Monitoring performance of clinical artificial intelligence in health care: a scoping review

OBJECTIVE: The objective of this review was to provide an overview of the diverse methods described, tested, or implemented for monitoring performance of clinical artificial intelligence (AI) systems, while also summarizing the arguments given for or against these methods. INTRODUCTION: The integration of AI in clinical decision-making is steadily growing. Performances of AI systems evolve over time, necessitating ongoing performance monitoring. However, the evidence on specific monitoring methods is sparse and heterogeneous. Thus, an overview of the evidence on this topic is warranted to guide further research on clinical AI monitoring. INCLUSION CRITERIA: We included publications detailing metrics or statistical processes employed in systematic, continuous, or repeated initiatives aimed at evaluating or predicting the clinical performance of AI models with direct implications for patient management in health care. No limitations on language or publication date were enforced. METHODS: We performed systematic database searches in MEDLINE (Ovid), Embase (Ovid), Scopus, and ProQuest Dissertations and Theses Global, supplemented by backward and forward citation searches and gray literature searches. Two or more independent reviewers conducted title and abstract screening, full-text evaluation, and data extraction using a tool developed by the authors. During extraction, the methods identified were divided into subcategories. The results are presented narratively and summarized in tables and graphs. RESULTS: Thirty-nine sources of evidence were included in the review, with the most abundant source types being opinion papers/narrative reviews (33%) and simulation studies (33%). One guideline on the topic was identified, offering limited guidance on specific metrics and statistical methods. The number of sources included increased year by year, with almost 4 times as many sources included in 2023 compared with 2019. The most commonly reported performance metrics were traditional metrics from the medical literature, including area under the receiver operating characteristics curve (AUROC), sensitivity, specificity, and predictive values, although few arguments were given supporting these choices. Some studies reported on metrics and statistical processing specifically designed to monitor clinical AI. CONCLUSION: This review provides a summary of the methods described for monitoring AI in health care. It reveals a relative scarcity of evidence and guidance for specific practical implementation of performance monitoring of clinical AI. This underscores the imperative for further research, discussion, and guidance regarding the specifics of implementing monitoring for clinical AI. The steady increase in the number of relevant sources published per year suggests that this area of research is gaining increased focus, and the amount of evidence and guidance available will likely increase significantly over the coming years. REVIEW REGISTRATION: Open Science Framework https://osf.io/afkrn.