Analysis of the application of artificial intelligence technology in health technology assessment and future prospects_Chinese Journal of Evidence-Based Medicine

Authors：

ZHANG Xiaolu ¹ , CHEN Lin ² , GAO Xin ¹ , WANG Peimeng ¹ , WANG Yaoling ¹ , XIAO Feiyi ¹ , LI Rui ¹ ,  LI Xue ¹ ,  GUO Wudong ¹

1. China National Health Development Research Center, Beijing 100044, P. R. China;
2. School of Public Health, Dalian Medical University, Dalian 116044, P. R. China;

Corresponding?author：

LI Xue, Email: lx0204@126.com; GUO Wudong, Email: guowudong@hotmail.com

Keywords：

Artificial intelligence (AI); Health technology assessment (HTA); Application scenarios; Risks and challenges; Principles and positions

DOI：

10.7507/1672-2531.202511003

Video：

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With the rapid global development of artificial intelligence (AI) technology, its applications in various industries continue to deepen. Against this backdrop, AI empowering high-quality development in the pharmaceutical and healthcare sector has become a global research hotspot, and AI-enabled health technology assessment (HTA) constitutes a key component of AI applications in the pharmaceutical field. Currently, the World Health Organization (WHO), International Society for Pharmacoeconomics and Outcomes Research (ISPOR), National Institute for Health and Care Excellence (NICE), and Canada’s Drug Agency (CDA-AMC) are all actively exploring the application pathways, technical challenges, developmental limitations, and solutions of AI in HTA. This paper aims to conduct a systematic analysis of AI-related basic concepts and classifications, typical application scenarios of AI in HTA, risk challenges, and position principles, based on the exploratory findings of the aforementioned entities regarding AI applications in HTA and integrating the technical characteristics of AI and HTA themselves. It further seeks to propose the application pathways, directions, and future prospects of AI in China’s HTA field by drawing on international beneficial experiences.

Citation： ZHANG Xiaolu, CHEN Lin, GAO Xin, WANG Peimeng, WANG Yaoling, XIAO Feiyi, LI Rui, LI Xue, GUO Wudong. Analysis of the application of artificial intelligence technology in health technology assessment and future prospects. Chinese Journal of Evidence-Based Medicine, 2026, 26(5): 580-588. doi: 10.7507/1672-2531.202511003 Copy

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37.	Juwara L, El-Hussuna A, El Emam K. An evaluation of synthetic data augmentation for mitigating covariate bias in health data. Patterns (NY), 2024, 5(4): 100946.
38.	Drukker K, Chen W, Gichoya J, et al. Toward fairness in artificial intelligence for medical image analysis: identification and mitigation of potential biases in the roadmap from data collection to model deployment. J Med Imaging (Bellingham), 2023, 10(6): 061104.
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40.	Br?nnvall R, Forsgren H, Linge H. HEIDA: software examples for rapid introduction of homomorphic encryption for privacy preservation of health data. 2023.

1. Bhavnani SP, Narula J, Sengupta PP. Mobile technology and the digitization of healthcare. Eur Heart J, 2016, 37(18): 1428-1438.
2. NICE. NICE statement of intent for artificial intelligence (AI). 2025.
3. World Health Organization. Regulatory considerations on artificial intelligence for health. 2023.
4. CDA-AMC. New position statement aims to guide the use of AI methods in health technology assessment. 2025.
5. Fleurence RL, Bian J, Wang X, et al. Generative artificial intelligence for health technology assessment: opportunities, challenges, and policy considerations: an ISPOR Working Group Report. Value Health, 2025, 28(2): 175-183.
6. WINS. An introduction to artificial intelligence and data science. 2025.
7. Dajjar G. The Parliamentary Office of Science and Technology artificial intelligence (AI) glossary. 2024.
8. Zhang L, Le B, Akhtar N, et al. Large language models for computer-aided design: a survey. 2025.
9. Tian J, Yu X, Wang Y, et al. Relayout: integrating relation reasoning for content-aware layout generation with multi-modal large language models. 2025.
10. Martin K. AI language models are transforming the medical writing space–like it or not! Medical Writing, 2023, 32(3): 22-27.
11. Cochrane. Artificial intelligence (AI) technologies in Cochrane. 2025.
12. Qureshi R, Shaughnessy D, Gill KAR, et al. Are ChatGPT and large language models "the answer" to bringing us closer to systematic review automation. Syst Rev, 2023, 12(1): 72.
13. Jin Q, Leaman R, Lu Z. Retrieve, summarize, and verify: how will ChatGPT affect information seeking from the medical literature. J Am Soc Nephrol, 2023, 34(8): 1302-1304.
14. ISPOR. Revolutionizing systematic reviews: harnessing the power of AI. 2025.
15. Khraisha Q, Put S, Kappenberg J, et al. Can large language models replace humans in systematic reviews. Evaluating GPT-4's efficacy in screening and extracting data from peer-reviewed and grey literature in multiple languages. Res Synth Methods, 2024, 15(4): 616-626.
16. Guo E, Gupta M, Deng J, et al. Automated paper screening for clinical reviews using large language models: data analysis study. J Med Internet Res, 2024, 26: e48996.
17. Robinson A, Thorne W, Wu BP, et al. Bio-sieve: exploring instruction tuning large language models for systematic review automation. 2023.
18. Hasan B, Saadi S, Rajjoub NS, et al. Integrating large language models in systematic reviews: a framework and case study using ROBINS-I for risk of bias assessment. BMJ Evid Based Med, 2024, 29(6): 394-398.
19. Reason T, Benbow E, Langham J, et al. Artificial intelligence to automate network meta-analyses: four case studies to evaluate the potential application of large language models. Pharmacoecon Open, 2024, 8(2): 205-220.
20. Marcisz-Grzanka K, K?osowska D, Harhala M, et al. Eligibility criteria in phase 3 randomized controlled trials in gastric cancer. Gastric Cancer, 2025, 28(6): 1232-1240.
21. Thall PF, Zang Y, Chapple AG, et al. Novel clinical trial designs with dose optimization to improve long-term outcomes. Clin Cancer Res, 2023, 29(22): 4549-4554.
22. Datta S, Lee K, Paek H, et al. AutoCriteria: a generalizable clinical trial eligibility criteria extraction system powered by large language models. J Am Med Inform Assoc, 2024, 31(2): 375-385.
23. Harrer S, Shah P, Antony B, et al. Artificial intelligence for clinical trial design. Trends Pharmacol Sci, 2019, 40(8): 577-591.
24. van Amsterdam WAC, Verhoeff JJC, Harlianto NI, et al. Individual treatment effect estimation in the presence of unobserved confounding using proxies: a cohort study in stage III non-small cell lung cancer. Sci Rep, 2022, 12(1): 5848.
25. 趙建中, 王水強. 老年人用藥的臨床試驗需關注的幾個問題. 中國臨床藥理學雜志, 2010, 26(5): 382-385.
26. 周志新, 陳曉陽, 楊同衛, 等. 藥物臨床試驗中使用安慰劑對照的倫理原則與沖突分析. 醫學與哲學(人文社會醫學版), 2011, 32(10): 23-25.
27. Ling AY, Montez-Rath ME, Carita P, et al. An overview of current methods for real-world applications to generalize or transport clinical trial findings to target populations of interest. Epidemiology, 2023, 34(5): 627-636.
28. Piciocchi A, Cipriani M, Messina M, et al. Unlocking the potential of synthetic patients for accelerating clinical trials: results of the first GIMEMA experience on acute myeloid leukemia patients. EJHaem, 2024, 5(2): 353-359.
29. Velupillai S, Suominen H, Liakata M, et al. Using clinical natural language processing for health outcomes research: overview and actionable suggestions for future advances. J Biomed Inform, 2018, 88: 11-19.
30. Saleh ME, Wazery YM, Ali AA. A systematic literature review of deep learning-based text summarization: techniques, input representation, training strategies, mechanisms, datasets, evaluation, and challenges. Expert Syst Appl, 2024, 252: 124153.
31. Elvidge J, Hawksworth C, Av?ar TS, et al. Consolidated health economic evaluation reporting standards for interventions that use artificial intelligence (CHEERS-AI). Value Health, 2024, 27(9): 1196-1205.
32. Padula WV, Kreif N, Vanness DJ, et al. Machine learning methods in health economics and outcomes research-the PALISADE checklist: a good practices report of an ISPOR Task Force. Value Health, 2022, 25(7): 1063-1080.
33. Elliott A. Better, broader, safer: using health data for research and analysis (the Goldacre review). J Radiol Prot, 2022, 42(3): 030101.
34. Shivade C, Raghavan P, Fosler-Lussier E, et al. A review of approaches to identifying patient phenotype cohorts using electronic health records. J Am Med Inform Assoc, 2014, 21(2): 221-230.
35. Li H, Moon JT, Purkayastha S, et al. Ethics of large language models in medicine and medical research. Lancet Digit Health, 2023, 5(6): e333-e335.
36. Yang Y, Lin M, Zhao H, et al. A survey of recent methods for addressing AI fairness and bias in biomedicine. J Biomed Inform, 2024, 154: 104646.
37. Juwara L, El-Hussuna A, El Emam K. An evaluation of synthetic data augmentation for mitigating covariate bias in health data. Patterns (NY), 2024, 5(4): 100946.
38. Drukker K, Chen W, Gichoya J, et al. Toward fairness in artificial intelligence for medical image analysis: identification and mitigation of potential biases in the roadmap from data collection to model deployment. J Med Imaging (Bellingham), 2023, 10(6): 061104.
39. Mosquera L, El Emam K, Ding L, et al. A method for generating synthetic longitudinal health data. BMC Med Res Methodol, 2023, 23(1): 67.
40. Br?nnvall R, Forsgren H, Linge H. HEIDA: software examples for rapid introduction of homomorphic encryption for privacy preservation of health data. 2023.

Chinese Journal of Evidence-Based Medicine

Analysis of the application of artificial intelligence technology in health technology assessment and future prospects

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