Chinese Journal of Dermatology ›› 2026, e20240061.doi: 10.35541/cjd.20240061
• Reviews • Next Articles
Wang Juncheng, Liu Jie
Received:2024-01-30
Revised:2025-10-02
Online:2026-02-09
Published:2026-03-06
Contact:
Liu Jie
E-mail:Liujie04672@pumch.cn
Supported by:Wang Juncheng, Liu Jie. Artificial intelligence in psoriasis: diagnosis, treatment and patient management [J]. Chinese Journal of Dermatology,2026,e20240061. doi:10.35541/cjd.20240061
| [1] | Griffiths C, Armstrong AW, Gudjonsson JE, et al. Psoriasis[J]. Lancet, 2021,397(10281):1301⁃1315. DOI: 10.1016/S0140⁃6736(20)32549⁃6. |
| [2] | Omiye JA, Gui H, Daneshjou R, et al. Principles, applications, and future of artificial intelligence in dermatology[J]. Front Med (Lausanne), 2023,10:1278232. DOI: 10.3389/fmed.2023.1278232. |
| [3] | Biswas S, Achar U, Hakim B, et al. Artificial intelligence in dermatology: a systematized review[J]. Int J Dermatol Venereol, 2025, 8(1):33⁃39. DOI: 10.1097/JD9.0000000000000404. |
| [4] | 高萌, 杨仙鸿, 姜祎群. 人工智能在医学领域的研究进展[J]. 中华皮肤科杂志, 2019,52(2):131⁃134. |
| [5] | 刘念, 陈宏翔. 人工智能在皮肤科领域的应用与发展[J]. 中华皮肤科杂志, 2019,52(1):63⁃66. DOI: 10.3760/cma.j.issn.0412⁃4030.2019.01.021. |
| [6] | Shrivastava VK, Londhe ND, Sonawane RS, et al. Computer⁃aided diagnosis of psoriasis skin images with HOS, texture and color features: a first comparative study of its kind[J]. Comput Methods Programs Biomed, 2016,126:98⁃109. DOI: 10.1016/j.cmpb.2015.11.013. |
| [7] | Wu H, Yin H, Chen H, et al. A deep learning, image based approach for automated diagnosis for inflammatory skin diseases[J]. Ann Transl Med, 2020,8(9):581. DOI: 10.21037/atm.2020. 04.39. |
| [8] | Hsiao YP, Chiu CW, Lu CW, et al. Identification of skin lesions by using single⁃step multiframe detector[J]. J Clin Med, 2021,10(1):144. DOI: 10.3390/jcm10010144. |
| [9] | Zhao S, Xie B, Li Y, et al. Smart identification of psoriasis by images using convolutional neural networks: a case study in China[J]. J Eur Acad Dermatol Venereol, 2020,34(3):518⁃524. DOI: 10.1111/jdv.15965. |
| [10] | Yang Y, Wang J, Xie F, et al. A convolutional neural network trained with dermoscopic images of psoriasis performed on par with 230 dermatologists[J]. Comput Biol Med, 2021,139:104924. DOI: 10.1016/j.compbiomed.2021.104924. |
| [11] | Czajkowska J, Badura P, Korzekwa S, et al. Deep learning⁃based high⁃frequency ultrasound skin image classification with multicriteria model evaluation[J]. Sensors (Basel), 2021:5846. DOI: 10.3390/s21175846. |
| [12] | Wan L, Chen J, Wu H, et al. Deep learning for inflammatory diseases classification based on reflectance confocal microscopy[J]. J Am Acad Dermatol, 2023, 88(5):e283⁃e284. DOI: 10.1016/j.jaad.2022.09.043. |
| [13] | Zhu CY, Wang YK, Chen HP, et al. A deep learning based framework for diagnosing multiple skin diseases in a clinical environment[J]. Front Med (Lausanne), 2021,8:626369. DOI: 10.3389/fmed.2021.626369. |
| [14] | Pal A, Chaturvedi A, Chandra A, et al. MICaps: multi⁃instance capsule network for machine inspection of Munro's microabscess[J]. Comput Biol Med, 2022,140:105071. DOI: 10.1016/j.compbiomed.2021.105071. |
| [15] | Aijaz SF, Khan SJ, Azim F, et al. Deep learning application for effective classification of different types of psoriasis[J]. J Healthc Eng, 2022,2022:7541583. DOI: 10.1155/2022/7541583. |
| [16] | Yu Z, Kaizhi S, Jianwen H, et al. A deep learning⁃based approach toward differentiating scalp psoriasis and seborrheic dermatitis from dermoscopic images[J]. Front Med (Lausanne), 2022,9:965423. DOI: 10.3389/fmed.2022.965423. |
| [17] | Zhu X, Zheng B, Cai W, et al. Deep learning⁃based diagnosis models for onychomycosis in dermoscopy[J]. Mycoses, 2022,65(4):466⁃472. DOI: 10.1111/myc.13427. |
| [18] | Lin GS, Lai KT, Syu JM, et al. Instance segmentation based on deep convolutional neural networks and transfer learning for unconstrained psoriasis skin images[J]. Applied Sciences, 2021, 11(7):3155. DOI: 10.3390/app11073155. |
| [19] | Mohan S, Kasthuri N. Automatic segmentation of psoriasis skin images using adaptive chimp optimization algorithm⁃based CNN[J]. J Digit Imaging, 2023,36(3):1123⁃1136. DOI: 10.1007/s10278⁃022⁃00765⁃x. |
| [20] | Czajkowska J, Badura P, Korzekwa S, et al. Automated segmentation of epidermis in high⁃frequency ultrasound of pathological skin using a cascade of DeepLab v3+ networks and fuzzy connectedness[J]. Comput Med Imaging Graph, 2022,95:102023. DOI: 10.1016/j.compmedimag.2021.102023. |
| [21] | Pal A, Garain U, Chandra A, et al. Psoriasis skin biopsy image segmentation using deep convolutional neural network[J]. Comput Methods Programs Biomed, 2018,159:59⁃69. DOI: 10. 1016/j.cmpb.2018.01.027. |
| [22] | Ahmad Fadzil MH, Prakasa E, Asirvadam VS, et al. 3D surface roughness measurement for scaliness scoring of psoriasis lesions[J]. Comput Biol Med, 2013,43(11):1987⁃2000. DOI: 10.1016/j.compbiomed.2013.08.009. |
| [23] | George Y, Aldeen M, Garnavi R. Automatic scale severity assessment method in psoriasis skin images using local descriptors[J]. IEEE J Biomed Health Inform, 2020,24(2):577⁃585. DOI: 10.1109/JBHI.2019.2910883. |
| [24] | Schaap MJ, Cardozo NJ, Patel A, et al. Image⁃based automated psoriasis area severity index scoring by convolutional neural networks[J]. J Eur Acad Dermatol Venereol, 2022,36(1):68⁃75. DOI: 10.1111/jdv.17711. |
| [25] | Huang K, Wu X, Li Y, et al. Artificial intelligence⁃based psoriasis severity assessment: real⁃world study and application[J]. J Med Internet Res, 2023,25:e44932. DOI: 10.2196/44932. |
| [26] | Okamoto T, Kawai M, Ogawa Y, et al. Artificial intelligence for the automated single⁃shot assessment of psoriasis severity[J]. J Eur Acad Dermatol Venereol, 2022,36(12):2512⁃2515. DOI: 10. 1111/jdv.18354. |
| [27] | Yoo KH, Jeong GJ, Park JH, et al. Estimation error of the body surface area in psoriasis: a comparative study of physician and computer⁃assisted image analysis (ImageJ)[J]. Clin Exp Dermatol, 2022,47(7):1298⁃1306. DOI: 10.1111/ced.15148. |
| [28] | Lee WH, Lee S, Kim J, et al. Measurement of psoriasis⁃affected area with artificial neural network[J]. J Am Acad Dermatol, 2023,88(3):731⁃732. DOI: 10.1016/j.jaad.2022.09.035. |
| [29] | 杭州咏柳科技有限公司.一种基于图像的银屑病严重程度的评估系统: 202210756235.8[P].2022⁃10⁃25. |
| [30] | Paik K, Kim BR, Youn SW. Evaluation of the area subscore of the palmoplantar pustulosis area and severity index using an attention U⁃net deep learning algorithm[J]. J Dermatol, 2023,50(6):787⁃792. DOI: 10.1111/1346⁃8138.16752. |
| [31] | Yang Y, Wu C, Zhang X, et al. Development and validation of an artificial intelligence⁃driven model for accurate classification of erythrodermic psoriasis severity: erythrodermic psoriasis integrated classification system (EPICS)[J]. Am J Clin Dermatol, 2025,26(6):1017⁃1029. DOI: 10.1007/s40257⁃025⁃00980⁃6. |
| [32] | Tomalin LE, Kim J, Correa da Rosa J, et al. Early quantification of systemic inflammatory proteins predicts long⁃term treatment response to tofacitinib and etanercept[J]. J Invest Dermatol, 2020,140(5):1026⁃1034. DOI: 10.1016/j.jid.2019.09.023. |
| [33] | Damiani G, Conic R, Pigatto P, et al. Predicting secukinumab fast⁃responder profile in psoriatic patients: advanced application of artificial⁃neural⁃networks (ANNs)[J]. J Drugs Dermatol, 2020,19(12):1241⁃1246. DOI: 10.36849/JDD.2020.5006. |
| [34] | Venerito V, Lopalco G, Abbruzzese A, et al. A machine learning approach to predict remission in patients with psoriatic arthritis on treatment with secukinumab[J]. Front Immunol, 2022,13:917939. DOI: 10.3389/fimmu.2022.917939. |
| [35] | Emam S, Du AX, Surmanowicz P, et al. Predicting the long⁃term outcomes of biologics in patients with psoriasis using machine learning[J]. Br J Dermatol, 2020,182(5):1305⁃1307. DOI: 10. 1111/bjd.18741. |
| [36] | Lam Hoai XL, Simonart T. Comparing meta⁃analyses with ChatGPT in the evaluation of the effectiveness and tolerance of systemic therapies in moderate⁃to⁃severe plaque psoriasis[J]. J Clin Med, 2023,12(16):5410. DOI: 10.3390/jcm12165410. |
| [37] | Rafay A, Hussain W. EfficientSkinDis: an EfficientNet⁃based classification model for a large manually curated dataset of 31 skin diseases[J]. Biomed Signal Process Control, 2023,85:104869. DOI: 10.1016/j.bspc.2023.104869. |
| [38] | Pangti R, Mathur J, Chouhan V, et al. A machine learning⁃based, decision support, mobile phone application for diagnosis of common dermatological diseases[J]. J Eur Acad Dermatol Venereol, 2021,35(2):536⁃545. DOI: 10.1111/jdv.16967. |
| [39] | Garzorz⁃Stark N, Beicht S, Baghin V, et al. IMPROVE 1.0: individual monitoring of psoriasis activity by regular online app questionnaires and outpatient visits[J]. Front Med (Lausanne), 2021,8:648233. DOI: 10.3389/fmed.2021.648233. |
| [40] | Patrick MT, Raja K, Miller K, et al. Drug repurposing prediction for immune⁃mediated cutaneous diseases using a word⁃embedding⁃based machine learning approach[J]. J Invest Dermatol, 2019,139(3):683⁃691. DOI: 10.1016/j.jid.2018.09.018. |
| [41] | Zhan YP, Chen BS. Drug target identification and drug repurposing in psoriasis through systems biology approach, DNN⁃based DTI model and genome⁃wide microarray data[J]. Int J Mol Sci, 2023,24(12):10033. DOI: 10.3390/ijms241210033. |
| [42] | Patrick MT, Stuart PE, Raja K, et al. Genetic signature to provide robust risk assessment of psoriatic arthritis development in psoriasis patients[J]. Nat Commun, 2018,9(1):4178. DOI: 10.1038/s41467⁃018⁃06672⁃6. |
| [43] | Lee LT, Yang HC, Nguyen PA, et al. Machine learning approaches for predicting psoriatic arthritis risk using electronic medical records: population⁃based study[J]. J Med Internet Res, 2023,25:e39972. DOI: 10.2196/39972. |
| [44] | Munger E, Choi H, Dey AK, et al. Application of machine learning to determine top predictors of noncalcified coronary burden in psoriasis: an observational cohort study[J]. J Am Acad Dermatol, 2020,83(6):1647⁃1653. DOI: 10.1016/j.jaad. 2019.10.060. |
| [45] | Navarini L, Sperti M, Currado D, et al. A machine⁃learning approach to cardiovascular risk prediction in psoriatic arthritis[J]. Rheumatology (Oxford), 2020,59(7):1767⁃1769. DOI: 10. 1093/rheumatology/kez677. |
| [1] | Wang Li, Wang Qianqiu, Zhang Ruili. Research progress in latent syphilis [J]. Chinese Journal of Dermatology, 2026, 59(3): 286-289. |
| [2] | Yang Wanqing, Chen Nuoran, Meng Xiaoqi, Liu Tingwei, Xu Yang. Application of focused ultrasound in dermatology beyond the anti?aging aspects [J]. Chinese Journal of Dermatology, 2026, 0(3): 20240433-e20240433. |
| [3] | Lei Liangxinwen, Wu Hao, Lu Zhong. Application of optical coherence tomography in cosmetic dermatology [J]. Chinese Journal of Dermatology, 2026, 59(3): 273-277. |
| [4] | Zeng Wanxin, Wen Xiang. Photoelectric therapy for hypertrophic scars [J]. Chinese Journal of Dermatology, 2026, 59(3): 269-273. |
| [5] | Xia Zhikuan, Ge Ge. Laser therapy for vitiligo: clinical applications and prospects [J]. Chinese Journal of Dermatology, 2026, 59(3): 213-217. |
| [6] | Committee on Psoriasis, Chinese Society of Dermatology. Expert consensus on management of psoriasis with comorbidities (2026) [J]. Chinese Journal of Dermatology, 2026, 59(3): 193-207. |
| [7] | Expert Group on the Interpretation of the "Chinese expert consensus on the clinical application of photobiomodulation therapy in dermatology ()", Laser Medicine Group, Chinese Society of Dermatology. Interpretation of the Chinese expert consensus on the clinical application of photobiomodulation therapy in dermatology (2025) [J]. Chinese Journal of Dermatology, 2026, 59(3): 208-212. |
| [8] | Liu Lihao, Hu Yu, Chen Kun. Clinical application and development of photobiomodulation in dermatology [J]. Chinese Journal of Dermatology, 2026, 59(3): 278-282. |
| [9] | Liu Jia, Gu Yinghua, He Xiaoning, . Treatment and disease burden of hereditary angioedema [J]. Chinese Journal of Dermatology, 2026, 59(3): 282-286. |
| [10] | Yan Kexin, Zhang Wei, Song Hao, Xu Xiulian. Application of nanomedicine in the diagnosis and treatment of melanoma [J]. Chinese Journal of Dermatology, 2026, 59(2): 189-192. |
| [11] | Chinese Society of Dermatology, China Dermatologist Association. Guidelines on the diagnosis and treatment of dermatitis and eczematous diseases in China (2026 edition) [J]. Chinese Journal of Dermatology, 2026, 59(2): 97-106. |
| [12] | Environmental and Occupational Skin Disease Research Group, Chinese Dermatovenerology Society of Integrative Medicine, Committee on Dermatology, Chinese Association of Geriatric Research, Committee on Drugs for Dermatology, China Association of Traditional Chinese Medicine, Dermatology Branch of China Association for Promotion of Health Science and Technology. Expert consensus on rational application of moisturizers for atopic dermatitis (2026 edition) [J]. Chinese Journal of Dermatology, 2026, 59(2): 107-112. |
| [13] | Chen Hao. Clinical histological characteristics of several novel cutaneous soft tissue neoplasms and their subtypes [J]. Chinese Journal of Dermatology, 2026, 59(2): 120-126. |
| [14] | Chinese Society of Dermatology, China Dermatologist Association, Dermatology Branch of China International Exchange and Promotive Association for Medical and Health Care, Rare Skin Diseases Committee, China Alliance for Rare Diseases. Expert consensus on spesolimab in the treatment of inflammatory skin diseases (2026 edition) [J]. Chinese Journal of Dermatology, 2026, 59(2): 113-119. |
| [15] | Li Yanhong, Jang Kun, Wu Zhuoxuan, Zhang Jianlan, Fang Meizhen, Liu Shuting, Wang Linlin, Hu Lei, Zhou Xiaoyong, Chen Liuqing, Chen Jinbo. Analysis of 18 cases of cutaneous adverse reactions associated with programmed death-1 inhibitors [J]. Chinese Journal of Dermatology, 2026, 0(2): 20240002-e20240002. |
|
||