ArticleAnnals of family medicine2025
Reporting Guideline for Chatbot Health Advice Studies: Chatbot Assessment Reporting Tool (CHART) Statement.
Article in Annals of family medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- Quality and Readability of Four AI Chatbots Answering Search-Derived Patient Questions About Primary Aldosteronism: A Cross-Sectional Comparative Study.Healthcare (Basel, Switzerland) · 2026Article
- Validating LLM judges for automated oversight of patient communication.medRxiv : the preprint server for health sciences · 2026Article
- Reliability and readability of five AI chatbots for concussion health advice across retrieval augmented and pretrained models.Scientific reports · 2026Article
- Benchmarking public large language model responses to patient-facing varicose veins questions: informational quality, verifiability indicators, and readability.Frontiers in public health · 2026Article
- Safety, accuracy, empathy, information quality, and readability of publicly accessible LLM-based chatbots for traumatic brain injury and concussion questions: a cross-sectional comparative study.Frontiers in public health · 2026Article
- Evaluating search-enabled large language model interfaces for mpox public health consultation: a guideline-based comparative study.Frontiers in public health · 2026Article
- Safety, accuracy, empathic communication, information quality, and readability of five large language model interfaces answering public questions about interstitial cystitis/bladder pain syndrome.Frontiers in public health · 2026Article
- Safety and quality of public chatbots for lung cancer prognostic information: a comparative evaluation.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 chatbots driven by generative artificial intelligence when summarizing 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, 3 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. KEY MESSAGES: CHART was developed by performing a systematic review, Delphi consensus of 531 international stakeholders, and several consensus meetings among an expert panel comprised of 48 membersThe CHART statement outlines 12 key reporting items for chatbot health advice studies in the form of a checklist and methodological diagramAll stakeholders including clinicians, researchers, and journal editors should encourage the transparent reporting of chatbot health advice studies.
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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.