Interpreting the STARD-AI reporting guideline: standards for reporting diagnostic accuracy using artificial intelligence_Chinese Journal of Evidence-Based Medicine

Authors：

ZHAO Yisi ^1,2 , PENG Xiran ^1,2 , HAO Xuechao ^1,2 , WANG Yaqiang ^2,3,4 , LI Ke ^2,5 , ZHU Tao ^1,2 ,  CHEN Guo ^1,2 ,  ZHOU Ruihao ^1,2

1. West China Hospital Anesthesia and Surgery Center, Chengdu 610041, P. R. China;
2. STAR Working Group of Specialty Committee on Anesthesiology, Collaborative Group for Anesthesia and Intelligent Medicine Research, Chengdu 610041, P. R. China;
3. Chengdu University of Information Technology, School of Software Engineering, Chengdu 610041, P. R. China;
4. Tianfu Jiangxi Laboratory, Chengdu 641419, P. R. China;
5. School of Statistics, Southwestern University of Finance and Economics, Center for Statistical Research, Joint Laboratory for Data Science and Business Intelligence, Chengdu 610074, P. R. China;

Corresponding?author：

CHEN Guo, Email: chenguohx2023@163.com; ZHOU Ruihao, Email: riozhou@163.com

Keywords：

Diagnostic test accuracy studies; Artificial intelligence; Reporting guidelines; STARD-AI

DOI：

10.7507/1672-2531.202510068

Video：

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As artificial intelligence (AI) becomes increasingly integrated into mainstream healthcare applications, there has been a surge in the number of AI-centered diagnostic test accuracy studies. In September 2025, the international journal Nature Medicine published the Reporting Guideline for Diagnostic Accuracy Studies Using AI (STARD-AI). The guideline represents a comprehensive reporting framework specifically updated for AI-based studies within the Standards for Reporting Diagnostic Accuracy Studies (STARD). It consists of a main checklist (40 major items with 48 sub-items) and an abstract checklist (11 items). This article provides an interpretation of STARD-AI's development process, main content, scope of application, and specific item details, aiming to assist researchers, clinicians, and editors in thoroughly understanding and correctly applying STARD-AI. The goal is to enhance the quality and transparency of reporting in AI-centered diagnostic accuracy studies and to promote their standardized and ethical integration into the medical field.

Citation： ZHAO Yisi, PENG Xiran, HAO Xuechao, WANG Yaqiang, LI Ke, ZHU Tao, CHEN Guo, ZHOU Ruihao. Interpreting the STARD-AI reporting guideline: standards for reporting diagnostic accuracy using artificial intelligence. Chinese Journal of Evidence-Based Medicine, 2026, 26(5): 598-605. doi: 10.7507/1672-2531.202510068 Copy

1.	Reichlin T, Hochholzer W, Bassetti S, et al. Early diagnosis of myocardial infarction with sensitive cardiac troponin assays. N Engl J Med, 2009, 361(9): 858-867.
2.	Neal RD, Tharmanathan P, France B, et al . Is increased time to diagnosis and treatment in symptomatic cancer associated with poorer outcomes? Systematic review. Br J Cancer, 2015, 112(Suppl 1): S92-S107.
3.	Crosby D, Bhatia S, Brindle KM, et al. Early detection of cancer. Science, 2022, 375(6586): eaay9040.
4.	Leifer BP. Early diagnosis of Alzheimer's disease: clinical and economic benefits. J Am Geriatr Soc, 2003, 51(5 Suppl Dementia): S281-S288.
5.	Fleming KA, Horton S, Wilson ML, et al. The Lancet Commission on diagnostics: transforming access to diagnostics. Lancet, 2021, 398(10315): 1997-2050.
6.	Ning Y, Teixayavong S, Shang Y, et al. Generative artificial intelligence and ethical considerations in health care: a scoping review and ethics checklist. Lancet Digit Health, 2024, 6(11): e848-e856.
7.	Sounderajah V, Ashrafian H, Golub RM, et al. Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol. BMJ Open, 2021, 11(6): e047709.
8.	Sounderajah V, Guni A, Liu X, et al. The STARD-AI reporting guideline for diagnostic accuracy studies using artificial intelligence. Nat Med, 2025, 31(10): 3283-3289.
9.	Bossuyt PM, Reitsma JB, Standards for reporting of diagnostic accuracy. The STARD initiative. Lancet. 2003, 361(9351): 71.
10.	Bossuyt PM, Reitsma JB, Bruns DE, et al. STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies. BMJ, 2015, 351: h5527.
11.	Lu R, Zhao X, Li J, et al. Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding. Lancet, 2020, 395(10224): 565-574.
12.	Miao Z, Humphreys BD, McMahon AP, et al. Multi-omics integration in the age of million single-cell data. Nat Rev Nephrol, 2021, 17(11): 710-724.
13.	De Fauw J, Ledsam JR, Romera-Paredes B, et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat Med, 2018, 24(9): 1342-1350.
14.	McKinney SM, Sieniek M, Godbole V, et al. International evaluation of an AI system for breast cancer screening. Nature, 2020, 577(7788): 89-94.
15.	Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med, 2019, 25(1): 44-56.
16.	Benjamens S, Dhunnoo P, Meskó B. The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database. NPJ Digit Med, 2020, 3: 118.
17.	Liu X, Faes L, Kale AU, et al. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. Lancet Digit Health, 2019, 1(6): e271-e297.
18.	王子君, 周奇, 邢丹, 等. 中國臨床實踐指南/共識解讀類文獻報告質量分析. 協和醫學雜志, 2021, 12(2): 260-267.
19.	周奇, 邢丹, 李沁原, 等. 指南解讀類文獻報告規范的制訂指南(RIGHT for INT). 中國循證醫學雜志, 2022, 22(5): 497-507.
20.	Sounderajah V, Ashrafian H, Aggarwal R, et al. Developing specific reporting guidelines for diagnostic accuracy studies assessing AI interventions: The STARD-AI Steering Group. Nat Med, 2020, 26(6): 807-808.
21.	朱一丹, 李會娟, 武陽豐. 診斷準確性研究報告規范(STARD)2015介紹與解讀. 中國循證醫學雜志, 2016, 16(6): 730-735.
22.	Prager R, Bowdridge J, Kareemi H, et al. Adherence to the Standards for Reporting of Diagnostic Accuracy (STARD) 2015 guidelines in acute point-of-care ultrasound research. JAMA Netw Open, 2020, 3(5): e203871.
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24.	CHART Collaborative; Huo B, Collins G, Chartash D, et al. Reporting guideline for chatbot health advice studies: The CHART statement. Artif Intell Med, 2025, 168: 103222.
25.	李澤宇, 詹正哲, 程嘉儀, 等. 基于人工智能的臨床預測模型研究報告規范(TRIPOD+AI)中文解讀. 中國循證醫學雜志, 2025, 25(3): 339-343.
26.	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.
27.	Phua QS, Lu L, White SJ, et al. Systematic review of adherence to the standards for reporting of diagnostic accuracy studies (STARD) 2015 reporting guideline in cerebral aneurysm imaging diagnostic accuracy studies. J Clin Neurosci, 2023, 115: 89-94.

1. Reichlin T, Hochholzer W, Bassetti S, et al. Early diagnosis of myocardial infarction with sensitive cardiac troponin assays. N Engl J Med, 2009, 361(9): 858-867.
2. Neal RD, Tharmanathan P, France B, et al . Is increased time to diagnosis and treatment in symptomatic cancer associated with poorer outcomes? Systematic review. Br J Cancer, 2015, 112(Suppl 1): S92-S107.
3. Crosby D, Bhatia S, Brindle KM, et al. Early detection of cancer. Science, 2022, 375(6586): eaay9040.
4. Leifer BP. Early diagnosis of Alzheimer's disease: clinical and economic benefits. J Am Geriatr Soc, 2003, 51(5 Suppl Dementia): S281-S288.
5. Fleming KA, Horton S, Wilson ML, et al. The Lancet Commission on diagnostics: transforming access to diagnostics. Lancet, 2021, 398(10315): 1997-2050.
6. Ning Y, Teixayavong S, Shang Y, et al. Generative artificial intelligence and ethical considerations in health care: a scoping review and ethics checklist. Lancet Digit Health, 2024, 6(11): e848-e856.
7. Sounderajah V, Ashrafian H, Golub RM, et al. Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol. BMJ Open, 2021, 11(6): e047709.
8. Sounderajah V, Guni A, Liu X, et al. The STARD-AI reporting guideline for diagnostic accuracy studies using artificial intelligence. Nat Med, 2025, 31(10): 3283-3289.
9. Bossuyt PM, Reitsma JB, Standards for reporting of diagnostic accuracy. The STARD initiative. Lancet. 2003, 361(9351): 71.
10. Bossuyt PM, Reitsma JB, Bruns DE, et al. STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies. BMJ, 2015, 351: h5527.
11. Lu R, Zhao X, Li J, et al. Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding. Lancet, 2020, 395(10224): 565-574.
12. Miao Z, Humphreys BD, McMahon AP, et al. Multi-omics integration in the age of million single-cell data. Nat Rev Nephrol, 2021, 17(11): 710-724.
13. De Fauw J, Ledsam JR, Romera-Paredes B, et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat Med, 2018, 24(9): 1342-1350.
14. McKinney SM, Sieniek M, Godbole V, et al. International evaluation of an AI system for breast cancer screening. Nature, 2020, 577(7788): 89-94.
15. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med, 2019, 25(1): 44-56.
16. Benjamens S, Dhunnoo P, Meskó B. The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database. NPJ Digit Med, 2020, 3: 118.
17. Liu X, Faes L, Kale AU, et al. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. Lancet Digit Health, 2019, 1(6): e271-e297.
18. 王子君, 周奇, 邢丹, 等. 中國臨床實踐指南/共識解讀類文獻報告質量分析. 協和醫學雜志, 2021, 12(2): 260-267.
19. 周奇, 邢丹, 李沁原, 等. 指南解讀類文獻報告規范的制訂指南(RIGHT for INT). 中國循證醫學雜志, 2022, 22(5): 497-507.
20. Sounderajah V, Ashrafian H, Aggarwal R, et al. Developing specific reporting guidelines for diagnostic accuracy studies assessing AI interventions: The STARD-AI Steering Group. Nat Med, 2020, 26(6): 807-808.
21. 朱一丹, 李會娟, 武陽豐. 診斷準確性研究報告規范(STARD)2015介紹與解讀. 中國循證醫學雜志, 2016, 16(6): 730-735.
22. Prager R, Bowdridge J, Kareemi H, et al. Adherence to the Standards for Reporting of Diagnostic Accuracy (STARD) 2015 guidelines in acute point-of-care ultrasound research. JAMA Netw Open, 2020, 3(5): e203871.
23. Zarei F, Zeinali-Rafsanjani B. Assessment of adherence of diagnostic accuracy studies published in radiology journals to STARD statement indexed in Web of Science, PubMed & Scopus in 2015. J Biomed Phys Eng. 2018, 8(3): 311-324.
24. CHART Collaborative; Huo B, Collins G, Chartash D, et al. Reporting guideline for chatbot health advice studies: The CHART statement. Artif Intell Med, 2025, 168: 103222.
25. 李澤宇, 詹正哲, 程嘉儀, 等. 基于人工智能的臨床預測模型研究報告規范(TRIPOD+AI)中文解讀. 中國循證醫學雜志, 2025, 25(3): 339-343.
26. 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.
27. Phua QS, Lu L, White SJ, et al. Systematic review of adherence to the standards for reporting of diagnostic accuracy studies (STARD) 2015 reporting guideline in cerebral aneurysm imaging diagnostic accuracy studies. J Clin Neurosci, 2023, 115: 89-94.

Chinese Journal of Evidence-Based Medicine

Interpreting the STARD-AI reporting guideline: standards for reporting diagnostic accuracy using artificial intelligence

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