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Evaluating the performance of artificial intelligence-based speech recognition for clinical documentation: a
Joel Jia Wei Ng1, Eugene Wang1, Xinyan Zhou1
1NUS Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
BMC Medical Informatics and Decision Making
|July 2, 2025
Summary
AI transcription tools show promise for clinical documentation but accuracy varies widely. Further development is needed for reliable integration into healthcare workflows.
Area of Science:
- Medical Informatics
- Artificial Intelligence in Healthcare
Background:
- Clinical documentation is essential for patient care and legal compliance.
- Traditional methods are inefficient and contribute to clinician burnout.
- AI transcription, using automatic speech recognition (ASR) and natural language processing (NLP), offers automation potential.
Purpose of the Study:
- To systematically review studies evaluating AI transcription tools in clinical settings.
- To assess the performance, accuracy, and efficiency of these systems.
- To identify challenges and areas for improvement in AI-driven clinical documentation.
Main Methods:
- A comprehensive literature search of MEDLINE, Embase, and Cochrane Library up to February 16, 2025.
- Inclusion of studies on AI transcription software used by clinicians, reporting accuracy, time efficiency, and user satisfaction.
- Systematic data extraction and quality assessment using QUADAS-2, followed by narrative synthesis.
Main Results:
- Twenty-nine studies were included, showing wide variability in Word Error Rates (0.087 to >50%) and F1 scores (0.416 to 0.856).
- Some studies reported time savings and improved note completeness, while others noted increased editing needs and errors with specialized terms or accents.
- Large Language Model (LLM)-based systems offered summarization but required human oversight for safety.
Conclusions:
- AI transcription systems have potential but face challenges in accuracy, adaptability, and workflow integration.
- Domain-specific training, real-time error correction, and EHR interoperability are crucial for adoption.
- Future research should focus on advanced "digital scribes" with LLM summarization capabilities.
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