Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review
By Avishek Choudhury, Onur Asan. JMIR Medical Informatics, 2020
338 citations recorded by the source corpus.
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.
Subfields: Health Informatics, Health Information Management, Surgery
View Paper: https://www.semanticscholar.org/paper/9721c705f2664cb313923210ff6614f2579f5f77
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Citations in the corpus
- Challenges to implementing artificial intelligence in healthcare: a qualitative interview study with healthcare leaders in Sweden (2022, background): “The conclusion in newly published reviews addressing regulation, privacy and legal aspects [15, 16], ethics [16–18], clinical and patient outcomes [19–21] and economic impact [22], is that further research is needed in a real-world clinical setting although the clinical implementation of AI…”
- Unleashing the potential of prompt engineering in Large Language Models: a comprehensive review (2023, background): “Indeed, by analyzing vast amounts of medical literature, these models can provide doctors with insights into rare conditions, suggest potential treatment pathways, and even predict patient outcomes [10].”