The new wave of evidence revolution: paradigm evolution, challenges, and future of living evidence (LE) synthesis_Chinese Journal of Evidence-Based Medicine

Authors：

ZHENG Qingyong ^1,2,3 , ZHOU Yongjia ^1,2,3 , TIAN Min ^1,2,3 , CUI Yating ^1,2,3 , XU Caihua ^1,2,3 , XU Jianguo ^1,2,3 , TIAN Chen ^2,4,5,6 , GE Long ^2,4,5,6 ,  ZHANG Junhua ^7,8 ,  TIAN Jinhui ^1,2,3

1. Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou 730000, P. R. China;
2. Key Laboratory of Evidence-Based Medicine of Gansu Province, Lanzhou 730000, P. R. China;
3. Research Unit of Evidence-Based Evaluation and Guidelines, Chinese Academy of Medical Sciences, Lanzhou 730000, P. R. China;
4. Department of Health Policy and Management, School of Public Health, Lanzhou 730000, P. R. China;
5. Laboratory of Cross-Innovation for Evidence-based Social Sciences, Lanzhou 730000, P. R. China;
6. Research Centre for Health Management and Health Development, Lanzhou University, Lanzhou 730000, P. R. China;
7. Evidence-Based Medicine Center, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, P. R. China;
8. Key Laboratory of Evidence-Based Evaluation of Traditional Chinese Medicine, National Medical Products Administration, Tianjin 301617, P. R. China;

Corresponding?author：

ZHANG Junhua, Email: zjhtcm@163.com; TIAN Jinhui, Email: tjh996@163.com

Keywords：

Living evidence synthesis; Evidence-based medicine; Artificial intelligence; Human-machine collaboration; Evidence ecosystem

DOI：

10.7507/1672-2531.202512009

Video：

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Abstract Full text Figures/Tables Video References Cited by

The contradiction between the exponential growth of medical knowledge and the delay in traditional evidence synthesis has necessitated the living evidence (LE) synthesis model, which focuses on continuous updates. The concept of LE synthesis has evolved into a dynamic full-lifecycle spectrum ranging from evidence maps and systematic reviews to clinical guidelines. Although the framework by Bendersky et al. established a methodological baseline for regulating this complex system, its reliance on human labor and a "process-driven" logic creates significant efficiency bottlenecks when dealing with massive heterogeneous data. In the context of the technological revolution driven by AI and large language models (LLMs), this paper analyzes the limitations of current frameworks and demonstrates the necessity of evolving from "mechanical process execution" to a paradigm of "human-machine collaborative cognitive augmentation". This shift demands a restructuring of how evidence is discovered and integrated. Furthermore, by combining China's advantages in clinical data resources with current standardization efforts, we explore localized pathways that integrate technological foresight with evidence-based rigor. This aims to build a smart, dynamic evidence ecosystem dedicated to high-quality health decision-making.

Citation： ZHENG Qingyong, ZHOU Yongjia, TIAN Min, CUI Yating, XU Caihua, XU Jianguo, TIAN Chen, GE Long, ZHANG Junhua, TIAN Jinhui. The new wave of evidence revolution: paradigm evolution, challenges, and future of living evidence (LE) synthesis. Chinese Journal of Evidence-Based Medicine, 2026, 26(7): 847-853. doi: 10.7507/1672-2531.202512009 Copy

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2.	Martínez García L, Sanabria AJ, García Alvarez E, et al. The validity of recommendations from clinical guidelines: a survival analysis. CMAJ, 2014, 186(16): 1211-1219.
3.	Shekelle PG, Ortiz E, Rhodes S, et al. Validity of the agency for healthcare research and quality clinical practice guidelines: how quickly do guidelines become outdated. JAMA, 2001, 286(12): 1461-1467.
4.	Zheng Q, Xu J, Gao Y, et al. Past, present and future of living systematic review: a bibliometrics analysis. BMJ Glob Health, 2022, 7(10): e009378.
5.	鄭卿勇, 程露穎, 許建國, 等. 動態系統評價的研究現狀與進展. 中國藥物評價, 2021, 38(6): 471-478.
6.	Bendersky J, Auladell-Rispau A, Urrútia G, et al. Methods for developing and reporting living evidence synthesis. J Clin Epidemiol, 2022, 152: 89-100.
7.	Zhang K, Yang X, Wang Y, et al. Artificial intelligence in drug development. Nat Med, 2025, 31(1): 45-59.
8.	Zhang Y, Khan SA, Mahmud A, et al. Exploring the role of large language models in the scientific method: from hypothesis to discovery. NPJ Artific Intell, 2025, 1(1): 14.
9.	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.
10.	Khondker A, Kwong JCC, Rickard M, et al. AI-PEDURO - artificial intelligence in pediatric urology: protocol for a living scoping review and online repository. J Pediatr Urol, 2025, 21(2): 532-538.
11.	R?st TB, Slaughter L, Nytr? ?, et al. Using neural networks to support high-quality evidence mapping. BMC Bioinformatics, 2021, 22(Suppl 11): 496.
12.	Hirt J, Adlbrecht L, Maurer C, et al. Exploring experiences of times without care and encounters in dementia: protocol for a living and adaptive evidence map. BMJ Open, 2023, 13(9): e075664.
13.	Zhao J, Bai W, Zhang Q, et al. Evidence-based practice implementation in healthcare in China: a living scoping review. Lancet Reg Health West Pac, 2022, 20: 100355.
14.	Créquit P, Martin-Montoya T, Attiche N, et al. Living network meta-analysis was feasible when considering the pace of evidence generation. J Clin Epidemiol, 2019, 108: 10-16.
15.	Negrini S, Ceravolo MG, C?té P, et al. A systematic review that is “rapid” and “living”: a specific answer to the COVID-19 pandemic. J Clin Epidemiol, 2021, 138: 194-198.
16.	Ciapponi A, Bardach A, Berrueta M, et al. A global living systematic review and meta-analysis hub of emerging vaccines in pregnancy and childhood. Reprod Health, 2025, 22(1): 129.
17.	Pussegoda K, Corrin T, Baumeister A, et al. Methods for conducting a living evidence profile on mpox: an evidence map of the literature. Cochrane Evid Synth Methods, 2024, 2(2): e12044.
18.	Hendriks LEL, Cortiula F, Martins-Branco D, et al. Updated treatment recommendations for systemic treatment: from the ESMO oncogene-addicted metastatic NSCLC living guideline. Ann Oncol, 2025, 36(10): 1227-1231.
19.	Merlin T, Street J, Carter D, et al. Challenges in the evaluation of emerging highly specialised technologies: is there a role for living HTA. Appl Health Econ Health Policy, 2023, 21(6): 823-830.
20.	Cuker A, Tseng EK, Nieuwlaat R, et al. American Society of Hematology 2021 guidelines on the use of anticoagulation for thromboprophylaxis in patients with COVID-19. Blood Adv, 2021, 5(3): 872-888.
21.	Gartlehner G, Kugley S, Crotty K, et al. Artificial intelligence-assisted data extraction with a large language model: a study within reviews. Ann Intern Med, 2025, 178(12): 1763-1771.
22.	Forero DA, Abreu SE, Tovar BE, et al. Automated analyses of risk of bias and critical appraisal of systematic reviews (ROBIS and AMSTAR 2): a comparison of the performance of 4 large language models. J Am Med Inform Assoc, 2025, 32(9): 1471-1476.
23.	Van De Schoot R, De Bruin J, Schram R, et al. An open source machine learning framework for efficient and transparent systematic reviews. Nat Mach Intell, 2021, 3(2): 125-133.
24.	Liu L, Blake V, Barman M, et al. Using natural language processing to extract information from clinical text in electronic medical records for populating clinical registries: a systematic review. J Am Med Inform Assoc, 2025: ocaf176.
25.	Wieland-Jorna Y, van Kooten D, Verheij RA, et al. Natural language processing systems for extracting information from electronic health records about activities of daily living. A systematic review. JAMIA Open, 2024, 7(2): ooae044.
26.	Gliozzo J, Mesiti M, Notaro M, et al. Heterogeneous data integration methods for patient similarity networks. Brief Bioinform, 2022, 23(4): bbac207.
27.	Marques-Cruz M, Pinto F, Vieira RJ, et al. Use of artificial intelligence to support the assessment of the methodological quality of systematic reviews. J Clin Epidemiol, 2025, 187: 111944.
28.	Lieberum JL, Toews 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.
29.	Cui H, Yasseri T. AI-enhanced collective intelligence. Patterns (N Y), 2024, 5(11): 101074.
30.	Kong X, Fang H, Chen W, et al. Examining human-AI collaboration in hybrid intelligence learning environments: insight from the Synergy Degree Model. Humanit Soc Sci Commun, 2025, 12(1): 821.
31.	Boyer A, Farzaneh F, Pitrone A. Leveraging human-machine collaborative mutual learning to optimize performance. 2023.
32.	Sivarajkumar S, Kelley M, Samolyk-Mazzanti A, et al. An empirical evaluation of prompting strategies for large language models in zero-shot clinical natural language processing: algorithm development and validation study. JMIR Med Inform, 2024, 12: e55318.
33.	Li R, Yang Y, Wu S, et al. Using artificial intelligence to improve medical services in China. Ann Transl Med, 2020, 8(11): 711.
34.	Zhong J, Zhang J, Fang H, et al. Advancing the development of real-world data for healthcare research in China: challenges and opportunities. BMJ Open, 2022, 12(7): e063139.
35.	Zhou E, Shen Q, Hou Y. Integrating artificial intelligence into the modernization of traditional Chinese medicine industry: a review. Front Pharmacol, 2024, 15: 1181183.
36.	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.
37.	毛渤淳, 陳圣愷, 謝雨, 等. 經典深度學習算法對中文隨機對照試驗智能判別應用. 中國循證醫學雜志, 2019, 19(11): 1262-1267.

1. Lehane E, Leahy-Warren P, O'Riordan C, et al. Evidence-based practice education for healthcare professions: an expert view. BMJ Evid Based Med, 2019, 24(3): 103-108.
2. Martínez García L, Sanabria AJ, García Alvarez E, et al. The validity of recommendations from clinical guidelines: a survival analysis. CMAJ, 2014, 186(16): 1211-1219.
3. Shekelle PG, Ortiz E, Rhodes S, et al. Validity of the agency for healthcare research and quality clinical practice guidelines: how quickly do guidelines become outdated. JAMA, 2001, 286(12): 1461-1467.
4. Zheng Q, Xu J, Gao Y, et al. Past, present and future of living systematic review: a bibliometrics analysis. BMJ Glob Health, 2022, 7(10): e009378.
5. 鄭卿勇, 程露穎, 許建國, 等. 動態系統評價的研究現狀與進展. 中國藥物評價, 2021, 38(6): 471-478.
6. Bendersky J, Auladell-Rispau A, Urrútia G, et al. Methods for developing and reporting living evidence synthesis. J Clin Epidemiol, 2022, 152: 89-100.
7. Zhang K, Yang X, Wang Y, et al. Artificial intelligence in drug development. Nat Med, 2025, 31(1): 45-59.
8. Zhang Y, Khan SA, Mahmud A, et al. Exploring the role of large language models in the scientific method: from hypothesis to discovery. NPJ Artific Intell, 2025, 1(1): 14.
9. 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.
10. Khondker A, Kwong JCC, Rickard M, et al. AI-PEDURO - artificial intelligence in pediatric urology: protocol for a living scoping review and online repository. J Pediatr Urol, 2025, 21(2): 532-538.
11. R?st TB, Slaughter L, Nytr? ?, et al. Using neural networks to support high-quality evidence mapping. BMC Bioinformatics, 2021, 22(Suppl 11): 496.
12. Hirt J, Adlbrecht L, Maurer C, et al. Exploring experiences of times without care and encounters in dementia: protocol for a living and adaptive evidence map. BMJ Open, 2023, 13(9): e075664.
13. Zhao J, Bai W, Zhang Q, et al. Evidence-based practice implementation in healthcare in China: a living scoping review. Lancet Reg Health West Pac, 2022, 20: 100355.
14. Créquit P, Martin-Montoya T, Attiche N, et al. Living network meta-analysis was feasible when considering the pace of evidence generation. J Clin Epidemiol, 2019, 108: 10-16.
15. Negrini S, Ceravolo MG, C?té P, et al. A systematic review that is “rapid” and “living”: a specific answer to the COVID-19 pandemic. J Clin Epidemiol, 2021, 138: 194-198.
16. Ciapponi A, Bardach A, Berrueta M, et al. A global living systematic review and meta-analysis hub of emerging vaccines in pregnancy and childhood. Reprod Health, 2025, 22(1): 129.
17. Pussegoda K, Corrin T, Baumeister A, et al. Methods for conducting a living evidence profile on mpox: an evidence map of the literature. Cochrane Evid Synth Methods, 2024, 2(2): e12044.
18. Hendriks LEL, Cortiula F, Martins-Branco D, et al. Updated treatment recommendations for systemic treatment: from the ESMO oncogene-addicted metastatic NSCLC living guideline. Ann Oncol, 2025, 36(10): 1227-1231.
19. Merlin T, Street J, Carter D, et al. Challenges in the evaluation of emerging highly specialised technologies: is there a role for living HTA. Appl Health Econ Health Policy, 2023, 21(6): 823-830.
20. Cuker A, Tseng EK, Nieuwlaat R, et al. American Society of Hematology 2021 guidelines on the use of anticoagulation for thromboprophylaxis in patients with COVID-19. Blood Adv, 2021, 5(3): 872-888.
21. Gartlehner G, Kugley S, Crotty K, et al. Artificial intelligence-assisted data extraction with a large language model: a study within reviews. Ann Intern Med, 2025, 178(12): 1763-1771.
22. Forero DA, Abreu SE, Tovar BE, et al. Automated analyses of risk of bias and critical appraisal of systematic reviews (ROBIS and AMSTAR 2): a comparison of the performance of 4 large language models. J Am Med Inform Assoc, 2025, 32(9): 1471-1476.
23. Van De Schoot R, De Bruin J, Schram R, et al. An open source machine learning framework for efficient and transparent systematic reviews. Nat Mach Intell, 2021, 3(2): 125-133.
24. Liu L, Blake V, Barman M, et al. Using natural language processing to extract information from clinical text in electronic medical records for populating clinical registries: a systematic review. J Am Med Inform Assoc, 2025: ocaf176.
25. Wieland-Jorna Y, van Kooten D, Verheij RA, et al. Natural language processing systems for extracting information from electronic health records about activities of daily living. A systematic review. JAMIA Open, 2024, 7(2): ooae044.
26. Gliozzo J, Mesiti M, Notaro M, et al. Heterogeneous data integration methods for patient similarity networks. Brief Bioinform, 2022, 23(4): bbac207.
27. Marques-Cruz M, Pinto F, Vieira RJ, et al. Use of artificial intelligence to support the assessment of the methodological quality of systematic reviews. J Clin Epidemiol, 2025, 187: 111944.
28. Lieberum JL, Toews 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.
29. Cui H, Yasseri T. AI-enhanced collective intelligence. Patterns (N Y), 2024, 5(11): 101074.
30. Kong X, Fang H, Chen W, et al. Examining human-AI collaboration in hybrid intelligence learning environments: insight from the Synergy Degree Model. Humanit Soc Sci Commun, 2025, 12(1): 821.
31. Boyer A, Farzaneh F, Pitrone A. Leveraging human-machine collaborative mutual learning to optimize performance. 2023.
32. Sivarajkumar S, Kelley M, Samolyk-Mazzanti A, et al. An empirical evaluation of prompting strategies for large language models in zero-shot clinical natural language processing: algorithm development and validation study. JMIR Med Inform, 2024, 12: e55318.
33. Li R, Yang Y, Wu S, et al. Using artificial intelligence to improve medical services in China. Ann Transl Med, 2020, 8(11): 711.
34. Zhong J, Zhang J, Fang H, et al. Advancing the development of real-world data for healthcare research in China: challenges and opportunities. BMJ Open, 2022, 12(7): e063139.
35. Zhou E, Shen Q, Hou Y. Integrating artificial intelligence into the modernization of traditional Chinese medicine industry: a review. Front Pharmacol, 2024, 15: 1181183.
36. 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.
37. 毛渤淳, 陳圣愷, 謝雨, 等. 經典深度學習算法對中文隨機對照試驗智能判別應用. 中國循證醫學雜志, 2019, 19(11): 1262-1267.

Chinese Journal of Evidence-Based Medicine

The new wave of evidence revolution: paradigm evolution, challenges, and future of living evidence (LE) synthesis

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