Clinical Implementation of Artificial Intelligence Scribes in Health Care: A Systematic Review
Abstract
Abstract Background Artificial intelligence (AI) scribes use advanced speech recognition and natural language processing to automate clinical documentation and ease administrative burden. However, little is known about the effect of AI scribes on clinicians, patients, and organizations. Objectives This study aimed to (1) propose an evaluation framework to guide future AI scribe implementations, (2) describe the effect of AI scribes along the domains proposed in the developed evaluation framework, and (3) identify gaps in the AI scribe implementation literature to be evaluated in future studies. Methods Databases including Embase, Embase Classic, and Ovid Medline were searched, and a manual review was conducted of the New England Journal of Medicine AI. Studies published after 2021 that reported on the implementation of AI scribes in health care were included. Descriptive analysis was undertaken. Quality assessment was undertaken using the Newcastle–Ottawa Scale. The nominal group technique was used to develop an evaluation framework. Results Eleven studies met the inclusion criteria, with 10 published in 2024. The most frequently used AI scribe was Dragon Ambient eXperience ( n = 7, 64%). While clinicians often reported improved documentation quality, AI scribe accuracy varied, frequently requiring manual edits and raising occasional concerns about errors. Nine of 10 studies reported improvements in at least one efficiency metric, and seven of ten studies highlighted…
How strong is the evidence?
Evidence Hub reads this as a systematic review, done in people. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid. Reported size: 7 participants or records.
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