Evaluating the performance of artificial intelligence-based speech recognition for clinical documentation: a systematic review

Clinical documentation is vital for effective communication, legal accountability and the continuity of care in healthcare. Traditional documentation methods, such as manual transcription, are time-consuming, prone to errors and contribute to clinician burnout. AI-driven transcription systems utilizing automatic speech recognition (ASR) and natural language processing (NLP) aim to automate and enhance the accuracy and efficiency of clinical documentation. However, the performance of these systems varies significantly across clinical settings, necessitating a systematic review of the published studies.

A comprehensive search of MEDLINE, Embase, and the Cochrane Library identified studies evaluating AI transcription tools in clinical settings, covering all records up to February 16, 2025. Inclusion criteria encompassed studies involving clinicians using AI-based transcription software, reporting outcomes such as accuracy (e.g., Word Error Rate), time efficiency and user satisfaction. Data were extracted systematically, and study quality was assessed using the QUADAS-2 tool. Due to heterogeneity in study designs and outcomes, a narrative synthesis was performed, with key findings and commonalities reported.