ArticleBMJ open2026
Clinical outcomes, patient satisfaction and operational efficiency of AI-powered chatbots in medicine and healthcare: protocol for an AI-aided scoping review.
Article in BMJ open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
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Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionArtificial intelligence (AI)-powered chatbots are increasingly integrated into healthcare to support administrative processes, health education and chronic disease management. These systems simulate human dialogue through natural language processing and machine learning, enabling dynamic and context-aware interactions. Despite their rapid adoption, there is limited synthesis of existing research describing how these technologies are applied across different healthcare contexts and what outcomes have been reported. This scoping review aims to map and describe the existing literature on the use of AI-powered chatbots in healthcare with a focus on clinical outcomes, patient satisfaction and operational efficiency. It will identify the types of studies conducted, their key characteristics and existing research gaps to guide future research. METHODS AND ANALYSIS: Following the Joanna Briggs Institute methodology and Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines, a comprehensive search will be conducted across Medline (PubMed), CINAHL, Embase, Web of Science, The Cochrane Library and MedRxiv from database inception to 10 September 2025. Studies published in English, French, Dutch or German, involving AI-powered chatbots in any healthcare context reporting on clinical outcomes and/or patient satisfaction and/or operational efficiency will be included. Studies without full-text availability, protocols, trial registrations, reviews and studies conducted solely in educational settings will be excluded. Title and abstract screening will be supported by ASReview LAB, an AI-based active learning tool to enhance efficiency. Screening and data extraction will be conducted independently by two reviewers with disagreements resolved by a third reviewer. Findings will be synthesised narratively and presented using structured evidence tables categorised by chatbot type, clinical healthcare context and reported outcomes. ETHICS AND DISSEMINATION: Ethical approval is not required, as this study involves the analysis of published data only. The results of this scoping review will be disseminated through publication in a peer-reviewed journal, presentations at academic conferences and established professional networks. TRIAL REGISTRATION NUMBER: Open Science Framework (OSF), https://doi.org/10.17605/OSF.IO/8UE3B.
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What Socratic 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 Socratic graph.