Current applications and prospects of artificial intelligence in burn diagnosis and treatment_Chinese Journal of Reparative and Reconstructive Surgery

Authors：

SUN Huihui ,  CUI Zhengjun

Department of Burn and Plastic Surgery, the First Affiliated Hospital of Zhengzhou University, Zhengzhou Henan, 450000, P. R. China;

Corresponding?author：

CUI Zhengjun, Email: 157856800@qq.com

Keywords：

Artificial intelligence; burn diagnosis and treatment; prognosis management; decision support

DOI：

10.7507/1002-1892.202601102

Video：

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Abstract Full text Figures/Tables Video References Cited by

Objective To summarize the current research status of artificial intelligence (AI) in burn diagnosis and treatment, and to anticipate its future development direction. Methods Research literature on the application of AI in burn diagnosis and treatment both domestically and internationally in recent years was reviewed. The progress of AI application in various stages of burn diagnosis and treatment was summarized, and the challenges in its clinical application were analyzed. Results AI has demonstrated its value in improving diagnosis and treatment efficiency and prediction accuracy in multiple aspects, including burn wound diagnosis and severity assessment, perioperative decision making and precise intervention, personalized rehabilitation, and prognosis prediction. However, it also faces challenges related to data, models, and clinical translation. Conclusion AI exhibits significant potential in burn diagnosis and treatment. In the future, it is necessary to address existing challenges such as the scarcity of data, privacy risks, insufficient interpretability, and clinical translation barriers, and promote its application in clinical practice.

Citation： SUN Huihui, CUI Zhengjun. Current applications and prospects of artificial intelligence in burn diagnosis and treatment. Chinese Journal of Reparative and Reconstructive Surgery, 2026, 40(7): 1157-1162. doi: 10.7507/1002-1892.202601102 Copy

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6.	Jaskille AD, Shupp JW, Jordan MH, et al. Critical review of burn depth assessment techniques: Part Ⅰ. Historical review. J Burn Care Res, 2009, 30(6): 937-947.
7.	Claes KEY, Hoeksema H, Vyncke T, et al. Evidence based burn depth assessment using laser-based technologies: where do we stand? J Burn Care Res, 2021, 42(3): 513-525.
8.	Nagrath M, Kumar Sahu A, Jangid N, et al. Enhanced skin burn assessment through transfer learning: a novel framework for human tissue analysis. J Med Eng Technol, 2023, 47(5): 288-297.
9.	Chauhan J, Goyal P. BPBSAM: Body part-specific burn severity assessment model. Burns, 2020, 46(6): 1407-1423.
10.	Tan P, Ravulapalli K, Lewis CJ. A systematic review of advances in the use of spectral imaging in burn depth assessment. Burns, 2025, 51(3): 107401. doi: 10.1016/j.burns.2025.107401.
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13.	Lee S, Rahul, Ye H, et al. Real-time burn classification using ultrasound imaging. Sci Rep, 2020, 10(1): 5829. doi: 10.1038/s41598-020-62674-9.
14.	Lee S, Lukan J, Boyko T, et al. A deep learning model for burn depth classification using ultrasound imaging. J Mech Behav Biomed Mater, 2022, 125: 104930. doi: 10.1016/j.jmbbm.2021.104930.
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18.	Chang CW, Ho CY, Lai F, et al. Application of multiple deep learning models for automatic burn wound assessment. Burns, 2023, 49(5): 1039-1051.
19.	Meevassana J, Sumonsriwarankun P, Suwajo P, et al. 3D PED BURN app: A precise and easy‐to‐use pediatric 3D burn surface area calculation tool. Health Sci Rep, 2022, 5(4): e694. doi: 10.1002/hsr2.694.
20.	Chang CW, Wang H, Lai F, et al. Comparison of 3D and 2D area measurement of acute burn wounds with LiDAR technique and deep learning model. Front Artif Intell, 2025, 8: 1510905. doi: 10.3389/frai.2025.1510905.
21.	Malkoff N, Cannata B, Wang S, et al. FireSync EMS: a novel mobile application for burn surface area calculation. J Burn Care Res, 2025, 46(1): 101-106.
22.	Zuo F, Su J, Li Y, et al. Development and validation of a machine learning-based model for predicting intraoperative blood loss during burn surgery. Surgery, 2025, 184: 109445. doi: 10.1016/j.surg.2025.109445.
23.	Rambhatla S, Huang S, Trinh L, et al. DL4Burn: Burn surgical candidacy prediction using multimodal deep learning. AMIA Annual Symposium Proceedings, 2022, 2021: 1039-1048.
24.	Perusseau-Lambert A, Akram M, Frew Q, et al. Comparison between multispectral imaging and laser Doppler imaging to predict burn wound requirements for surgery. Burns, 2025, 51(8): 107650. doi: 10.1016/j.burns.2025.107650.
25.	Shaw KM, Safavi KC. The role of artificial intelligence in anesthesia monitoring and surveillance. Anesthesiol Clin, 2025, 43(3): 577-585.
26.	Szrama J, Gradys A, Wo?niak A, et al. The hypotension prediction index in free flap transplant in head and neck surgery: protocol of a prospective randomized controlled trial. Life, 2025, 15(3): 400. doi: 10.3390/life15030400.
27.	Tripathi AN, Kumar S, Sharma VK, et al. Comparing toric intraocular lens alignment: intraoperative image-guided system versus manual marking in cataract surgery: a randomized clinical trial. Int Ophthalmol, 2025, 45: 228. doi: 10.1007/s10792-025-03601-7.
28.	Yu S, Dwight J, Siska RC, et al. Feasibility of intra-operative image guidance in burn excision surgery with multispectral imaging and deep learning. Burns, 2024, 50(1): 115-122.
29.	Wolbert TT, White AE, Han J, et al. The application of augmented reality technology in free flap reconstruction: a systematic review. Microsurgery, 2025, 45(5): e70080. doi: 10.1002/micr.70080.
30.	Feizi N, Tavakoli M, Patel RV, et al. Robotics and AI for teleoperation, tele-assessment, and tele-training for surgery in the era of COVID-19: Existing challenges, and future vision. Front Robot AI, 2021, 8: 610677. doi: 10.3389/frobt.2021.610677.
31.	Kim JW, Chen JT, Hansen P, et al. SRT-H: A hierarchical framework for autonomous surgery via language-conditioned imitation learning. Science robotics, 2025, 10(104): eadt5254. doi: 10.48550/arXiv.2505.10251.
32.	丁亞榮, 王毅, 王俊康, 等. 人工智能在燒傷外科臨床應用中的研究進展. 解放軍醫學院學報, 2025, 46(11): 1103-1109.
33.	Quellec G, Lamard M, Cochener B, et al. Real-time segmentation and recognition of surgical tasks in cataract surgery videos. IEEE transactions on medical imaging, 2014, 33(12): 2352-2360.
34.	Sivarajkumar S, Gao F, Denny P, et al. Mining clinical notes for physical rehabilitation exercise information: natural language processing algorithm development and validation study. JMIR medical informatics, 2024, 12(1): e52289. doi: 10.2196/52289.
35.	Jatesiktat P, Lim GM, Kuah CWK, et al. Autonomous modeling of repetitive movement for rehabilitation exercise monitoring. BMC Med Inform Decis Mak, 2022, 22(1): 175. doi: 10.1186/s12911-022-01907-5.
36.	Kim J, Lee SM, Kim DE, et al. Development of an automated free flap monitoring system based on artificial intelligence. JAMA Netw Open, 2024, 7(7): e2424299. doi: 10.1001/jamanetworkopen.2024.24299.
37.	Hsu SY, Chen LW, Huang RW, et al. Quantization of extraoral free flap monitoring for venous congestion with deep learning integrated iOS applications on smartphones: a diagnostic study. International Journal of Surgery, 2023, 109(6): 1584-1593.
38.	Barzegar Khanghah A, Fernie G, Roshan Fekr A. Design and validation of vision-based exercise biofeedback for tele-rehabilitation. Sensors, 2023, 23(3): 1206. doi: 10.3390/s23031206.
39.	Bulduk M, Can V, Akta? E, et al. Artificial intelligence-assisted virtual reality for reducing anxiety in pediatric endoscopy. J Clin Med, 2025, 14(4): 1344. doi: 10.3390/jcm14041344.
40.	Lan X, Tan Z, Zhou T, et al. Use of virtual reality in burn rehabilitation: a systematic review and meta-analysis. Arch Phys Med Rehabil, 2023, 104(3): 502-513.
41.	Le May S, Genest C, Francoeur M, et al. Virtual reality mobility for burn patients (VR-MOBILE): A within-subject-controlled trial protocol. Paediatr Neonatal Pain, 2022, 4(4): 192-198.
42.	Phelan I, Furness PJ, Matsangidou M, et al. Designing effective virtual reality environments for pain management in burn-injured patients. Virtual Real, 2023, 27(1): 201-215.
43.	E Moura FS, Amin K, Ekwobi C. Artificial intelligence in the management and treatment of burns: a systematic review. Burns Trauma, 2021, 9: tkab022. doi: 10.1093/burnst/tkab022.
44.	張文一, 郭九宮, 鄭金光, 等. 國內大語言模型在燒傷輔助診療多跳推理任務中的性能評估與比較. 解放軍醫學院學報, 2025, 46(10): 988-993.
45.	李方, 郭文培. AI醫療邁入規模化落地期 [N/OL]. 經濟日報, 2026-03-04 [2026-05-15]. http://paper.ce.cn/pc/attachment/202603/04/15f133d9-8757-4892-a6bb-a1c708b61166.pdf.
46.	Niculae A, Peride I, Tiglis M, et al. Burn-Induced acute kidney injury–two-lane road: from molecular to clinical aspects. Int J Mol Sci, 2022, 23(15): 8712. doi: 10.3390/ijms23158712.
47.	Rashidi HH, Makley A, Palmieri TL, et al. Enhancing military burn- and trauma-related acute kidney injury prediction through an automated machine learning platform and point-of-care testing. Arch Pathol Lab Med, 2021, 145(3): 320-326.
48.	Tedesco DJ, Hutter MF, Khalaf F, et al. Sepsis in burn care: incidence and outcomes. Mil Med Res, 2025, 12(1): 55. doi: 10.1186/s40779-025-00643-x.
49.	Luo W, Xiong L, Wang J, et al. Development and performance evaluation of a clinical prediction model for sepsis risk in burn patients. Medicine (Baltimore), 2024, 103(48): e40709. doi: 10.1097/MD.0000000000040709.
50.	Tran NK, Albahra S, Pham TN, et al. Novel application of an automated-machine learning development tool for predicting burn sepsis: proof of concept. Sci Rep, 2020, 10(1): 12354. doi: 10.1038/s41598-020-69433-w.
51.	Jeon K, Lee N, Jeong S, et al. Immature granulocyte percentage for prediction of sepsis in severe burn patients: a machine leaning-based approach. BMC Infect Dis, 2021, 21(1): 1258. doi: 10.1186/s12879-021-06971-2.
52.	Xi D, Yu H, Yu T. Development of a predictive model for the relationship between serum pan-immunoinflammatory index levels and scar formation in facial burn patients. Am J Transl Res, 2025, 17(3): 2197-2209.
53.	Esumi R, Funao H, Kawamoto E, et al. Machine learning-based prediction of delirium and risk factor identification in intensive care unit patients with burns: retrospective observational study. JMIR Form Res, 2025, 9: e65190. doi: 10.2196/65190.

1. Jeschke MG, van Baar ME, Choudhry MA, et al. Burn injury. Nat Rev Dis Primers, 2020, 6(1): 11. doi: 10.1038/s41572-020-0145-5.
2. 張家樂, 田璐, 伍國勝, 等. 淺析人工智能在海戰燒傷診療中的應用前景. 中華損傷與修復雜志(電子版), 2025, 20(5): 426-430.
3. Rahman MM, Masry MEL, Gnyawali SC, et al. A framework for advancing burn assessment with artificial intelligence. Mil Med, 2025, 190(Supplement_2): 387-393.
4. Mei XY, Lee HC, Diao KY, et al. Artificial intelligence-enabled rapid diagnosis of patients with COVID-19. Nat Med, 2020, 26(8): 1224-1228.
5. 孫磊, 汪安安, 宋一敏, 等. 大語言模型在臨床醫學領域的應用, 挑戰和展望. 解放軍醫學院學報, 2025, 46(1): 50-60.
6. Jaskille AD, Shupp JW, Jordan MH, et al. Critical review of burn depth assessment techniques: Part Ⅰ. Historical review. J Burn Care Res, 2009, 30(6): 937-947.
7. Claes KEY, Hoeksema H, Vyncke T, et al. Evidence based burn depth assessment using laser-based technologies: where do we stand? J Burn Care Res, 2021, 42(3): 513-525.
8. Nagrath M, Kumar Sahu A, Jangid N, et al. Enhanced skin burn assessment through transfer learning: a novel framework for human tissue analysis. J Med Eng Technol, 2023, 47(5): 288-297.
9. Chauhan J, Goyal P. BPBSAM: Body part-specific burn severity assessment model. Burns, 2020, 46(6): 1407-1423.
10. Tan P, Ravulapalli K, Lewis CJ. A systematic review of advances in the use of spectral imaging in burn depth assessment. Burns, 2025, 51(3): 107401. doi: 10.1016/j.burns.2025.107401.
11. Wang S, Gu M, Zhang M, et al. Research on a burn severity detection method based on hyperspectral imaging. Sensors, 2025, 25(5): 1330. doi: 10.3390/s25051330.
12. Tan P, Nyeko-Lacek M, Walsh K, et al. Artificial intelligence-enhanced multispectral imaging for burn wound assessment: insights from a multi-centre UK evaluation. Burns, 2025, 51(8): 107550. doi: 10.1016/j.burns.2025.107550.
13. Lee S, Rahul, Ye H, et al. Real-time burn classification using ultrasound imaging. Sci Rep, 2020, 10(1): 5829. doi: 10.1038/s41598-020-62674-9.
14. Lee S, Lukan J, Boyko T, et al. A deep learning model for burn depth classification using ultrasound imaging. J Mech Behav Biomed Mater, 2022, 125: 104930. doi: 10.1016/j.jmbbm.2021.104930.
15. Wu J, Ma Q, Zhou X, et al. Segmentation and quantitative analysis of optical coherence tomography (OCT) images of laser burned skin based on deep learning. Biomed Phys Eng Express, 2024, 10(4). doi: 10.1088/2057-1976/ad488f.
16. Carrougher GJ, Pham TN. Burn size estimation: A remarkable history with clinical practice implications. Burns Open, 2024, 8(2): 47-52.
17. Xu X, Bu Q, Xie J, et al. On-site burn severity assessment using smartphone-captured color burn wound images. Computers in Biology and Medicine, 2024, 182: 109171. doi: 10.1016/j.com-pbiomed.2024.109171.
18. Chang CW, Ho CY, Lai F, et al. Application of multiple deep learning models for automatic burn wound assessment. Burns, 2023, 49(5): 1039-1051.
19. Meevassana J, Sumonsriwarankun P, Suwajo P, et al. 3D PED BURN app: A precise and easy‐to‐use pediatric 3D burn surface area calculation tool. Health Sci Rep, 2022, 5(4): e694. doi: 10.1002/hsr2.694.
20. Chang CW, Wang H, Lai F, et al. Comparison of 3D and 2D area measurement of acute burn wounds with LiDAR technique and deep learning model. Front Artif Intell, 2025, 8: 1510905. doi: 10.3389/frai.2025.1510905.
21. Malkoff N, Cannata B, Wang S, et al. FireSync EMS: a novel mobile application for burn surface area calculation. J Burn Care Res, 2025, 46(1): 101-106.
22. Zuo F, Su J, Li Y, et al. Development and validation of a machine learning-based model for predicting intraoperative blood loss during burn surgery. Surgery, 2025, 184: 109445. doi: 10.1016/j.surg.2025.109445.
23. Rambhatla S, Huang S, Trinh L, et al. DL4Burn: Burn surgical candidacy prediction using multimodal deep learning. AMIA Annual Symposium Proceedings, 2022, 2021: 1039-1048.
24. Perusseau-Lambert A, Akram M, Frew Q, et al. Comparison between multispectral imaging and laser Doppler imaging to predict burn wound requirements for surgery. Burns, 2025, 51(8): 107650. doi: 10.1016/j.burns.2025.107650.
25. Shaw KM, Safavi KC. The role of artificial intelligence in anesthesia monitoring and surveillance. Anesthesiol Clin, 2025, 43(3): 577-585.
26. Szrama J, Gradys A, Wo?niak A, et al. The hypotension prediction index in free flap transplant in head and neck surgery: protocol of a prospective randomized controlled trial. Life, 2025, 15(3): 400. doi: 10.3390/life15030400.
27. Tripathi AN, Kumar S, Sharma VK, et al. Comparing toric intraocular lens alignment: intraoperative image-guided system versus manual marking in cataract surgery: a randomized clinical trial. Int Ophthalmol, 2025, 45: 228. doi: 10.1007/s10792-025-03601-7.
28. Yu S, Dwight J, Siska RC, et al. Feasibility of intra-operative image guidance in burn excision surgery with multispectral imaging and deep learning. Burns, 2024, 50(1): 115-122.
29. Wolbert TT, White AE, Han J, et al. The application of augmented reality technology in free flap reconstruction: a systematic review. Microsurgery, 2025, 45(5): e70080. doi: 10.1002/micr.70080.
30. Feizi N, Tavakoli M, Patel RV, et al. Robotics and AI for teleoperation, tele-assessment, and tele-training for surgery in the era of COVID-19: Existing challenges, and future vision. Front Robot AI, 2021, 8: 610677. doi: 10.3389/frobt.2021.610677.
31. Kim JW, Chen JT, Hansen P, et al. SRT-H: A hierarchical framework for autonomous surgery via language-conditioned imitation learning. Science robotics, 2025, 10(104): eadt5254. doi: 10.48550/arXiv.2505.10251.
32. 丁亞榮, 王毅, 王俊康, 等. 人工智能在燒傷外科臨床應用中的研究進展. 解放軍醫學院學報, 2025, 46(11): 1103-1109.
33. Quellec G, Lamard M, Cochener B, et al. Real-time segmentation and recognition of surgical tasks in cataract surgery videos. IEEE transactions on medical imaging, 2014, 33(12): 2352-2360.
34. Sivarajkumar S, Gao F, Denny P, et al. Mining clinical notes for physical rehabilitation exercise information: natural language processing algorithm development and validation study. JMIR medical informatics, 2024, 12(1): e52289. doi: 10.2196/52289.
35. Jatesiktat P, Lim GM, Kuah CWK, et al. Autonomous modeling of repetitive movement for rehabilitation exercise monitoring. BMC Med Inform Decis Mak, 2022, 22(1): 175. doi: 10.1186/s12911-022-01907-5.
36. Kim J, Lee SM, Kim DE, et al. Development of an automated free flap monitoring system based on artificial intelligence. JAMA Netw Open, 2024, 7(7): e2424299. doi: 10.1001/jamanetworkopen.2024.24299.
37. Hsu SY, Chen LW, Huang RW, et al. Quantization of extraoral free flap monitoring for venous congestion with deep learning integrated iOS applications on smartphones: a diagnostic study. International Journal of Surgery, 2023, 109(6): 1584-1593.
38. Barzegar Khanghah A, Fernie G, Roshan Fekr A. Design and validation of vision-based exercise biofeedback for tele-rehabilitation. Sensors, 2023, 23(3): 1206. doi: 10.3390/s23031206.
39. Bulduk M, Can V, Akta? E, et al. Artificial intelligence-assisted virtual reality for reducing anxiety in pediatric endoscopy. J Clin Med, 2025, 14(4): 1344. doi: 10.3390/jcm14041344.
40. Lan X, Tan Z, Zhou T, et al. Use of virtual reality in burn rehabilitation: a systematic review and meta-analysis. Arch Phys Med Rehabil, 2023, 104(3): 502-513.
41. Le May S, Genest C, Francoeur M, et al. Virtual reality mobility for burn patients (VR-MOBILE): A within-subject-controlled trial protocol. Paediatr Neonatal Pain, 2022, 4(4): 192-198.
42. Phelan I, Furness PJ, Matsangidou M, et al. Designing effective virtual reality environments for pain management in burn-injured patients. Virtual Real, 2023, 27(1): 201-215.
43. E Moura FS, Amin K, Ekwobi C. Artificial intelligence in the management and treatment of burns: a systematic review. Burns Trauma, 2021, 9: tkab022. doi: 10.1093/burnst/tkab022.
44. 張文一, 郭九宮, 鄭金光, 等. 國內大語言模型在燒傷輔助診療多跳推理任務中的性能評估與比較. 解放軍醫學院學報, 2025, 46(10): 988-993.
45. 李方, 郭文培. AI醫療邁入規模化落地期 [N/OL]. 經濟日報, 2026-03-04 [2026-05-15]. http://paper.ce.cn/pc/attachment/202603/04/15f133d9-8757-4892-a6bb-a1c708b61166.pdf.
46. Niculae A, Peride I, Tiglis M, et al. Burn-Induced acute kidney injury–two-lane road: from molecular to clinical aspects. Int J Mol Sci, 2022, 23(15): 8712. doi: 10.3390/ijms23158712.
47. Rashidi HH, Makley A, Palmieri TL, et al. Enhancing military burn- and trauma-related acute kidney injury prediction through an automated machine learning platform and point-of-care testing. Arch Pathol Lab Med, 2021, 145(3): 320-326.
48. Tedesco DJ, Hutter MF, Khalaf F, et al. Sepsis in burn care: incidence and outcomes. Mil Med Res, 2025, 12(1): 55. doi: 10.1186/s40779-025-00643-x.
49. Luo W, Xiong L, Wang J, et al. Development and performance evaluation of a clinical prediction model for sepsis risk in burn patients. Medicine (Baltimore), 2024, 103(48): e40709. doi: 10.1097/MD.0000000000040709.
50. Tran NK, Albahra S, Pham TN, et al. Novel application of an automated-machine learning development tool for predicting burn sepsis: proof of concept. Sci Rep, 2020, 10(1): 12354. doi: 10.1038/s41598-020-69433-w.
51. Jeon K, Lee N, Jeong S, et al. Immature granulocyte percentage for prediction of sepsis in severe burn patients: a machine leaning-based approach. BMC Infect Dis, 2021, 21(1): 1258. doi: 10.1186/s12879-021-06971-2.
52. Xi D, Yu H, Yu T. Development of a predictive model for the relationship between serum pan-immunoinflammatory index levels and scar formation in facial burn patients. Am J Transl Res, 2025, 17(3): 2197-2209.
53. Esumi R, Funao H, Kawamoto E, et al. Machine learning-based prediction of delirium and risk factor identification in intensive care unit patients with burns: retrospective observational study. JMIR Form Res, 2025, 9: e65190. doi: 10.2196/65190.

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