntroductionAdverse 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 e...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2024.1522554/full |
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| author | Francesco De Micco Francesco De Micco Gianmarco Di Palma Gianmarco Di Palma Davide Ferorelli Anna De Benedictis Anna De Benedictis Luca Tomassini Vittoradolfo Tambone Mariano Cingolani Roberto Scendoni Roberto Scendoni |
| author_facet | Francesco De Micco Francesco De Micco Gianmarco Di Palma Gianmarco Di Palma Davide Ferorelli Anna De Benedictis Anna De Benedictis Luca Tomassini Vittoradolfo Tambone Mariano Cingolani Roberto Scendoni Roberto Scendoni |
| author_sort | Francesco De Micco |
| collection | DOAJ |
| description | IntroductionAdverse 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.MethodsThe 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.ResultsAI 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.DiscussionThe 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. |
| format | Article |
| id | doaj-art-71c25a02a3544e57bab79e2e73fa19ae |
| institution | Kabale University |
| issn | 2296-858X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj-art-71c25a02a3544e57bab79e2e73fa19ae2025-01-08T06:12:06ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2025-01-011110.3389/fmed.2024.15225541522554Artificial intelligence in healthcare: transforming patient safety with intelligent systems—A systematic reviewFrancesco De Micco0Francesco De Micco1Gianmarco Di Palma2Gianmarco Di Palma3Davide Ferorelli4Anna De Benedictis5Anna De Benedictis6Luca Tomassini7Vittoradolfo Tambone8Mariano Cingolani9Roberto Scendoni10Roberto Scendoni11Research Unit of Bioethics and Humanities, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Rome, ItalyDepartment of Clinical Affair, Fondazione Policlinico Universitario Campus Bio-Medico, Rome, ItalyResearch Unit of Bioethics and Humanities, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Rome, ItalyDepartment of Public Health, Experimental and Forensic Sciences, University of Pavia, Pavia, ItalyInterdisciplinary Department of Medicine (DIM), Section of Legal Medicine, University of Bari “Aldo Moro”, Bari, ItalyResearch Unit of Bioethics and Humanities, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Rome, ItalyResearch Unit of Nursing Science, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Rome, ItalyInternational School of Advanced Studies, University of Camerino, Camerino, ItalyResearch Unit of Bioethics and Humanities, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Rome, ItalyDepartment of Law, Institute of Legal Medicine, University of Macerata, Macerata, ItalyDepartment of Law, Institute of Legal Medicine, University of Macerata, Macerata, ItalyItalian Network for Safety in Healthcare (INSH), Coordination of Marche Region, Macerata, ItalyIntroductionAdverse 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.MethodsThe 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.ResultsAI 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.DiscussionThe 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.https://www.frontiersin.org/articles/10.3389/fmed.2024.1522554/fullartificial intelligencepatient safetyhealthcareintelligent systemsmachine learning |
| spellingShingle | Francesco De Micco Francesco De Micco Gianmarco Di Palma Gianmarco Di Palma Davide Ferorelli Anna De Benedictis Anna De Benedictis Luca Tomassini Vittoradolfo Tambone Mariano Cingolani Roberto Scendoni Roberto Scendoni Artificial intelligence in healthcare: transforming patient safety with intelligent systems—A systematic review Frontiers in Medicine artificial intelligence patient safety healthcare intelligent systems machine learning |
| title | Artificial intelligence in healthcare: transforming patient safety with intelligent systems—A systematic review |
| title_full | Artificial intelligence in healthcare: transforming patient safety with intelligent systems—A systematic review |
| title_fullStr | Artificial intelligence in healthcare: transforming patient safety with intelligent systems—A systematic review |
| title_full_unstemmed | Artificial intelligence in healthcare: transforming patient safety with intelligent systems—A systematic review |
| title_short | Artificial intelligence in healthcare: transforming patient safety with intelligent systems—A systematic review |
| title_sort | artificial intelligence in healthcare transforming patient safety with intelligent systems a systematic review |
| topic | artificial intelligence patient safety healthcare intelligent systems machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2024.1522554/full |
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