Turn off MathJax
Article Contents
Wang Xingang,Liu Huan.Opportunities and challenges for the discipline of burn and wound repair in the era of artificial intelligence[J].Chin J Burns Wounds,2026,42(11):1-6.DOI: 10.3760/cma.j.cn501225-20260809-00290.
Citation: Wang Xingang,Liu Huan.Opportunities and challenges for the discipline of burn and wound repair in the era of artificial intelligence[J].Chin J Burns Wounds,2026,42(11):1-6.DOI: 10.3760/cma.j.cn501225-20260809-00290.

Opportunities and challenges for the discipline of burn and wound repair in the era of artificial intelligence

doi: 10.3760/cma.j.cn501225-20260809-00290
Funds:

General Program of National Natural Science Foundation of China 82673274

Zhejiang Provincial Natural Science Foundation LHDMD25H150001

More Information
  • Burns and chronic refractory wounds represent a major global public-health challenge. China faces substantial clinical demand for wound repair, and the development of a specialized discipline system for wound repair is advancing steadily. With its capabilities in image recognition, data analysis, and predictive modeling, artificial intelligence (AI) is comprehensively reshaping the diagnosis-and-treatment system for burns and wound repair and bringing new opportunities for disciplinary development. This paper systematically reviews multiple application scenarios and the latest advances of AI in the field of burns and wound repair. It also rationally analyzes the potential risks and challenges associated with current AI applications, and offers an outlook on AI-driven transformation of modern medicine from conventional diagnostic-therapeutic paradigms toward individualized and precision-oriented care.

     

  • loading
  • [1]
    ThomsonIK, IversonKR, InnocentSHS, et al. Management of paediatric burns in low- and middle-income countries: assessing capacity using the World Health Organization Surgical Assessment Tool[J]. Int Health, 2020, 12(5):499-506. DOI: 10.1093/inthealth/ihz068.
    [2]
    ChengB, JiangY, FuX, et al. Epidemiological characteristics and clinical analyses of chronic cutaneous wounds of inpatients in China: prevention and control[J]. Wound Repair Regen, 2020, 28(5):623-630. DOI: 10.1111/wrr.12825.
    [3]
    付小兵.持续推进中国特色创面修复学科体系建设[J].中华烧伤与创面修复杂志,2023,39(1):5-8.DOI: 10.3760/cma.j.cn501225-20230106-00005.
    [4]
    AlowaisSA,AlghamdiSS,AlsuhebanyN,et al.Revolutionizing healthcare: the role of artificial intelligence in clinical practice[J].BMC Med Educ,2023,23(1):689.DOI: 10.1186/s12909-023-04698-z.
    [5]
    SinghK, PrabhuA, KaurN. The impact and role of artificial intelligence (AI) in healthcare: systematic review[J]. Curr Top Med Chem, 2025, 25(22):2635-2654. DOI: 10.2174/0115680266339394250225112747.
    [6]
    BhattachanP, RicciutiZ, KhalafF, et al. The role of artificial intelligence in burn assessment, complication diagnosis, and outcome prediction: a narrative review[J/OL]. Burns Trauma, 2025, 14:tkaf71[2026-08-09]. https://pubmed.ncbi.nlm.nih.gov/41624750/. DOI: 10.1093/burnst/tkaf071.
    [7]
    TaibBG, KarwathA, WensleyK, et al. Artificial intelligence in the management and treatment of burns: a systematic review and meta-analyses[J]. J Plast Reconstr Aesthet Surg, 2023, 77:133-161. DOI: 10.1016/j.bjps.2022.11.049.
    [8]
    BoissinC,LaflammeL,FransénJ,et al.Development and evaluation of deep learning algorithms for assessment of acute burns and the need for surgery[J].Sci Rep,2023,13(1):1794.DOI: 10.1038/s41598-023-28164-4.
    [9]
    ChoiJ, PatilA, VendrowE, et al. Practical computer vision application to compute total body surface area burn: reappraising a fundamental burn injury formula in the modern era[J]. JAMA Surg, 2022, 157(2):129-135. DOI: 10.1001/jamasurg.2021.5848.
    [10]
    YıldızM, SarpdağıY, OkuyarM, et al. Segmentation and classification of skin burn images with artificial intelligence: development of a mobile application[J]. Burns, 2024, 50(4):966-979. DOI: 10.1016/j.burns.2024.01.007.
    [11]
    WilsonRH,RowlandR,KennedyGT,et al.Review of machine learning for optical imaging of burn wound severity assessment[J].J Biomed Opt,2024,29(2):020901.DOI: 10.1117/1.JBO.29.2.020901.
    [12]
    CartottoR, JohnsonLS, SavetamalA, et al. American Burn Association clinical practice guidelines on burn shock resuscitation[J]. J Burn Care Res, 2024, 45(3):565-589. DOI: 10.1093/jbcr/irad125.
    [13]
    GuptaCB,BasuD,WilliamsTK,et al.Improving the precision of shock resuscitation by predicting fluid responsiveness with machine learning and arterial blood pressure waveform data[J].Sci Rep,2024,14(1):2227.DOI: 10.1038/s41598-023-50120-5.
    [14]
    BerkhoutWEM, van WijngaardenJJ, WorkumJD, et al. Operationalization of artificial intelligence applications in the intensive care unit: a systematic review[J]. JAMA Netw Open, 2025, 8(7):e2522866. DOI: 10.1001/jamanetworkopen.2025.22866.
    [15]
    AnS,YeZ,CheW,et al.Development and validation of machine learning models to predict in-hospital mortality in ICU patients with sepsis and chronic kidney disease[J].BMC Infect Dis,2025,25(1):1504.DOI: 10.1186/s12879-025-11949-5.
    [16]
    GaoJ,LuY,AshrafiN,et al.Prediction of sepsis mortality in ICU patients using machine learning methods[J].BMC Med Inform Decis Mak,2024,24(1):228.DOI: 10.1186/s12911-024-02630-z.
    [17]
    邱侃,李江虹,叶勇,等.基于机器学习的烧伤患者脓毒性休克预测模型建立与验证[J].医学研究与战创伤救治,2023,36(12):1299-1304.DOI: 10.16571/j.cnki.2097-2768.2023.12.012.
    [18]
    FrederickAB, GavrilovaY, KorenN, et al. A randomized feasibility study of virtual and face-to-face care using a novel mobile health solution for outpatient pediatric burns[J]. J Burn Care Res, 2025, 46(6):1392-1401. DOI: 10.1093/jbcr/iraf148.
    [19]
    MennellaC, ManiscalcoU, PietroG, et al. A deep learning system to monitor and assess rehabilitation exercises in home-based remote and unsupervised conditions[J]. Comput Biol Med, 2023, 166:107485. DOI: 10.1016/j.compbiomed.2023.107485.
    [20]
    EthierO, ChanHO, AbdolahnejadM, et al. Using computer vision and artificial intelligence to track the healing of severe burns[J]. J Burn Care Res, 2024, 45(3):700-708. DOI: 10.1093/jbcr/irad197.
    [21]
    Hayavi-HaghighiMH, AlipourJ. Applications, opportunities, and challenges in using telehealth for burn injury management: a systematic review[J]. Burns, 2023, 49(6):1237-1248. DOI: 10.1016/j.burns.2023.07.001.
    [22]
    LanX, TanZ, ZhouT, et al. Use of virtual reality in burn rehabilitation: a systematic review and meta-analysis[J]. Arch Phys Med Rehabil, 2023, 104(3):502-513. DOI: 10.1016/j.apmr.2022.08.005.
    [23]
    LiuH,SunW,CaiW,et al.Current status, challenges, and prospects of artificial intelligence applications in wound repair theranostics[J].Theranostics,2025,15(5):1662-1688.DOI: 10.7150/thno.105109.
    [24]
    MonroyB,SanchezK,ArguelloP,et al.Automated chronic wounds medical assessment and tracking framework based on deep learning[J].Comput Biol Med,2023,165:107335.DOI: 10.1016/j.compbiomed.2023.107335.
    [25]
    ChangDH,NguyenDK,NguyenTN,et al.Application of deep learning in wound size measurement using fingernail as the reference[J].BMC Med Inform Decis Mak,2024,24(1):390.DOI: 10.1186/s12911-024-02778-8.
    [26]
    ReifsD, Casanova-LozanoL, Reig-BolañoR, et al. Clinical validation of computer vision and artificial intelligence algorithms for wound measurement and tissue classification in wound care[J]. Inform Med Unlocked, 2023, 37:101185. DOI: 10.1016/j.imu.2023.101185.
    [27]
    BerezoM, BudmanJ, HayesD, et al. Predicting chronic wound healing time using machine learning[J]. Adv Wound Care (New Rochelle), 2022, 11(6):281-296. DOI: 10.1089/wound.2021.0073.
    [28]
    DallmannAC, SheridanM, MattkeS, et al. Prediction of healing trajectory of chronic wounds using a machine learning approach[J]. Adv Wound Care (New Rochelle), 2025, 14(12):645-654. DOI: 10.1089/wound.2024.0095.
    [29]
    NgoQC,OgrinR,KumarDK.Computerised prediction of healing for venous leg ulcers[J].Sci Rep,2022,12(1):17962.DOI: 10.1038/s41598-022-20835-y.
    [30]
    DabasM, SchwartzD, BeeckmanD, et al. Application of artificial intelligence methodologies to chronic wound care and management: a scoping review[J]. Adv Wound Care (New Rochelle), 2023, 12(4):205-240. DOI: 10.1089/wound.2021.0144.
    [31]
    SwerdlowM, LoJ, ArmstrongDG. Reliability of an AI-powered application across different mobile devices for assessment of chronic wounds[J]. Adv Wound Care (New Rochelle), 2024, 13(1):14-21. DOI: 10.1089/wound.2022.0095.
    [32]
    AlderdenJ, JohnnyJ, BrooksKR, et al. Explainable artificial intelligence for early prediction of pressure injury risk[J]. Am J Crit Care, 2024, 33(5):373-381. DOI: 10.4037/ajcc2024856.
    [33]
    PolancichS, HickmanC, DickT, et al. Charting and predicting risk: artificial intelligence/machine learning pilot model for hospital-acquired pressure injuries[J]. J Nurs Care Qual, 2026, 41(4):305-313. DOI: 10.1097/NCQ.0000000000000988.
    [34]
    WangX,MengL,ZhangJ,et al.Identification of angiogenesis-related genes in diabetic foot ulcer using machine learning algorithms[J].Heliyon,2023,9(12):e23003.DOI: 10.1016/j.heliyon.2023.e23003.
    [35]
    WardAC, DubeyP, BasnettP, et al. Toward a closed loop, integrated biocompatible biopolymer wound dressing patch for detection and prevention of chronic wound infections[J]. Front Bioeng Biotechnol, 2020, 8:1039. DOI: 10.3389/fbioe.2020.01039.
    [36]
    HsuSY,ChenLW,HuangRW,et al.Quantization of extraoral free flap monitoring for venous congestion with deep learning integrated iOS applications on smartphones: a diagnostic study[J].Int J Surg,2023,109(6):1584-1593.DOI: 10.1097/JS9.0000000000000391.
    [37]
    KimJ, LeeSM, KimDE, et al. Development of an automated free flap monitoring system based on artificial intelligence[J]. JAMA Netw Open, 2024, 7(7):e2424299. DOI: 10.1001/jamanetworkopen.2024.24299.
    [38]
    KhorsandiJ, MirharooniJ, KahenJ, et al. Artificial intelligence in burn and wound care: image analysis, prediction, and clinical integration[J]. J Burn Care Res, 2026, 47(5):1331-1338. DOI: 10.1093/jbcr/irag090.
    [39]
    DrabiakK,KyzerS,NemovV,et al.AI and machine learning ethics, law, diversity, and global impact[J].Br J Radiol,2023,96(1150):20220934.DOI: 10.1259/bjr.20220934.
    [40]
    MehrtabarS, MareyA, DesaiA, et al. Ethical considerations in patient privacy and data handling for AI in cardiovascular imaging and radiology[J]. J Imaging Inform Med, 2026, 39(3):2004-2025. DOI: 10.1007/s10278-025-01656-7.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(1)

    Article Metrics

    Article views (28) PDF downloads(9) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return