xRead - Incorporating Artificial Intelligence into Clinical Practice (March 2026)

Ng et al. BMC Medical Informatics and Decision Making

(2025) 25:236

Page 2 of 24

Conclusions AI-based transcription systems show potential to improve clinical documentation but face challenges in accuracy, adaptability and workflow integration. Refinements in domain-specific training, real-time error correction and interoperability with electronic health records are critical for their effective adoption in clinical practice. Future research should also focus on next-generation “digital scribes” incorporating LLM-driven summarization and repurposing of text. Clinical trial number Not applicable. Keywords Artificial intelligence, Speech recognition, Digital scribe, Ambient scribe, Clinical documentation, Accuracy

Background Clinical documentation, defined as the systematic recording of a patient’s medical history, diagnoses, treat ment plans and care provided, remains a cornerstone of effective healthcare. It is critical in ensuring accu rate communication among healthcare providers, legal accountability, and continuity of care [1]. However, tra ditional documenting methods, such as manual note-tak ing or transcription, are often labour-intensive, prone to errors and can detract from the quality of patient-clini cian interactions [2, 3]. These inefficiencies not only con tribute to clinician burnout but also risk compromising the accuracy of medical records and patient safety. In recent years, Artificial Intelligence (AI) has begun to transform clinical documentation through the use of advanced technologies like automatic speech recogni tion (ASR), large language models (LLM) and natural language processing (NLP) [4, 5]. These AI-driven tran scription tools automate the process of converting spo ken language into structured electronic medical records (EMRs), thereby alleviating the burden of manual data entry [5]. By streamlining this process, AI transcrip tion systems offer the potential to improve the accu racy and completeness of clinical documentation while allowing clinicians to focus more on patient care and communication. Despite the promise of AI in this domain, the effective ness of AI transcription tools remains inconsistent across different clinical settings [4]. Studies report varying lev els of accuracy, time savings and user satisfaction [4, 5]. While some tools demonstrate significant improvements in documentation speed and precision, others face chal lenges with speech recognition (SR) errors, the need for manual post-editing and inconsistencies in real-world clinical use [4, 5]. These mixed outcomes highlight the complexity of integrating AI tools into diverse healthcare environments and underscore the need for a thorough evaluation of their performance. This review aims to synthesize the current evidence on AI transcription tools, focusing on their accuracy, efficiency and usability in clinical practice. By examin ing the successes and challenges of implementing these technologies, the review seeks to provide insights that can guide the development and integration of AI-driven

documentation systems, ultimately shaping the future of clinical workflows and improving the quality of patient care. Methods Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [6] for identifying, selecting and synthesizing evidence, the protocol for this review was developed and registered in PROSPERO (registration number CRD42024597200). Search strategy A comprehensive literature search was performed on February 16, 2025, and it was conducted across multiple electronic databases, including MEDLINE (via OVID), Embase and the Cochrane Library, covering all records published up to February 16, 2025. The search strategy was developed in consultation with the help of medical information experts to identify studies that evaluated the performance of AI-based medical transcription software in clinical settings. Key words and the following Medical Subject Headings (MeSH) were applied: “Artificial Intelli gence”, “Digital Scribe”, “Medical Transcription”, “Speech Recognition”, “Natural Language Processing”, “Electronic Health Records” and “Clinical Documentation”. Details of the search strategy can be found in the Supplementary Material (Table S1). Additionally, grey literature was searched via Google search engine to capture relevant non-peer-reviewed studies. To further enhance the comprehensiveness of the search, forward and backward searching were performed on the reference lists of relevant studies to identify addi tional literature that may not have been captured in the initial search. Inclusion and exclusion criteria The inclusion criteria for this systematic review were as follows: the population of interest included studies that involved clinicians, such as physicians and nurses, who used AI-based transcription software for clinical docu mentation. The intervention of focus was the use of AI-driven transcription tools, which may include tech nologies like ASR, LLM, and NLP systems. Eligible stud ies must report on one or more key outcomes, such as