Interpretation of the TRIPOD-LLM reporting guideline for studies using large language models_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

ZHOU Xiaoqin ¹ , LIU Huizhen ¹ , WANG Ting ¹ ,  LIU Xuemei ² ,  KANG Deying ¹

1. Center of Biostatistics, Design, Measurement and Evaluation (CBDME), Department of Clinical Research Management, West China Hospital, Sichuan University, Chengdu, 610041, P. R. China;
2. Editorial Department of Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, West China Periodicals Press of West China Hospital, Sichuan University, Chengdu, 610041, P. R. China;

Corresponding author：

LIU Xuemei, Email: liuxuemei@wchscu.cn; KANG Deying, Email: deyingkang@126.com

Keywords：

Large language model; artificial intelligence; predictive models; reporting guideline; TRIPOD-LLM; interpretation

DOI：

10.7507/1007-4848.202503064

Video：

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As the volume of medical research using large language models (LLM) surges, the need for standardized and transparent reporting standards becomes increasingly critical. In January 2025, Nature Medicine published statement titled by TRIPOD-LLM reporting guideline for studies using large language models. This represents the first comprehensive reporting framework specifically tailored for studies that develop prediction models based on LLM. It comprises a checklist with 19 main items (encompassing 50 sub-items), a flowchart, and an abstract checklist (containing 12 items). This article provides an interpretation of TRIPOD-LLM’s development methods, primary content, scope, and the specific details of its items. The goal is to help researchers, clinicians, editors, and healthcare decision-makers to deeply understand and correctly apply TRIPOD-LLM, thereby improving the quality and transparency of LLM medical research reporting and promoting the standardized and ethical integration of LLM into healthcare.

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3.	Heus P, Reitsma JB, Collins GS, et al. Transparent reporting of multivariable prediction models in Journal and Conference Abstracts: TRIPOD for Abstracts. Ann Intern Med, 2020, 173(1): 42-47.
4.	Debray TPA, Collins GS, Riley RD, et al. Transparent reporting of multivariable prediction models developed or validated using clustered data: TRIPOD-custer checklist. BMJ, 2023, 380: e071018.
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6.	Snell KIE, Levis B, Damen JAA, et al. Transparent reporting of multivariable prediction models for individual prognosis or diagnosis: Checklist for systematic reviews and meta-analyses (TRIPOD-SRMA). BMJ, 2023, 381: e073538.
7.	Dhiman P, Whittle R, Van Calster B, et al. The TRIPOD-P reporting guideline for improving the integrity and transparency of predictive analytics in healthcare through study protocols. Nat Mach Intell, 2023, 5(8): 816-817.
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15.	Khalid SI, Massaad E, Roy JM, et al. An Appraisal of the quality of development and reporting of predictive models in neurosurgery: A systematic review. Neurosurg, 2025, 96(2): 269-275.
16.	Collins GS, Moons KGM, Dhiman P, et al. TRIPOD+AI statement: Updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ, 2024, 385: e078378.
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21.	AlSaad R, Abd-Alrazaq A, Boughorbel S, et al. Multimodal large language models in health care: Applications, challenges, and future outlook. J Med Internet Res, 2024, 26: e59505.
22.	He K, Mao R, Lin Q, et al. A survey of large language models for healthcare: From data, technology, and applications to accountability and ethics. Inf Fusion, 2025, 118: 102963.
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24.	Shool S, Adimi S, Saboori Amleshi R, et al. A systematic review of large language model (LLM) evaluations in clinical medicine. BMC Med Inform Decis Mak, 2025, 25(1): 117.
25.	Lieberum JL, Töws M, Metzendorf MI, et al. Large language models for conducting systematic reviews: On the rise, but not yet ready for use: A scoping review. J Clin Epidemiol, 2025, 181: 111746.
26.	Cacciamani GE, Collins GS, Gill IS. ChatGPT: Standard reporting guidelines for responsible use. Nature, 2023, 618(7964): 238.
27.	Huo B, Cacciamani GE, Collins GS, et al. Reporting standards for the use of large language model-linked Chatbots for health advice. Nat Med, 2023, 29(12): 2988.

1. Collins GS, Reitsma JB, Altman DG, et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement. BMJ, 2015, 350: g7594.
2. Moons KG, Altman DG, Reitsma JB, et al. Transparent reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): Explanation and elaboration. Ann Intern Med, 2015, 162(1): W1-W73.
3. Heus P, Reitsma JB, Collins GS, et al. Transparent reporting of multivariable prediction models in Journal and Conference Abstracts: TRIPOD for Abstracts. Ann Intern Med, 2020, 173(1): 42-47.
4. Debray TPA, Collins GS, Riley RD, et al. Transparent reporting of multivariable prediction models developed or validated using clustered data: TRIPOD-custer checklist. BMJ, 2023, 380: e071018.
5. Debray TPA, Collins GS, Riley RD, et al. Transparent reporting of multivariable prediction models developed or validated using clustered data (TRIPOD-Cluster): Explanation and elaboration. BMJ, 2023, 380: e071058.
6. Snell KIE, Levis B, Damen JAA, et al. Transparent reporting of multivariable prediction models for individual prognosis or diagnosis: Checklist for systematic reviews and meta-analyses (TRIPOD-SRMA). BMJ, 2023, 381: e073538.
7. Dhiman P, Whittle R, Van Calster B, et al. The TRIPOD-P reporting guideline for improving the integrity and transparency of predictive analytics in healthcare through study protocols. Nat Mach Intell, 2023, 5(8): 816-817.
8. Lex JR, Di Michele J, Koucheki R, et al. Artificial intelligence for hip fracture detection and outcome prediction: A systematic review and meta-analysis. JAMA Netw Open, 2023, 6(3): e233391.
9. Issaiy M, Zarei D, Saghazadeh A. Artificial intelligence and acute appendicitis: A systematic review of diagnostic and prognostic models. World J Emerg Surg, 2023, 18(1): 59.
10. Smith LA, Oakden-Rayner L, Bird A, et al. Machine learning and deep learning predictive models for long-term prognosis in patients with chronic obstructive pulmonary disease: A systematic review and meta-analysis. Lancet Digit Health, 2023, 5(12): e872-e881.
11. Alsaleh MM, Allery F, Choi JW, et al. Prediction of disease comorbidity using explainable artificial intelligence and machine learning techniques: A systematic review. Int J Med Inform, 2023, 175: 105088.
12. Usman TM, Saheed YK, Nsang A, et al. A systematic literature review of machine learning based risk prediction models for diabetic retinopathy progression. Artif Intell Med, 2023, 143: 102617.
13. Altuhaifa FA, Win KT, Su G. Predicting lung cancer survival based on clinical data using machine learning: A review. Comput Biol Med, 2023, 165: 107338.
14. van Royen FS, Asselbergs FW, Alfonso F, et al. Five critical quality criteria for artificial intelligence-based prediction models. Eur Heart J, 2023, 44(46): 4831-4834.
15. Khalid SI, Massaad E, Roy JM, et al. An Appraisal of the quality of development and reporting of predictive models in neurosurgery: A systematic review. Neurosurg, 2025, 96(2): 269-275.
16. Collins GS, Moons KGM, Dhiman P, et al. TRIPOD+AI statement: Updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ, 2024, 385: e078378.
17. Bedi S, Liu Y, Orr-Ewing L, et al. Testing and evaluation of health care applications of large language models: A systematic review. JAMA, 2025, 333(4): 319-328.
18. Cao Z, Keloth VK, Xie Q, et al. The development landscape of large language models for biomedical applications. Annu Rev Biomed Data Sci, 2025. [Epub ahead of print].
19. Gallifant J, Afshar M, Ameen S, et al. The TRIPOD-LLM reporting guideline for studies using large language models. Nat Med, 2025, 31(1): 60-69.
20. Omiye JA, Gui H, Rezaei SJ, et al. Large language models in medicine: the potentials and pitfalls: A narrative review. Ann Intern Med, 2024, 177(2): 210-220.
21. AlSaad R, Abd-Alrazaq A, Boughorbel S, et al. Multimodal large language models in health care: Applications, challenges, and future outlook. J Med Internet Res, 2024, 26: e59505.
22. He K, Mao R, Lin Q, et al. A survey of large language models for healthcare: From data, technology, and applications to accountability and ethics. Inf Fusion, 2025, 118: 102963.
23. Meskó B, Topol EJ. The imperative for regulatory oversight of large language models (or generative AI) in healthcare. NPJ Digit Med, 2023, 6(1): 120.
24. Shool S, Adimi S, Saboori Amleshi R, et al. A systematic review of large language model (LLM) evaluations in clinical medicine. BMC Med Inform Decis Mak, 2025, 25(1): 117.
25. Lieberum JL, Töws M, Metzendorf MI, et al. Large language models for conducting systematic reviews: On the rise, but not yet ready for use: A scoping review. J Clin Epidemiol, 2025, 181: 111746.
26. Cacciamani GE, Collins GS, Gill IS. ChatGPT: Standard reporting guidelines for responsible use. Nature, 2023, 618(7964): 238.
27. Huo B, Cacciamani GE, Collins GS, et al. Reporting standards for the use of large language model-linked Chatbots for health advice. Nat Med, 2023, 29(12): 2988.

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