Accessibility settings

This paper is in the following e-collection/theme issue:

Editorial (4) Adoption and Change Management of eHealth Systems (968) Electronic Health Records (1393) eHealth Infrastructures (444) Clinical Communication, Electronic Consultation and Telehealth (813) New Technologies (120) Artificial Intelligence (2395) Generative Language Models Including ChatGPT (841) AI Language Models in Health Care (266) Ambient AI Scribes and AI-Driven Documentation Technologies (28)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/80898, first published .
AI Scribes in Health Care: Balancing Transformative Potential With Responsible Integration

AI Scribes in Health Care: Balancing Transformative Potential With Responsible Integration

AI Scribes in Health Care: Balancing Transformative Potential With Responsible Integration

Authors of this article:

Tiffany I Leung1, 2 Author Orcid Image ;   Andrew J Coristine1, 3 Author Orcid Image ;   Arriel Benis4 Author Orcid Image
Article Authors Cited by (14) Tweetations Metrics

Journals

  1. Palm E, Manikantan A, Mahal H, Belwadi S, Pepin M. Assessing the quality of AI-generated clinical notes: validated evaluation of a large language model ambient scribe. Frontiers in Artificial Intelligence 2025;8 View
  2. Scott I, Cresswell K, Williams R, van der Vegt A. How can we promote greater adoption of AI in healthcare?. BMJ Quality & Safety 2026;35(4):288 View
  3. Leiserowitz G, Mansfield J, MacDonald S, Jost M. Patient Attitudes Toward Ambient Voice Technology: Preimplementation Patient Survey in an Academic Medical Center. JMIR Medical Informatics 2025;13:e77901 View
  4. Coiera E, Fraile-Navarro D. AI Scribes: Are We Measuring What Matters?. JMIR Medical Informatics 2026;14:e89337 View
  5. Atiku S, Olakotan O, Owolanke K. Usability‐Related Barriers and Facilitators Influencing the Adoption and Use of AI Scribes in Healthcare: A Scoping Review. Journal of Evaluation in Clinical Practice 2026;32(1) View
  6. Ferreira Santos J, Dores H. Large Language Models in Cardiovascular Prevention: A Narrative Review and Governance Framework. Diagnostics 2026;16(3):390 View
  7. Menz B, Scarfo N, Modi N, Cornelisse E, Li L, Tan J, Gandhi J, Maher D, Kousa D, Daniel K, Menon V, Bacchi S, McKinnon R, Wiese M, Rowland A, Sorich M, Hopkins A. Vision-Enabled AI scribes reduce omissions in clinical conversations: evidence from simulated medication histories. npj Digital Medicine 2026;9(1) View
  8. Xu Q, Li Y, Zhu M, Cai Y, Cheng X, Wang W, Ju J, Xu Y, Liu Y, Liu Y. Precision cardiovascular medicine with big data and AI. npj Digital Medicine 2026 View
  9. Ohde J, Thompson A, Liu Z, Rost L, Overgaard J, Tan J, Overgaard S, Sarmiento R, Ke Y, Liew J, Ong J, Liu N. Barriers and opportunities of scaling ambient AI scribes for clinical documentation across diverse healthcare settings. npj Digital Medicine 2026 View
  10. Qamar A, Kelly M, Maweni R, Abdul-Hamid A. Improving ward round documentation using the Heidi Health application. BMJ Open Quality 2026;15(1):e003910 View
  11. Rinderknecht E, Alexa R, Wülfing C, Carl N, May M, Frees S. „Large language models“ als Kommunikations- und Organisationsinfrastruktur in der Urologie: Evidenz, Grenzen und klinische Verantwortung. Die Urologie 2026 View
  12. Haider S, Prabha S, Gomez-Cabello C, Genovese A, Collaco B, Wood N, Lifson M, Tao C, Bagaria S, Forte A. An AI-Powered RAG Conversational Agent for Enhancing Patient Recall of Surgical Consultations: A Proof-of-Concept Study. Aesthetic Surgery Journal 2026 View

Books/Policy Documents

  1. Hirosawa T. Artificial Intelligence in Medical Diagnostics. View

Conference Proceedings

  1. Anand A, Ganesan D, Karkar R. Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems. High Accuracy and Hidden Disparities: Investigating Foundation Model Performance in Clinical Cognitive Assessment View