ArticleNPJ digital medicine2025
An evaluation framework for ambient digital scribing tools in clinical applications.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed.
- Bridging the trust-adoption gap for AI scribes in rural communities: A machine learning approach using the 2024 Canadian digital health survey.International journal of medical informatics · 2026Article
- Ambient AI Scribes as Emerging Infrastructure in the Learning Health System.Learning health systems · 2026Article
- General-Purpose Artificial Intelligence Use in Routine Mental Health Practice Among Australian Clinicians: Mixed Methods Study.Journal of medical Internet research · 2026Article
- Evaluation of Socio-Technical Mechanisms Shaping AI Scribe Documentation Failures: A Netnographic Study.Journal of evaluation in clinical practice · 2026Article
- Selecting, Scaling, and Measuring the Value of Ambient AI in a Nonacademic Health System: Multiphase Pilot Study.JMIR medical informatics · 2026Article
- From Clinical Encounter to Draft Documentation: A Mechanistic Narrative Review of Ambient Scribe Technology.Cureus · 2026Review
- Article
- The Ethics of AI Scribes as Epistemic Agents.JMIR medical informatics · 2026Article
- Validating Digital Scribes: A Scoping Review of Evaluation Practices and Clinical Use.Journal of medical systems · 2026Article
- AI-generated clinical summaries: errors and susceptibility to speech and speaker variability.BMJ health & care informatics · 2026Article
- Evaluating open-source LLMs for dental EMR generation.BMC oral health · 2026Article
- Barriers and opportunities of scaling ambient AI scribes for clinical documentation across diverse healthcare settings.NPJ digital medicine · 2026Review
- Listening to the note: clinician perspectives on ambient artificial intelligence scribes in medical documentation.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Artificial Intelligence in Eating Disorder Treatment: A Qualitative Analysis of Clinical Opportunities, Barriers, and Ethical Considerations From Multi-Disciplinary Focus Groups.The International journal of eating disorders · 2026Article
- Real-world evaluation of an ambient AI scribe in Spanish outpatient care after 2.3 million uses: impact on clinician experience, semantic agreement, and workflow efficiency.Frontiers in digital health · 2026Article
- Evaluating the Quality and Safety of Ambient Digital Scribe Platforms Using Simulated Ambulatory Encounters.Mayo Clinic proceedings. Digital health · 2025Article
- Enabling inclusive systematic reviews: incorporating preprint articles with large language model-driven evaluations.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- AI Scribes in Health Care: Balancing Transformative Potential With Responsible Integration.JMIR medical informatics · 2025Article
- Assessing the quality of AI-generated clinical notes: validated evaluation of a large language model ambient scribe.Frontiers in artificial intelligence · 2025Article
Corrections and comments
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Authors and funding
18 authors.
Funding
Abstract
Ambient digital scribing (ADS) tools alleviate clinician documentation burden, reducing burnout and enhancing efficiency. As AI-driven ADS tools integrate into clinical workflows, robust governance is essential for ethical and secure deployment. This study proposes a comprehensive ADS evaluation framework incorporating human evaluation, automated metrics, simulation testing, and large language models (LLMs) as evaluators. Our framework assesses transcription, diarization, and medical note generation across criteria such as fluency, completeness, and factuality. To demonstrate its effectiveness, we developed an ADS tool and applied our framework to evaluate the tool's performance on 40 real clinical visit recordings. Our evaluation revealed strengths, such as fluency and clarity, but also highlighted weaknesses in factual accuracy and the ability to capture new medications. These findings underscore the value of structured ADS evaluation in improving healthcare delivery while emphasizing the need for strong governance to ensure safe, ethical integration.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.