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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[Abstract ]As the volume of medical research using large language models (LLMs) surges, the need for standardized and transparent reporting standards becomes increasingly critical. In January 2025, Nature Medicine published “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 LLMs. 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 LLMs into healthcare.

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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.
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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.

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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