Ethics of AI in evidence synthesis: an interpretation of the Cochrane joint statement and the RAISE framework_Chinese Journal of Evidence-Based Medicine

Authors：

DING Wenjing ¹ , LIU Yanyan ² , ZOU Zhiguo ³ , LV Xinming ¹ , WU Hui ¹ ,  QIU Xiaochun ¹

1. Shanghai Jiao Tong University School of Medicine, Shanghai 201318, P. R. China;
2. Shanghai Jiao Tong University School of Nursing, Shanghai 201318, P. R. China;
3. Renji Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200127, P. R. China;

Corresponding?author：

QIU Xiaochun, Email: tsg2@shsmu.edu.cn

Keywords：

Artificial intelligence; Evidence synthesis; Evidence-based medicine; Cochrane; RAISE framework

DOI：

10.7507/1672-2531.202512018

Video：

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Abstract Full text Figures/Tables Video References Cited by

As artificial intelligence (AI) is rapidly integrated into evidence synthesis workflows—spanning literature retrieval and deduplication, screening, risk-of-bias assessment, data extraction and structuring, and drafting and updating evidence products. While AI may improve efficiency, it can also compromise methodological rigor through non-reproducible outputs, systematic false exclusions, distorted bias judgments, extraction errors, and inappropriate summarization. Ensuring traceability, accountability, and auditability while leveraging efficiency gains has therefore become a central challenge for the evidence synthesis community. In November 2025, four leading evidence organizations—Cochrane, the Campbell Collaboration, the Joanna Briggs Institute (JBI), and the Collaboration for Environmental Evidence (CEE)—issued a joint statement on the responsible use of AI in evidence synthesis and endorsed the responsible use of AI in evidence synthesis (RAISE) framework. The joint statement articulates core positions, including human accountability, transparent disclosure, and human oversight, whereas RAISE operationalizes these principles through actionable recommendations covering roles and responsibilities within the evidence ecosystem, requirements for building and evaluating AI tools, and ethical considerations for selecting and using AI in specific projects. This paper interprets the key elements of the joint statement and the RAISE framework, presents a scenario-based view of risk control in major stages of evidence synthesis, and discusses practical implications for Chinese-language contexts, including differences in Chinese-language text forms, heterogeneity of traditional Chinese medicine evidence, policy and journal disclosure requirements, and constraints related to cost and accessibility. The analysis aims to provide a structured reference for the responsible, locally applicable use of AI in evidence synthesis in China.

Citation： DING Wenjing, LIU Yanyan, ZOU Zhiguo, LV Xinming, WU Hui, QIU Xiaochun. Ethics of AI in evidence synthesis: an interpretation of the Cochrane joint statement and the RAISE framework. Chinese Journal of Evidence-Based Medicine, 2026, 26(7): 854-860. doi: 10.7507/1672-2531.202512018 Copy

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34.	《中國現代神經疾病雜志》編輯部關于生成式人工智能技術的使用規定. 中國現代神經疾病雜志, 2025, 25(12): 1162.
35.	《中國組織工程研究》雜志關于在論文寫作過程中使用生成式人工智能技術的有關規定. 中國組織工程研究, 2026, 30(9): 2143.
36.	中國腦血管病雜志編輯部關于在論文寫作和評審過程中使用生成式人工智能技術的有關規定. 中國腦血管病雜志, 2025, 22(9): 658.

1. Gurevitch J, Koricheva J, Nakagawa S, et al. Meta-analysis and the science of research synthesis. Nature, 2018, 555(7695): 175-182.
2. None authors listed]. The value of evidence synthesis. Nat Hum Behav, 2021, 5(5): 539.
3. Rose CJ, Meneses-Echavez JF, Muller AE, et al. Artificial intelligence and machine learning to improve evidence synthesis production efficiency: an observational study of resource use and time-to-completion. Cochrane Evid Synth Methods, 2025, 3(3): e70030.
4. Angus DC, Khera R, Lieu T, et al. AI, health, and health care today and tomorrow: the JAMA summit report on artificial intelligence. JAMA, 2025, 334(18): 1650-1664.
5. Haug CJ, Drazen JM. Artificial intelligence and machine learning in clinical medicine, 2023. N Engl J Med, 2023, 388(13): 1201-1208.
6. Berger-Tal O, Wong BBM, Adams CA, et al. Leveraging AI to improve evidence synthesis in conservation. Trends Ecol Evol, 2024, 39(6): 548-557.
7. Abogunrin S, Muir JM, Zerbini C, et al. How much can we save by applying artificial intelligence in evidence synthesis. Results from a pragmatic review to quantify workload efficiencies and cost savings. Front Pharmacol, 2025, 16: 1454245.
8. 鄧宏勇. 從循證中醫到智慧中醫: 自動系統評價技術和中醫快速循證決策. 上海中醫藥雜志, 2024, 58(3): 1-5.
9. 賴鴻皓, 馬寧, 趙威龍, 等. 數智循證醫學: AI時代循證研究與實踐新方向. 科學通報, 2026, 71(2): 449-463.
10. Trevi?o-Juarez AS. Assessing risk of bias using ChatGPT-4 and Cochrane ROB2 tool. Med Sci Educ, 2024, 34(3): 691-694.
11. Siemens W, von Elm E, Binder H, et al. Opportunities, challenges and risks of using artificial intelligence for evidence synthesis. BMJ Evid Based Med, 2025, 30(6): 381-384.
12. 羅姚, 談在祥. ChatGPT對循證醫學發展的影響、挑戰及其應對. 醫學與哲學, 2024, 45(12): 16-21.
13. 劉鳴, 宋心迪, 王亞楠. 從循證臨床決策角度看人工智能應用的相關問題. 華西醫學, 2025, 40(5): 685-688.
14. Flemyng E, Noel-Storr A, Macura B, et al. Position statement on artificial intelligence (AI) use in evidence synthesis across Cochrane, the Campbell Collaboration, JBI, and the Collaboration for Environmental Evidence 2025. Campbell Syst Rev, 2025, 21(4): e70074.
15. Thomas J, Flemyng E, Noel-Storr A, et al. Responsible use of AI in evidence SynthEsis (RAISE) 1: recommendations for practice. In: open science framework. Center for Open Science.
16. Thomas J, Flemyng E, Noel-Storr A, et al. Responsible use of AI in evidence SynthEsis (RAISE) 2: building and evaluating AI evidence synthesis tools. In: open science framework. Center for Open Science.
17. Thomas J, Flemyng E, Noel-Storr A, et al. Responsible use of AI in evidence SynthEsis (RAISE) 3: selecting and using AI evidence synthesis tools. In: open science framework. Center for Open Science.
18. Badawy W, Zinhom H, Shaban M. Navigating ethical considerations in the use of artificial intelligence for patient care: a systematic review. Int Nurs Rev, 2025, 72(3): e13059.
19. Ohu FC, Burrell DN, Jones LA. Public health risk management, policy, and ethical imperatives in the use of AI tools for mental health therapy. Healthcare (Basel), 2025, 13(21): 2721.
20. 宋麗君, 鄧蕊. AI預測死亡技術的倫理挑戰與應對策略. 中國醫學倫理學, 2026, 1-8 (網絡首發).
21. The Cochrane colloquium tool for addressing conflicts of interest in trials (TACIT)—background and rationale. 2019.
22. Hunter KE, Aberoumand M, Libesman S, et al. The individual participant data integrity tool for assessing the integrity of randomised trials. Res Synth Methods, 2024, 15(6): 917-939.
23. Wilkinson J, Heal C, Antoniou GA, et al. Protocol for the development of a tool (INSPECT-SR) to identify problematic randomised controlled trials in systematic reviews of health interventions. BMJ Open, 2024, 14(3): e084164.
24. Yuh MN, Ndum Okwen GA, Miong RHP, et al. Using an innovative family-centered evidence toolkit to improve the livelihood of people with disabilities in Bamenda (Cameroon): a mixed-method study. Front Public Health, 2024, 11: 11907223.
25. Blaizot A, Veettil SK, Saidoung P, et al. Using artificial intelligence methods for systematic review in health sciences: a systematic review. Res Synth Methods, 2022, 13(3): 353-362.
26. Ge L, Agrawal R, Singer M, et al. Leveraging artificial intelligence to enhance systematic reviews in health research: advanced tools and challenges. Syst Rev, 2024, 13: 269.
27. 宋填, 郭敬, 劉斌, 等. 大語言模型在分析性研究偏倚風險評估中的準確性評價. 中國循證醫學雜志, 2025, 25(5): 562-567.
28. 郭玉杰, 張雪芹, 孫文宇, 等. 自動化文獻篩選工具在系統評價中的應用. 協和醫學雜志, 2024, 15(4): 921-926.
29. 鄭卿勇, 周泳佳, 張夢君, 等. 人工智能推動系統評價與Meta分析自動化工具發展. 中國循證醫學雜志, 2025, 25(11): 1340-1349.
30. 胡源暉, 鄧偉祺, 李夏妍, 等. 2024全球證據峰會: 熱點議題與人工智能在循證醫學中的應用. 中國醫院用藥評價與分析, 2025, 25(2): 129-133, 136.
31. 國家新一代人工智能治理專業委員會. 新一代人工智能治理原則—發展負責任的人工智能, 2019.
32. 國家新一代人工智能治理專業委員會. 新一代人工智能倫理規范, 2021.
33. 國家互聯網信息辦公室, 中華人民共和國國家發展和改革委員會, 中華人民共和國教育部, 中華人民共和國科學技術部, 中華人民共和國工業和信息化部, 中華人民共和國公安部, 國家廣播電視總局. 生成式人工智能服務管理暫行辦法, 2023.
34. 《中國現代神經疾病雜志》編輯部關于生成式人工智能技術的使用規定. 中國現代神經疾病雜志, 2025, 25(12): 1162.
35. 《中國組織工程研究》雜志關于在論文寫作過程中使用生成式人工智能技術的有關規定. 中國組織工程研究, 2026, 30(9): 2143.
36. 中國腦血管病雜志編輯部關于在論文寫作和評審過程中使用生成式人工智能技術的有關規定. 中國腦血管病雜志, 2025, 22(9): 658.

Chinese Journal of Evidence-Based Medicine

Ethics of AI in evidence synthesis: an interpretation of the Cochrane joint statement and the RAISE framework

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