ArticleBMJ medicine2025
Reporting guideline for chatbot health advice studies: the Chatbot Assessment Reporting Tool (CHART) statement.
Article in BMJ medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 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
27 citing papers in PubMed.
- Accountability for large language models in health care.Bulletin of the World Health Organization · 2026Article
- Hospital Human Resource Managers' Perspectives on Organizational Readiness for Generative AI Skills: Qualitative Descriptive Study.JMIR medical informatics · 2026Article
- Navigating the Artificial Intelligence Revolution in Clinical Neurology: A New Multidisciplinary Task Force Within the European Academy of Neurology.European journal of neurology · 2026Article
- Fine-Tuning Large Language Models for Motivational Interviewing in Health Behavior Change: Development and Evaluation Study.JMIR formative research · 2026Article
- Clinical outcomes and reporting quality of large language model interventions in practice: a systematic evidence map.NPJ digital medicine · 2026Article
- Evaluation of ChatGPT-4o in oral and maxillofacial surgery examinations: a comparative study of performance on U.S. dental decks and chinese dental licensing examination practice questions.BMC oral health · 2026Article
- Generative artificial intelligence-driven chatbots and medical misinformation: an accuracy, referencing and readability audit.BMJ open · 2026Article
- The Alberta Quality Assessment Tool: Risk of Bias (AQAT:RoB) for the Evaluation of Medical Large Language Model Question-Answer Studies: Development and Pilot Validation.Journal of medical Internet research · 2026Article
- Indonesian pharmaceutical dataset for self-medication.Data in brief · 2026Article
- Systematic literature review and narrative synthesis of the use of natural language processing to triage outpatient referrals.Frontiers in health services · 2026Review
- Generative AI for pre-consultation mental health triage in disorders of gut-brain interaction.Frontiers in psychiatry · 2026Article
- Safety, accuracy, empathy, reliability, and readability of large language model chatbot responses to public-facing vegetarian and vegan nutrition advice questions: a cross-sectional comparative study.Frontiers in public health · 2026Article
- Quality, readability, and clinical-risk signals of default public-interface LLM responses to vascular and perioperative patient questions: a cross-sectional snapshot.Frontiers in public health · 2026Article
- Evaluation of generative AI-driven chatbots as sources of consumer health information on hand, foot, and mouth disease: a cross-sectional comparative study of safety, accuracy, information quality, readability, and empathy.Frontiers in public health · 2026Article
- Benchmarking public large language model responses to patient-facing inflammatory bowel disease questions: informational quality, transparency proxies, and readability.Frontiers in public health · 2026Article
- A cross-sectional evaluation of large language model chatbot interfaces for patient-facing herpes zoster information: safety, information quality, and readability.Frontiers in public health · 2026Article
- A comparative cross-sectional evaluation of generative AI chatbots for patient-oriented bacterial vaginosis health advice: safety, accuracy, guideline concordance, empathy, and readability.Frontiers in reproductive health · 2026Article
- Artificial Intelligence Chatbots as Sources of Cancer Pain Information: A Comparative Evaluation of Quality, Transparency, and Readability.Journal of pain research · 2026Article
- Towards precision medicine in Tourette syndrome: a perspective on AI-driven predictive modelling and personalised care.Frontiers in computational neuroscience · 2026Review
- Benchmarking public large language model responses to patient-facing varicose veins questions: informational quality, verifiability indicators, and readability.Frontiers in public health · 2026Article
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
The Chatbot Assessment Reporting Tool (CHART) is a reporting guideline developed to provide reporting recommendations for studies evaluating the performance of generative artificial intelligence (AI)-driven chatbots when summarising clinical evidence and providing health advice, referred to as chatbot health advice studies. CHART was developed in several phases after performing a comprehensive systematic review to identify variation in the conduct, reporting, and method in chatbot health advice studies. Findings from the review were used to develop a draft checklist that was revised through an international, multidisciplinary, modified, asynchronous Delphi consensus process of 531 stakeholders, three synchronous panel consensus meetings of 48 stakeholders, and subsequent pilot testing of the checklist. CHART includes 12 items and 39 subitems to promote transparent and comprehensive reporting of chatbot health advice studies. These include title (subitem 1a), abstract/summary (subitem 1b), background (subitems 2a,b), model identifiers (subitems 3a,b), model details (subitems 4a-c), prompt engineering (subitems 5a,b), query strategy (subitems 6a-d), performance evaluation (subitems 7a,b), sample size (subitem 8), data analysis (subitem 9a), results (subitems 10a-c), discussion (subitems 11a-c), disclosures (subitem 12a), funding (subitem 12b), ethics (subitem 12c), protocol (subitem 12d), and data availability (subitem 12e). The CHART checklist and corresponding diagram of the method were designed to support key stakeholders including clinicians, researchers, editors, peer reviewers, and readers in reporting, understanding, and interpreting the findings of chatbot health advice studies.
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.