ReviewCardiovascular diagnosis and therapy2026
Transforming clinical documentation with ambient artificial intelligence (AI) scribes: a narrative review of technology, impact, and implementation.
Review in Cardiovascular diagnosis and therapy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evidence on artificial intelligence-assisted clinical documentation and healthcare workers' emotional wellbeing at work: a scoping review.Frontiers in psychology · 2026Pooled it
- Medical AI and Human Dignity: A Catholic Normative Ethical Framework in Light of Magnifica Humanitas.Journal of religion and health · 2026Article
- When the Algorithm Speaks First: Trust, Reliance and the Reconstruction of Clinical Metacognition.Journal of evaluation in clinical practice · 2026Article
- Article
- From Symptom Control to Precision Supportive Oncology: Integrating Artificial Intelligence in Supportive Oncology for Gastrointestinal Cancers.Current oncology reports · 2026Review
- Beyond productivity: aBMJ digital health & AI · 2026Review
- Implementation of Ambient AI Scribes in Hospitals: Lessons for Healthcare Leadership, Governance, and Change Management.Journal of healthcare leadership · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and Objective: Electronic health records (EHRs) have modernized care but increased documentation burden and clinician burnout. Ambient artificial intelligence (AI) scribes, combining automated speech recognition (ASR), natural language processing (NLP), and generative AI, aim to address this by capturing encounters and generating documentation. Related technologies, including virtual assistants and autonomous patient-facing systems, extend these capabilities beyond the clinician's physical presence. This narrative review synthesizes current evidence on the real-world performance, implementation, and impact of these AI tools. Methods: A narrative literature search was conducted using PubMed, supplemented by a manual review of reference lists from key articles. The search covered studies published between January 2019 and June 2025. After screening and full-text review, 18 studies met inclusion criteria and were incorporated into this review. Key Content and Findings: AI scribes consistently reduce documentation burden and cognitive load, improve workflow efficiency, save time, and enhance patient-clinician interaction by allowing greater clinician focus. However, studies also report frequent documentation omissions and occasional clinically significant hallucinations. Implementation remains a sociotechnical challenge involving workflow redesign, medico-legal considerations, and preservation of the patient-clinician relationship. In cardiology, where documentation requires precise, time-sensitive detail, AI-related errors may carry greater risk, underscoring the need for specialty-specific validation. Conclusions: Ambient AI scribes show promise in reducing workload, improving efficiency, and decreasing burnout, but current systems still generate high omission rates and intermittent factual inaccuracies that may affect clinical decision-making. Evidence remains limited by small cohorts and methodological variability, warranting cautious interpretation. More rigorous, standardized evaluations are needed before routine clinical adoption.
Indexed as
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.