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Cited by 440Artificial Intelligence in Healthcare and EducationMachine Learning in HealthcareArtificial Intelligence in Healthcare

Role of artificial intelligence in patient safety outcomes: systematic literature review

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Choudhury, Avishek, Asan, Onur·2020·JMIR Medical Informatics
DOI 10.2196/18599PMC PMC7414411

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Abstract

Background: Artificial intelligence (AI) provides opportunities to identify the health risks of patients and thus influence patient safety outcomes. Objective: The purpose of this systematic literature review was to identify and analyze quantitative studies utilizing or integrating AI to address and report clinical-level patient safety outcomes. Methods: We restricted our search to the PubMed, PubMed Central, and Web of Science databases to retrieve research articles published in English between January 2009 and August 2019. We focused on quantitative studies that reported positive, negative, or intermediate changes in patient safety outcomes using AI apps, specifically those based on machine-learning algorithms and natural language processing. Quantitative studies reporting only AI performance but not its influence on patient safety outcomes were excluded from further review. Results: We identified 53 eligible studies, which were summarized concerning their patient safety subcategories, the most frequently used AI, and reported performance metrics. Recognized safety subcategories were clinical alarms (n=9; mainly based on decision tree models), clinical reports (n=21; based on support vector machine models), and drug safety (n=23; mainly based on decision tree models). Analysis of these 53 studies also identified two essential findings: (1) the lack of a standardized benchmark and (2) heterogeneity in AI reporting. Conclusions: This systematic review indicates that AI-enabled decision support systems, when implemented correctly, can aid in enhancing patient safety by improving error detection, patient stratification, and drug management. Future work is still needed for robust validation of these systems in prospective and real-world clinical environments to understand how well AI can predict safety outcomes in health care settings.

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  • According to the literature reviewed, none of the software analyzed have reached an official approval stage according to the EU MDR 2017/745 regulation or the US FDA.

    “Several software analyzed in this study have been designed and developed with features that can be considered medical devices, however, according to the literature reviewed, none of them have reached an official approval stage according to the EU MDR 2017/745 regulation or the US FDA.”

    From Artificial intelligence in healthcare: transforming patient safety with intelligent systems—A systematic review (2025) · cites this paper as [79]

    The Discussion > Principal Findings addresses FDA standardization and validation of AI medical devices, but not whether the software analyzed in the citing study received approval. Searches for EU MDR 2017/745 and FDA approval found no passage establishing that none of those products had been approved. (LLM, conf=0.75)

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Cited by

Sorted by citations

Authors
Choudhury, A., & Asan, O.
Affiliations
Stevens Institute of Technology (US)
Corresponding author
Asan, Onur
ORCID
Venue
JMIR Medical Informatics
Publisher
JMIR Publications Inc.
Published
2020-07-24
Volume
8
Issue
7
Pages
e18599
ISSN
2291-9694 (ISSN-L)
DOI
10.2196/18599
PMC
PMC7414411
OpenAlex
W3044626901
Cited by
440
References
16
Citations by year
2026: 75 · 2025: 132 · 2024: 102 · 2023: 63 · 2022: 37 · 2021: 24 · 2020: 5 · 2018: 1
FWCI
2.45
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • JMIR Medical Informatics (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
artificial intelligence, patient safety, clinical decision support systems, machine learning, natural language processing, clinical alarm systems, drug safety, medication errors, patient stratification, error detection, benchmarking

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