ArticleBMJ open2024
Protocol for the development of the Chatbot Assessment Reporting Tool (CHART) for clinical advice.
Article in BMJ open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses 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
16 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Reporting guideline for Chatbot Health Advice studies: the CHART statement.BMC medicine · 2025Guideline
- Adherence of Studies on Large Language Models for Medical Applications Published in Leading Medical Journals According to the MI-CLEAR-LLM Checklist.Korean journal of radiology · 2025Pooled it
- Methodological Considerations for AI-Powered Chatbots in Oral Cancer Awareness: Stakeholder Diversity, Instrument Transparency, and Safety Auditing.Clinical and translational science · 2026Article
- Insufficient reporting quality in large language model studies in the field of radiology.Insights into imaging · 2026Article
- Critical Appraisal Tools for Evaluating Artificial Intelligence in Clinical Studies: Scoping Review.Journal of medical Internet research · 2025Article
- Reporting guideline for the use of Generative Artificial intelligence tools in MEdical Research: the GAMER Statement.BMJ evidence-based medicine · 2025Article
- Reporting guidelines for studies involving generative artificial intelligence applications: what do I use, and when?NPJ digital medicine · 2025Article
- Reporting Guideline for Chatbot Health Advice Studies: Chatbot Assessment Reporting Tool (CHART) Statement.Annals of family medicine · 2025Article
- Evaluating a Chatbot as a Companion for Patients With Breast Cancer: Collaborative Pilot Study.JMIR cancer · 2025Article
- Reporting guidelines for chatbot health advice studies: explanation and elaboration for the Chatbot Assessment Reporting Tool (CHART).BMJ (Clinical research ed.) · 2025Article
- Reporting guideline for chatbot health advice studies: the Chatbot Assessment Reporting Tool (CHART) statement.The British journal of surgery · 2025Article
- Conversational Agents to Support Pain Management: A Scoping Review.European journal of pain (London, England) · 2025Article
- Advantages and limitations of large language models for antibiotic prescribing and antimicrobial stewardship.npj antimicrobials and resistance · 2025Review
- Reporting guideline for chatbot health advice studies: the Chatbot Assessment Reporting Tool (CHART) statement.BMJ medicine · 2025Article
- Minimum Reporting Items for Clear Evaluation of Accuracy Reports of Large Language Models in Healthcare (MI-CLEAR-LLM).Korean journal of radiology · 2024Article
- Reporting Guidelines for Artificial Intelligence Studies in Healthcare (for Both Conventional and Large Language Models): What's New in 2024.Korean journal of radiology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionLarge language model (LLM)-linked chatbots are being increasingly applied in healthcare due to their impressive functionality and public availability. Studies have assessed the ability of LLM-linked chatbots to provide accurate clinical advice. However, the methods applied in these Chatbot Assessment Studies are inconsistent due to the lack of reporting standards available, which obscures the interpretation of their study findings. This protocol outlines the development of the Chatbot Assessment Reporting Tool (CHART) reporting guideline. METHODS AND ANALYSIS: The development of the CHART reporting guideline will consist of three phases, led by the Steering Committee. During phase one, the team will identify relevant reporting guidelines with artificial intelligence extensions that are published or in development by searching preprint servers, protocol databases, and the Enhancing the Quality and Transparency of health research Network. During phase two, we will conduct a scoping review to identify studies that have addressed the performance of LLM-linked chatbots in summarising evidence and providing clinical advice. The Steering Committee will identify methodology used in previous Chatbot Assessment Studies. Finally, the study team will use checklist items from prior reporting guidelines and findings from the scoping review to develop a draft reporting checklist. We will then perform a Delphi consensus and host two synchronous consensus meetings with an international, multidisciplinary group of stakeholders to refine reporting checklist items and develop a flow diagram. ETHICS AND DISSEMINATION: We will publish the final CHART reporting guideline in peer-reviewed journals and will present findings at peer-reviewed meetings. Ethical approval was submitted to the Hamilton Integrated Research Ethics Board and deemed "not required" in accordance with the Tri-Council Policy Statement (TCPS2) for the development of the CHART reporting guideline (#17025). REGISTRATION: This study protocol is preregistered with Open Science Framework: https://doi.org/10.17605/OSF.IO/59E2Q.
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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.