HMN 2025: What is the double-edged sword of AI scribes in health care

New editorial explores the double-edged sword of AI scribes in health care
Terminology used for artificial intelligence (AI)–based documentation technologies, based on published literature over the past decade [5-18]. *Transcription software in this period was based on different types of AI models, including one or more of the following: automated speech recognition, natural language processing, probabilistic graphical models (conditional random field), or logistic regression models [5]. Credit: JMIR Medical Informatics (2025). DOI: 10.2196/80898

A new editorial published in JMIR Medical Informatics examines the rapid rise of ambient artificial intelligence (AI) scribes—technologies designed to automate clinical documentation and ease the administrative burden on health care practitioners. While these tools show great promise in reducing burnout and freeing up time for patient care, the editorial highlights significant concerns that warrant further investigation.

The piece, authored by Dr. Tiffany I Leung, Dr. Andrew J Coristine, and Dr. Arriel Benis, acknowledges the potential of ambient AI scribes to revolutionize clinical workflows. It notes that early evidence suggests these tools can lead to reduced clinician burnout, less time spent on after-hours documentation, and improved patient-physician interaction as providers can be more present during visits.

However, the authors caution that these benefits are balanced by a number of challenges. The editorial points to persistent concerns about the accuracy and reliability of AI-generated notes, including errors, omissions, and hallucinations. It also raises ethical and legal questions, such as algorithmic bias, privacy risks, and the potential for “cognitive debt” or overreliance on AI, which could diminish critical thinking skills.

Although the enthusiasm for AI scribes is understandable, the must proceed with caution and a commitment to rigorous, evidence-based evaluation. The analysis shows that while these tools are a promising solution to a long-standing problem, many questions remain unanswered about their impact on , clinician training, and system-level outcomes.

More information:
Tiffany I Leung et al, AI Scribes in Health Care: Balancing Transformative Potential With Responsible Integration, JMIR Medical Informatics (2025). DOI: 10.2196/80898

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