Performance comparison of ChatGPT-4.5 and DeepSeek-V3 in rehabilitation guidance for knee osteoarthritis_West China Medical Journal

Authors：

ZHOU Lijun ^1,2,3 , LIAN Hao ⁴ ,  LIU Sijia ^1,2

1. Rehabilitation Medicine Center and Institute of Rehabilitation Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P. R. China;
2. Key Laboratory of Rehabilitation Medicine in Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P. R. China;
3. West China School of Medicine, Sichuan University, Chengdu, Sichuan 610041, P. R. China;
4. West China School of Public Health, Sichuan University, Chengdu, Sichuan 610041, P. R. China;

Corresponding author：

LIU Sijia, Email: 137595879@qq.com

Keywords：

Large language models; knee osteoarthritis; physical therapy; artificial intelligence

DOI：

10.7507/1002-0179.202504066

Video：

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Objective To compare the performance of ChatGPT-4.5 and DeepSeek-V3 across five key domains of physical therapy for knee osteoarthritis (KOA), evaluating the accuracy, completeness, reliability, and readability of their responses and exploring their clinical application potential. Methods Twenty-one core questions were extracted from 10 authoritative KOA rehabilitation guidelines published between September 2011 and January 2024, covering five task categories: rehabilitation assessment, physical agent modalities, exercise therapy, assistive device use, and patient education. Responses were generated using both the ChatGPT-4.5 and DeepSeek-V3 models and evaluated by four physical therapists with over five years of clinical experience using Likert scales (accuracy and completeness: 5 points; reliability: 7 points). The scale scores were compared between the two large language models. Additional assessment included language style clustering. Results Most of the scale scores did not follow a normal distribution, and were presented as median (lower quartile, upper quartile). ChatGPT-4.5 outperformed DeepSeek-V3 with higher scores in accuracy [4.75 (4.75, 4.75) vs. 4.75 (4.50, 5.00), P=0.018], completeness [4.75 (4.50, 5.00) vs. 4.25 (4.00, 4.50), P=0.006], and reliability [5.75 (5.50, 6.00) vs. 5.50 (5.50, 5.50), P=0.015]. Clustering analysis of language styles revealed that ChatGPT-4.5 demonstrated a more diverse linguistic style, whereas DeepSeek-V3 responses were more standardized. ChatGPT-4.5 achieved higher scores than DeepSeek-V3 in lexical richness [4.792 (4.720, 4.912) vs. 4.564 (4.409, 4.653), P<0.001], but lower than DeepSeek-V3 in syntactic richness [2.133 (2.072, 2.154) vs. 2.187 (2.154, 2.206), P=0.003]. Conclusions ChatGPT-4.5 demonstrates superior performance in accuracy, completeness, and reliability, indicating a stronger capacity for task execution. It uses more diverse words and has stronger flexibility in language generation. DeepSeek-V3 exhibited greater syntactic richness and is more normative in language. ChatGPT-4.5 is better suited for content-rich tasks that require detailed explanation, while DeepSeek-V3 is more appropriate for standardized question-answering applications.

Citation： ZHOU Lijun, LIAN Hao, LIU Sijia. Performance comparison of ChatGPT-4.5 and DeepSeek-V3 in rehabilitation guidance for knee osteoarthritis. West China Medical Journal, 2025, 40(6): 870-875. doi: 10.7507/1002-0179.202504066 Copy

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19.	何东仪, 程鹏. 老年骨关节炎慢病管理指南. 中西医结合研究, 2023, 15(6): 374-387.
20.	雷蕾, 韦瑶瑜, 刘康龙. AlphaReadabilityChinese: 汉语文本可读性工具开发与应用. 外语与外语教学, 2024(1): 83-93, 149.
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22.	Chen X, Wang L, You M, et al. Evaluating and enhancing large language models’ performance in domain-specific medicine: development and usability study with DocOA. J Med Internet Res, 2024, 26: e58158.
23.	Marcaccini G, Seth I, Xie Y, et al. Breaking bones, breaking barriers: ChatGPT, DeepSeek, and Gemini in hand fracture management. J Clin Med, 2025, 14(6): 1983.
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25.	Abreu AA, Murimwa GZ, Farah E, et al. Enhancing readability of online patient-facing content: the role of AI chatbots in improving cancer information accessibility. J Natl Compr Canc Netw, 2024, 22(2D): e237334.
26.	Nasra M, Jaffri R, Pavlin-Premrl D, et al. Can artificial intelligence improve patient educational material readability? A systematic review and narrative synthesis. Intern Med J, 2025, 55(1): 20-34.
27.	Kaygisiz ÖF, Teke MT. Can DeepSeek and ChatGPT be used in the diagnosis of oral pathologies?. BMC Oral Health, 2025, 25(1): 638.
28.	Zhou M, Pan Y, Zhang Y, et al. Evaluating AI-generated patient education materials for spinal surgeries: comparative analysis of readability and DISCERN quality across ChatGPT and DeepSeek models. Int J Med Inform, 2025, 198: 105871.
29.	Piette JD, Newman S, Krein SL, et al. Patient-centered pain care using artificial intelligence and mobile health tools: a randomized comparative effectiveness trial. JAMA Intern Med, 2022, 182(9): 975-983.

1. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med, 2019, 25(1): 44-56.
2. Tekkeşin Aİ. Artificial intelligence in healthcare: past, present and future. Anatol J Cardiol, 2019, 22(Suppl 2): 8-9.
3. Kung TH, Cheatham M, Medenilla A, et al. Performance of ChatGPT on USMLE: potential for AI-assisted medical education using large language models. PLOS Digit Health, 2023, 2(2): e0000198.
4. Lanotte F, O’Brien MK, Jayaraman A. AI in rehabilitation medicine: opportunities and challenges. Ann Rehabil Med, 2023, 47(6): 444-458.
5. Hunter DJ, Bierma-Zeinstra S. Osteoarthritis. Lancet, 2019, 393(10182): 1745-1759.
6. Sharma L. Osteoarthritis of the knee. N Engl J Med, 2021, 384(1): 51-59.
7. Dobson F, Hinman RS, Roos EM, et al. OARSI recommended performance-based tests to assess physical function in people diagnosed with hip or knee osteoarthritis. Osteoarthritis Cartilage, 2013, 21(8): 1042-1052.
8. Tanaka R, Ozawa J, Kito N, et al. Effect of the frequency and duration of land-based therapeutic exercise on pain relief for people with knee osteoarthritis: a systematic review and meta-analysis of randomized controlled trials. J Phys Ther Sci, 2014, 26(7): 969-975.
9. Chen M, Decary M. Artificial intelligence in healthcare: an essential guide for health leaders. Healthc Manage Forum, 2020, 33(1): 10-18.
10. 中华医学会物理医学与康复学分会, 四川大学华西医院. 中国膝骨关节炎康复治疗指南(2023 版). 中国循证医学杂志, 2024, 24(1): 1-14.
11. Peter WF, Jansen MJ, Hurkmans EJ, et al. Physiotherapy in hip and knee osteoarthritis: development of a practice guideline concerning initial assessment, treatment and evaluation. Acta Reumatol Port, 2011, 36(3): 268-281.
12. Kolasinski SL, Neogi T, Hochberg MC, et al. 2019 American College of Rheumatology/Arthritis Foundation guideline for the management of osteoarthritis of the hand, hip, and knee. Arthritis Care Res (Hoboken), 2020, 72(2): 149-162.
13. Bannuru RR, Osani MC, Vaysbrot EE, et al. OARSI guidelines for the non-surgical management of knee, hip, and polyarticular osteoarthritis. Osteoarthritis Cartilage, 2019, 27(11): 1578-1589.
14. National Institute for Health and Care Excellence (NICE). Osteoarthritis in over 16s: diagnosis and management. London: National Institute for Health and Care Excellence (NICE), 2022.
15. 何成奇. 骨关节炎康复指南. 北京: 人民卫生出版社, 2016: 511.
16. 李沁懋. 膝骨关节炎患者的运动康复指南. 家庭医药·快乐养生, 2024(4): 11.
17. Dhawan A, Mather RC 3rd, Karas V, et al. An epidemiologic analysis of clinical practice guidelines for non-arthroplasty treatment of osteoarthritis of the knee. Arthroscopy, 2014, 30(1): 65-71.
18. Moseng T, Vliet Vlieland TPM, Battista S, et al. EULAR recommendations for the non-pharmacological core management of hip and knee osteoarthritis: 2023 update. Ann Rheum Dis, 2024, 83(6): 730-740.
19. 何东仪, 程鹏. 老年骨关节炎慢病管理指南. 中西医结合研究, 2023, 15(6): 374-387.
20. 雷蕾, 韦瑶瑜, 刘康龙. AlphaReadabilityChinese: 汉语文本可读性工具开发与应用. 外语与外语教学, 2024(1): 83-93, 149.
21. Menekşeoğlu AK, İş EE. Comparative performance of artificial intelligence models in physical medicine and rehabilitation board-level questions. Rev Assoc Med Bras (1992), 2024, 70(7): e20240241.
22. Chen X, Wang L, You M, et al. Evaluating and enhancing large language models’ performance in domain-specific medicine: development and usability study with DocOA. J Med Internet Res, 2024, 26: e58158.
23. Marcaccini G, Seth I, Xie Y, et al. Breaking bones, breaking barriers: ChatGPT, DeepSeek, and Gemini in hand fracture management. J Clin Med, 2025, 14(6): 1983.
24. Chen R, Zeng D, Li Y, et al. Evaluating the performance and clinical decision-making impact of ChatGPT-4 in reproductive medicine. Int J Gynaecol Obstet, 2025, 168(3): 1285-1291.
25. Abreu AA, Murimwa GZ, Farah E, et al. Enhancing readability of online patient-facing content: the role of AI chatbots in improving cancer information accessibility. J Natl Compr Canc Netw, 2024, 22(2D): e237334.
26. Nasra M, Jaffri R, Pavlin-Premrl D, et al. Can artificial intelligence improve patient educational material readability? A systematic review and narrative synthesis. Intern Med J, 2025, 55(1): 20-34.
27. Kaygisiz ÖF, Teke MT. Can DeepSeek and ChatGPT be used in the diagnosis of oral pathologies?. BMC Oral Health, 2025, 25(1): 638.
28. Zhou M, Pan Y, Zhang Y, et al. Evaluating AI-generated patient education materials for spinal surgeries: comparative analysis of readability and DISCERN quality across ChatGPT and DeepSeek models. Int J Med Inform, 2025, 198: 105871.
29. Piette JD, Newman S, Krein SL, et al. Patient-centered pain care using artificial intelligence and mobile health tools: a randomized comparative effectiveness trial. JAMA Intern Med, 2022, 182(9): 975-983.

West China Medical Journal

Performance comparison of ChatGPT-4.5 and DeepSeek-V3 in rehabilitation guidance for knee osteoarthritis

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