Interpreting the CHART statement: an example-based guide for reporting chatbot health advice studies_Chinese Journal of Evidence-Based Medicine

Authors：

ZHANG Mengjun ¹ , ZHENG Qingyong ² , SHI Zhongli ³ , YANG Xiaomei ⁴ , DU Li ⁵ ,  JIA Youchao ⁶

1. College of Nursing, Hebei University, Baoding 071000, P. R. China;
2. Evidence-Based Medicine, School of Basic Medical Sciences, Lanzhou University, Lanzhou 730000, P. R. China;
3. The Department of Mental and Psychological Nursing of the Nursing College of Jining Medical University, Jining 272067, P. R. China;
4. Department of Oncology, Affiliated Hospital of Hebei University, Baoding 071000, P. R. China;
5. The Third People’s Hospital of Lanzhou, Lanzhou 730050, P. R. China;
6. Department of Oncology, Affiliated Hospital of Hebei University, Key Laboratory of Mechanism and Protocol of Tumor Radiotherapy and Chemotherapy in Hebei Province, Baoding 071000, P. R. China;

Corresponding?author：

JIA Youchao, Email: youchaojia1@163.com

Keywords：

Chatbot; Large language model (LLM); Healthcare AI; Health advice; CHART reporting guideline

DOI：

10.7507/1672-2531.202508080

Video：

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Studies on chatbot health advice (CHA) driven by large language models are rapidly increasing, yet their reporting is marked by significant heterogeneity and incompleteness, which severely limits the scientific credibility and reproducibility of their findings. To promote the effective dissemination and application of the newly released chatbot assessment reporting tool (CHART) statement, this paper provides a systematic interpretation and example-based analysis of the guideline. This paper dissects the 12 main items and 39 sub-items of the CHART checklist on an item-by-item basis, systematically elaborating on the methodological rationale behind each reporting requirement. A particular focus is placed on key requirements tailored to the unique characteristics of generative AI, such as the transparent disclosure of prompt engineering, query strategies, and dialogue safety. To bridge the gap between theory and practice, a high-quality, published CHA study is used as an exemplar to demonstrate the practical application of each reporting item. This interpretation report aims to provide a clear and practical handbook for researchers, journal reviewers, and editors, with the goal of fostering standardized, high-quality development in the field of CHA research and promoting the safe and effective application of AI in healthcare.

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2.	Peng C, Yang X, Chen A, et al. A study of generative large language model for medical research and healthcare. NPJ Digit Med, 2023, 6(1): 210.
3.	鄭卿勇, 吳景玲, 李莫蘭, 等. TRIPOD-LLM指南解讀: 規范醫療領域大語言模型研究的報告標準. 中國循證醫學雜志, 2025, 25(8): 967-978.
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6.	Aggarwal A, Tam CC, Wu D, et al. Artificial intelligence-based chatbots for promoting health behavioral changes: systematic review. J Med Internet Res, 2023, 25: e40789.
7.	Eysenbach G. The role of chatgpt, generative language models, and artificial intelligence in medical education: a conversation with chatgpt and a call for papers. JMIR Med Educ, 2023, 9: e46885.
8.	Huo B, Boyle A, Marfo N, et al. Large language models for chatbot health advice studies: a systematic review. JAMA Netw Open, 2025, 8(2): e2457879.
9.	Tawfik E, Ghallab E, Moustafa A. A nurse versus a chatbot ? the effect of an empowerment program on chemotherapy-related side effects and the self-care behaviors of women living with breast cancer: a randomized controlled trial. BMC Nurs, 2023, 22(1): 102.
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12.	Neo JRE, Ser JS, Tay SS. Use of large language model-based chatbots in managing the rehabilitation concerns and education needs of outpatient stroke survivors and caregivers. Front Digit Health, 2024, 6: 1395501.
13.	姚麗波, 苗俊杰, 代敏. 基于DeepSeek和知識庫的醫學科研管理智能問答系統構建與應用. 中國衛生事業管理, 2025, 42(8): 920-924.
14.	譚平, 劉惠娜, 韋昌法. 融合大語言模型與知識圖譜的抑郁癥中西醫結合智能問答系統構建研究. 上海中醫藥雜志, 2025, 59(7): 1-10.
15.	Van Veen D, Van Uden C, Blankemeier L, et al. Adapted large language models can outperform medical experts in clinical text summarization. Nat Med, 2024, 30(4): 1134-1142.
16.	Huo B, Collins GS, Chartash D, et al. Reporting guidelines for chatbot health advice studies: explanation and elaboration for the chatbot assessment reporting tool (CHART). BMJ, 2025, 390: e083305.
17.	Liu X, Cruz Rivera S, Moher D, et al. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Nat Med, 2020, 26(9): 1364-1374.
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19.	CHART Collaborative, Huo B, Collins GS, et al. Reporting guideline for chatbot health advice studies: the CHART statement. JAMA Netw Open, 2025, 8(8): e2530220.
20.	CHART Collaborative. Reporting guideline for chatbot health advice studies: chatbot assessment reporting tool (CHART) statement. Ann Fam Med, 2025, 23(5): 389-398.
21.	Huo B, Collins G, Chartash D, et al. Reporting guideline for chatbot health advice studies: the CHART statement. BMC Med, 2025, 23(1): 447.
22.	CHART Collaborative, Huo B, Collins G, et al. Reporting guideline for chatbot health advice studies: the CHART statement. Artif Intell Med, 2025, 168: 103222.
23.	Zhang D, Du H, Wang X, et al. CMedRAGBot: a Chinese medical chatbot based on graph RAG and large language models. Interdiscip Sci, 2025, 17(4): 844-859.
24.	Marks M, Haupt CE. AI chatbots, health privacy, and challenges to HIPAA compliance. JAMA, 2023, 330(4): 309-310.
25.	汪琛. 醫療人工智能倫理治理的問題、困境與求解. 科學學研究, 2025, 43(2): 414-422.

1. Thirunavukarasu AJ, Ting DSJ, Elangovan K, et al. Large language models in medicine. Nat Med, 2023, 29(8): 1930-1940.
2. Peng C, Yang X, Chen A, et al. A study of generative large language model for medical research and healthcare. NPJ Digit Med, 2023, 6(1): 210.
3. 鄭卿勇, 吳景玲, 李莫蘭, 等. TRIPOD-LLM指南解讀: 規范醫療領域大語言模型研究的報告標準. 中國循證醫學雜志, 2025, 25(8): 967-978.
4. Kuperberg SJ, Christiani DC. Artificial intelligence-based methods: the path forward in achieving equity in lung cancer screening and evaluation. CancerInnov, 2025, 4(4): e70019.
5. Iqbal U, Tanweer A, Rahmanti AR, et al. Impact of large language model (ChatGPT) in healthcare: an umbrella review and evidence synthesis. J Biomed Sci, 2025, 32(1): 45.
6. Aggarwal A, Tam CC, Wu D, et al. Artificial intelligence-based chatbots for promoting health behavioral changes: systematic review. J Med Internet Res, 2023, 25: e40789.
7. Eysenbach G. The role of chatgpt, generative language models, and artificial intelligence in medical education: a conversation with chatgpt and a call for papers. JMIR Med Educ, 2023, 9: e46885.
8. Huo B, Boyle A, Marfo N, et al. Large language models for chatbot health advice studies: a systematic review. JAMA Netw Open, 2025, 8(2): e2457879.
9. Tawfik E, Ghallab E, Moustafa A. A nurse versus a chatbot ? the effect of an empowerment program on chemotherapy-related side effects and the self-care behaviors of women living with breast cancer: a randomized controlled trial. BMC Nurs, 2023, 22(1): 102.
10. Vorensky M, Peredo D, Ferraro R, et al. Improving chatgpt's performance in orthopedics: opportunities using the CRISPE framework. JOSPT Methods, 2025, 1(2): 56-60.
11. He W, Zhang W, Jin Y, et al. Physician versus large language model chatbot responses to web-based questions from autistic patients in Chinese: cross-sectional comparative analysis. J Med Internet Res, 2024, 26: e54706.
12. Neo JRE, Ser JS, Tay SS. Use of large language model-based chatbots in managing the rehabilitation concerns and education needs of outpatient stroke survivors and caregivers. Front Digit Health, 2024, 6: 1395501.
13. 姚麗波, 苗俊杰, 代敏. 基于DeepSeek和知識庫的醫學科研管理智能問答系統構建與應用. 中國衛生事業管理, 2025, 42(8): 920-924.
14. 譚平, 劉惠娜, 韋昌法. 融合大語言模型與知識圖譜的抑郁癥中西醫結合智能問答系統構建研究. 上海中醫藥雜志, 2025, 59(7): 1-10.
15. Van Veen D, Van Uden C, Blankemeier L, et al. Adapted large language models can outperform medical experts in clinical text summarization. Nat Med, 2024, 30(4): 1134-1142.
16. Huo B, Collins GS, Chartash D, et al. Reporting guidelines for chatbot health advice studies: explanation and elaboration for the chatbot assessment reporting tool (CHART). BMJ, 2025, 390: e083305.
17. Liu X, Cruz Rivera S, Moher D, et al. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Nat Med, 2020, 26(9): 1364-1374.
18. Cohen JF, Bossuyt PMM. TRIPOD+AI: an updated reporting guideline for clinical prediction models. BMJ, 2024, 385: q824.
19. CHART Collaborative, Huo B, Collins GS, et al. Reporting guideline for chatbot health advice studies: the CHART statement. JAMA Netw Open, 2025, 8(8): e2530220.
20. CHART Collaborative. Reporting guideline for chatbot health advice studies: chatbot assessment reporting tool (CHART) statement. Ann Fam Med, 2025, 23(5): 389-398.
21. Huo B, Collins G, Chartash D, et al. Reporting guideline for chatbot health advice studies: the CHART statement. BMC Med, 2025, 23(1): 447.
22. CHART Collaborative, Huo B, Collins G, et al. Reporting guideline for chatbot health advice studies: the CHART statement. Artif Intell Med, 2025, 168: 103222.
23. Zhang D, Du H, Wang X, et al. CMedRAGBot: a Chinese medical chatbot based on graph RAG and large language models. Interdiscip Sci, 2025, 17(4): 844-859.
24. Marks M, Haupt CE. AI chatbots, health privacy, and challenges to HIPAA compliance. JAMA, 2023, 330(4): 309-310.
25. 汪琛. 醫療人工智能倫理治理的問題、困境與求解. 科學學研究, 2025, 43(2): 414-422.

Chinese Journal of Evidence-Based Medicine

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