Systematic Review of Deep Learning Image Analyses for the Diagnosis and Monitoring of Skin Disease
Open accessAbstract
Skin diseases affect one-third of the global population, posing a major healthcare burden. Deep learning may optimise healthcare workflows through processing skin images via neural networks to make predictions. A focus of deep learning research is skin lesion triage to detect cancer, but this may not translate to the wider scope of >2000 other skin diseases. We searched for studies applying deep learning to skin images, excluding benign/malignant lesions (1/1/2000-23/6/2022, PROSPERO CRD42022309935). The primary outcome was accuracy of deep learning algorithms in disease diagnosis or severity assessment. We modified QUADAS-2 for quality assessment. Of 13,857 references identified, 64 were included. The most studied diseases were acne, psoriasis, eczema, rosacea, vitiligo, urticaria. Deep learning algorithms had high specificity and variable sensitivity in diagnosing these conditions. Accuracy of algorithms in diagnosing acne (median 94%, IQR 86-98; n = 11), rosacea (94%, 90-97; n = 4), eczema (93%, 90-99; n = 9) and psoriasis (89%, 78-92; n = 8) was high. Accuracy for grading severity was highest for psoriasis (range 93-100%, n = 2), eczema (88%, n = 1), and acne (67-86%, n = 4). However, 59 (92%) studies had high risk-of-bias judgements and 62 (97%) had high-level applicability concerns. Only 12 (19%) reported participant ethnicity/skin type. Twenty-four (37.5%) evaluated the algorithm in an independent dataset, clinical setting or prospectively. These data indicate potential of deep learning image analysis in diagnosing and monitoring common skin diseases. Current research has important methodological/reporting limitations. Real-world, prospectively-acquired image datasets with external validation/testing will advance deep learning beyond the current experimental phase towards clinically-useful tools to mitigate rising health and cost impacts of skin disease.
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Key technical hurdles include standardizing image quality across photo acquisition methods, addressing confounding factors and artifacts in images, and developing representative datasets that span diverse patient populations.
“Key technical hurdles include standardizing image quality across photo acquisition methods, addressing confounding factors and artifacts in images, and developing representative datasets that span diverse patient populations (Choy et al, 2023; Daneshjou et al, 2022)”
From Artificial intelligence–enabled precision medicine for inflammatory skin diseases (2026) · cites this paper as Choy et al, 2023
The Discussion calls for high-quality images from well-phenotyped cohorts and describes underrepresentation of some skin types and bias toward particular phenotypes and locations. That supports the need for more representative datasets. The passages do not identify standardizing image quality across acquisition methods or addressing image artifacts and confounders as specific hurdles. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe accuracy of deep learning achieves optimal outcomes in skin disease diagnosis.
“The accuracy of deep learning is confirmed to achieve optimal outcomes in skin disease diagnosis [48]”
From Artificial Intelligence in the Histopathological Assessment of Non-Neoplastic Skin Disorders: A Narrative Review with Future Perspectives (2025) · cites this paper as [48]
The Results report high diagnostic accuracy for several commonly studied skin diseases, supporting the claim's core point. But the Discussion describes the findings as encouraging rather than optimal, notes low accuracy for externally tested algorithms diagnosing skin infections, and highlights variable sensitivity and methodological limitations. The claim overstates how consistently well deep learning performs across skin diseases. (LLM, conf=0.88)
Experimental — accuracy not yet measuredThe finding that acne, psoriasis and atopic dermatitis were the most studied diseases was consistent with previous findings.
“which was consistent with previous findings.14”
From Assessing the performance of artificial intelligence models in evaluating inflammatory skin disease severity: a systematic review and meta-analysis (2025) · cites this paper as [14]
Table 1 (Results, General study characteristics; page unavailable) lists acne (30 studies), psoriasis (27), and eczema (22) as the three most studied disease categories, supporting the overall comparison. It labels the third category eczema, not specifically atopic dermatitis, so the narrower identification is not established by our copy. (LLM, conf=0.84)
Experimental — accuracy not yet measuredA recent review included 64 studies, of which 52 focused on detection and 12 on severity assessment.
“with a recent review including 64 studies, of which 52 were focused on detection and 12 concentrated on severity assessment.14”
From Assessing the performance of artificial intelligence models in evaluating inflammatory skin disease severity: a systematic review and meta-analysis (2025) · cites this paper as [14]
An earlier review by Choy et al. noted the highest accuracy in psoriasis (93-100%, n=2) and the lowest in acne (67-86%, n=4), but lacked a meta-analysis for direct comparisons.
“An earlier review of deep learning image analyses by Choy et al. noted the highest accuracy in psoriasis (93–100%, n = 2) and the lowest in acne (67–86%, n = 4), but lacked a meta-analysis for direct comparisons.14”
From Assessing the performance of artificial intelligence models in evaluating inflammatory skin disease severity: a systematic review and meta-analysis (2025) · cites this paper as [14]
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
PRISMA-P Group, Moher, D., Shamseer, L., Clarke, M., Ghersi, D., Liberati, A., Petticrew, M., Shekelle, P., & Stewart, L. A. (2015) · Systematic Reviews · doi:10.1186/2046-4053-4-1 · Cited by 26,223 · cited 1× · see citing works
“This systematic review is reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines 33 .”
MethodRayyan-a web and mobile app for systematic reviews.
Ouzzani, M., Hammady, H., Fedorowicz, Z., & Elmagarmid, A. (2016) · Systematic Reviews · doi:10.1186/s13643-016-0384-4 · Cited by 26,092 · see citing works
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362 · see citing works
A guide to deep learning in healthcare
Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., & Dean, J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0316-z · Cited by 5,312 · see citing works
Synthesis without meta-analysis (SWiM) in systematic reviews: reporting guideline
Campbell, M., McKenzie, J. E., Sowden, A., Katikireddi, S. V., Brennan, S. E., Ellis, S., Hartmann-Boyce, J., Ryan, R., Shepperd, S., Thomas, J., et al. (2020) · BMJ · doi:10.1136/bmj.l6890 · Cited by 4,628 · see citing works
Utilization of the PICO framework to improve searching PubMed for clinical questions
Schardt, C., Adams, M. B., Owens, T., Keitz, S., & Fontelo, P. (2007) · BMC Medical Informatics and Decision Making · doi:10.1186/1472-6947-7-16 · Cited by 3,053 · see citing works
PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies
Wolff, R. F., Moons, K. G. M., Riley, R. D., Whiting, P. F., Westwood, M., Collins, G. S., Reitsma, J. B., Kleijnen, J., Mallett, S., & for the PROBAST Group† (2019) · Annals of Internal Medicine · doi:10.7326/m18-1376 · Cited by 2,984 · see citing works
Liu, X., Faes, L., Kale, A. U., Wagner, S. K., Fu, D. J., Bruynseels, A., Mahendiran, T., Moraes, G., Shamdas, M., Kern, C., et al. (2019) · The Lancet Digital Health · doi:10.1016/s2589-7500(19)30123-2 · Cited by 1,883 · see citing works
Hay, R. J., Johns, N. E., Williams, H. C., Bolliger, I. W., Dellavalle, R. P., Margolis, D. J., Marks, R., Naldi, L., Weinstock, M. A., Wulf, S. K., et al. (2014) · Journal of Investigative Dermatology · doi:10.1038/jid.2013.446 · Cited by 1,706 · see citing works
Liu, X., Cruz Rivera, S., Moher, D., Calvert, M. J., Denniston, A. K., The SPIRIT-AI and CONSORT-AI Working Group, SPIRIT-AI and CONSORT-AI Steering Group, Chan, A.-W., Darzi, A., Holmes, C., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-1034-x · Cited by 1,421 · see citing works
Nagendran, M., Chen, Y., Lovejoy, C. A., Gordon, A. C., Komorowski, M., Harvey, H., Topol, E. J., Ioannidis, J. P. A., Collins, G. S., & Maruthappu, M. (2020) · BMJ · doi:10.1136/bmj.m689 · Cited by 1,152 · see citing works
Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis
Aggarwal, R., Sounderajah, V., Martin, G., Ting, D. S. W., Karthikesalingam, A., King, D., Ashrafian, H., & Darzi, A. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00438-z · Cited by 946 · see citing works
Collins, G. S., Dhiman, P., Andaur Navarro, C. L., Ma, J., Hooft, L., Reitsma, J. B., Logullo, P., Beam, A. L., Peng, L., Van Calster, B., et al. (2021) · BMJ Open · doi:10.1136/bmjopen-2020-048008 · Cited by 852 · see citing works
Larrazabal, A. J., Nieto, N., Peterson, V., Milone, D. H., & Ferrante, E. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1919012117 · Cited by 685 · see citing works
Bellemo, V., Lim, Z. W., Lim, G., Nguyen, Q. D., Xie, Y., Yip, M. Y. T., Hamzah, H., Ho, J., Lee, X. Q., Hsu, W., et al. (2019) · The Lancet Digital Health · doi:10.1016/s2589-7500(19)30004-4 · Cited by 419 · see citing works
Daneshjou, R., Smith, M. P., Sun, M. D., Rotemberg, V., & Zou, J. (2021) · JAMA Dermatology · doi:10.1001/jamadermatol.2021.3129 · Cited by 403 · see citing works
What is AI? Applications of artificial intelligence to dermatology
Du‐Harpur, X., Watt, F. M., Luscombe, N. M., & Lynch, M. D. (2020) · British Journal of Dermatology · doi:10.1111/bjd.18880 · Cited by 355 · see citing works
Self-citationSounderajah, V., Ashrafian, H., Rose, S., Shah, N. H., Ghassemi, M., Golub, R., Kahn, C. E., Esteva, A., Karthikesalingam, A., Mateen, B. A., et al. (2021) · Nature Medicine · doi:10.1038/s41591-021-01517-0 · Cited by 277 · see citing works
Jones, O. T., Matin, R. N., van der Schaar, M., Prathivadi Bhayankaram, K., Ranmuthu, C. K. I., Islam, M. S., Behiyat, D., Boscott, R., Calanzani, N., Emery, J., et al. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(22)00023-1 · Cited by 272 · see citing works
Characteristics of publicly available skin cancer image datasets: a systematic review
Wen, D., Khan, S. M., Ji Xu, A., Ibrahim, H., Smith, L., Caballero, J., Zepeda, L., de Blas Perez, C., Denniston, A. K., Liu, X., et al. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(21)00252-1 · Cited by 243 · see citing works
Checklist for Evaluation of Image-Based Artificial Intelligence Reports in Dermatology
Daneshjou, R., Barata, C., Betz‐Stablein, B., Celebi, M. E., Codella, N., Combalia, M., Guitera, P., Gutman, D. A., Halpern, A. C., Helba, B., et al. (2021) · JAMA Dermatology · doi:10.1001/jamadermatol.2021.4915 · Cited by 163 · see citing works
Unmet medical needs in chronic, non-communicable inflammatory skin diseases
Ujiie, H., Rosmarin, D., Schön, M. P., Ständer, S., Boch, K., Metz, M., Maurer, M., Thaci, D., Schmidt, E., Cole, C., et al. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.875492 · Cited by 158 · see citing works
di Ruffano, L. F., Takwoingi, Y., Dinnes, J., Chuchu, N., Bayliss, S. E., Davenport, C. F., Matin, R., Godfrey, K., O'Sullivan, C., Gulati, A., et al. (2018) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd013186 · Cited by 143 · see citing works
Deep learning in dermatology: a systematic review of current approaches, outcomes, and limitations
Jeong, H. K., Park, C., Henao, R., & Kheterpal, M. (2023) · JID Innovations · doi:10.1016/j.xjidi.2022.100150 · Cited by 134 · see citing works
Accuracy of Computer-Aided Diagnosis of Melanoma: A Meta-analysis
Dick, V., Sinz, C., Mittlböck, M., Kittler, H., & Tschandl, P. (2019) · JAMA Dermatology · doi:10.1001/jamadermatol.2019.1375 · Cited by 125 · see citing works
Kim, H., Lee, K. H., Han, K., Lee, J. W., Kim, J. Y., Im, D. J., Hong, Y. J., Choi, B. W., & Hur, J. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2022.53820 · Cited by 14 · see citing works
Evaluation of diagnosis diversity in artificial intelligence datasets: a scoping review
Chen, M. L., Rotemberg, V. M., Lester, J., Novoa, R. A., Chiou, A. S., & Daneshjou, R. (2022) · British Journal of Dermatology · doi:10.1093/bjd/ljac047 · Cited by 8 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [27]Page M. J. H. J. & Sterne J. A. C. Chapter 13: Assessing risk of bias due to missing results in a synthesis. in Cochrane Handbook for Systematic Reviews of Interventions version 63 (updated February 2022) Ch. 13 (Cochrane, 2022).
Cited by
Sorted by citations
Large language models for disease diagnosis: a scoping review
Zhou, S., Xu, Z., Zhang, M., Xu, C., Guo, Y., Zhan, Z., Fang, Y., Ding, S., Wang, J., Xu, K., et al. (2025) · npj Artificial Intelligence · doi:10.1038/s44387-025-00011-z · Cited by 143 · see citing works
Smart Dressings and Their Applications in Chronic Wound Management
Li, X., Xue, X., & Xie, P. (2024) · Cell Biochemistry and Biophysics · doi:10.1007/s12013-024-01402-w · Cited by 46 · see citing works
Unraveling the Impact of Class Imbalance on Deep-Learning Models for Medical Image Classification
Hellín, C. J., Olmedo, A., Valledor, A., Gómez, J., López‐Benítez, M., & Tayebi, A. (2024) · Applied Sciences · doi:10.3390/app14083419 · Cited by 41 · see citing works
Deep learning models across the range of skin disease
Venkatesh, K. P., Raza, M. M., Nickel, G., Wang, S., & Kvedar, J. C. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01033-8 · Cited by 36 · see citing works
Shapiro, J., & Lyakhovitsky, A. (2024) · Clinics in Dermatology · doi:10.1016/j.clindermatol.2024.06.020 · Cited by 26 · see citing works
Leal, J. F. D. C., Barroso, D. H., Trindade, N. S., de Miranda, V. L., & Gurgel‐Gonçalves, R. (2023) · Biomedicines · doi:10.3390/biomedicines12010012 · Cited by 24 · see citing works
Deep learning approaches to surgical video segmentation and object detection: A scoping review
Kamtam, D. N., Shrager, J. B., Malla, S. D., Lin, N., Cardona, J. J., Kim, J., & Hu, C. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110482 · Cited by 23 · see citing works
Deep Ensemble Learning for Multiclass Skin Lesion Classification
Chiu, T.-M., Chi, I.-C., Li, Y., & Tseng, M. (2025) · Bioengineering · doi:10.3390/bioengineering12090934 · Cited by 22 · see citing works
Ramessur, R., Dand, N., Langan, S., Saklatvala, J. R., Fritzsche, M.-C., Holland, S., Arents, B. W. M., McAteer, H., Proctor, A., McMahon, D., et al. (2024) · British Journal of Dermatology · doi:10.1093/bjd/ljae080 · Cited by 22 · see citing works
Self-citationSundar, S., Pandey, K. M., Mondal, D., Madhukar, M., Topno, R. K., Kumar, A., Kumar, V., Verma, D. K., Chakravarty, J., Chaubey, R., et al. (2024) · PLoS neglected tropical diseases · doi:10.1371/journal.pntd.0012242 · Cited by 21 · see citing works
Khalaf, A. D., Hamdan, H., Halin, A. B. A., & Manshor, N. B. (2025) · IEEE Access · doi:10.1109/access.2025.3569170 · Cited by 21 · see citing works
Talimi, H., Retmi, K., Fissoune, R., & Lemrani, M. (2024) · Diagnostics · doi:10.3390/diagnostics14090963 · Cited by 21 · see citing works
Artificial Intelligence in the Assessment and Grading of Acne Vulgaris: A Systematic Review
Traini, D. O., Palmisano, G., Guerriero, C., & Peris, K. (2025) · Journal of Personalized Medicine · doi:10.3390/jpm15060238 · Cited by 20 · see citing works
Bioengineered nanomaterials for dynamic diagnostics in vivo
Wu, J., Zhou, X., Tsang, C. Y., Mei, Q., & Zhang, Y. (2025) · Chemical Society Reviews · doi:10.1039/d5cs00136f · Cited by 18 · see citing works
Tao, Y., Xiong, M., Peng, Y., Yao, L., Zhu, H., Zhou, Q., & Ouyang, J. (2025) · Gene · doi:10.1016/j.gene.2024.149015 · Cited by 16 · see citing works
LKA: Large Kernel Adapter for Enhanced Medical Image Classification
Zhu, Z., Lu, S., Huang, T., Liu, L., & Liu, Z. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04978-0_38 · Cited by 15 · see citing works
Enhancing Nanomaterial-Based Optical Spectroscopic Detection of Cancer through Machine Learning
Sahli, C., & Kenry, K. (2024) · ACS Materials Letters · doi:10.1021/acsmaterialslett.4c01267 · Cited by 15 · see citing works
Remote Assessment of Eczema Severity via AI-powered Skin Image Analytics: A Systematic Review
Huang, L., Tang, W. H., Attar, R., Gore, C., Williams, H. C., Čustović, A., & Tanaka, R. J. (2024) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2024.102968 · Cited by 14 · see citing works
Ghorbian, M., Ghobaei‐Arani, M., & Ghorbian, . (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-11031-7 · Cited by 14 · see citing works
A survey of recent advances in analysis of skin images
Gupta, P., Nirmal, J. H., & Mehendale, N. D. (2024) · Evolutionary Intelligence · doi:10.1007/s12065-024-00977-w · Cited by 13 · see citing works
External Validation of AI Models for Skin Diseases: A Systematic Review
Fernandes, T. R. S., Teles, A. S., Fernandes, J. R. N., Lima, L. D. B., Sousa, D. L., Soares, R. D. C., & Teixeira, S. S. (2025) · IEEE Access · doi:10.1109/access.2025.3584904 · Cited by 12 · see citing works
Deep learning in dermatopathology: applications for skin disease diagnosis and classification
Fatima, S., Akram, M. U., Mohammad, S., & Ahmed, S. B. (2025) · Discover Applied Sciences · doi:10.1007/s42452-025-07138-3 · Cited by 12 · see citing works
Transforming Skin Quality Evaluation With AI : From Subjective Grading to Data‐Driven Precision
Pooth, R., Sattler, S., Westerberg, F., Pavicic, T., & Kerscher, M. J. (2025) · Journal of Cosmetic Dermatology · doi:10.1111/jocd.70371 · Cited by 11 · see citing works
Droppelmann, G., Rodríguez, C., Smague, D., Jorquera, C., & Feijoo, F. (2024) · EFORT Open Reviews · doi:10.1530/eor-24-0016 · Cited by 10 · see citing works
HArmonyCa™ hybrid filler to restore connective tissue: An Italian real‐life retrospective study
Proietti, I., Spagnoli, A., Favaroni, A., Gritti, A., Canton, M. D., Quartucci, S., Sciuto, C., Bertossi, D., Patalano, M., Cavallini, M., et al. (2024) · Journal of Cosmetic Dermatology · doi:10.1111/jocd.16503 · Cited by 9 · see citing works
Kamp, E., Ascott, A., & George, S. M. (2025) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2025.02.001 · Cited by 9 · see citing works
Artificial intelligence in autoimmune bullous dermatoses
Manuelyan, K., Dragolov, M., Drenovska, K., Shahid, M., & Vassileva, S. (2024) · Clinics in Dermatology · doi:10.1016/j.clindermatol.2024.06.008 · Cited by 9 · see citing works
A Bibliometric Review of Deep Learning Approaches in Skin Cancer Research
Supriyanto, C., Salam, A., Zeniarja, J., Utomo, D. W., Dewi, I. N., Paramita, C., Wijaya, A., & Safar, N. Z. M. (2025) · Computation · doi:10.3390/computation13030078 · Cited by 9 · see citing works
Cai, Z. R., Kim, J., Rezaei, S. J., Chen, M. L., Touma, F., Zhu, C., Onyeka, S., Fonjungo, F., Omiye, J. A., Krakowski, I., et al. (2025) · British Journal of Dermatology · doi:10.1093/bjd/ljaf250 · Cited by 8 · see citing works
“14 Notably, a meta-analysis evaluating the performance of image-based AI models for skin disease severity assessment has not been reported.”
Khalid, U., Chen, L., Khan, A. A., Chen, B., Mehmood, F., & Yasir, M. (2025) · Discover Computing · doi:10.1007/s10791-025-09503-7 · Cited by 8 · see citing works
Marode, T. P., Bhangdiya, V. K., Nemane, S. G., Tulaskar, D. P., Sarad, V. M., Sankar, K., Chopade, S., Avthankar, A., Bhaiyya, M. L., & Kulkarni, M. B. (2026) · Bioengineering · doi:10.3390/bioengineering13010075 · Cited by 7 · see citing works
Mawgoud, R. A., & Posch, C. (2024) · JEADV Clinical Practice · doi:10.1002/jvc2.580 · Cited by 7 · see citing works
Artificial intelligence–enabled precision medicine for inflammatory skin diseases
Tang, A. S., Wei, M. L., Haemel, A., La, C., Sirota, M., & Lee, E. Y. (2026) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2025.10.596 · Cited by 6 · see citing works
Della Mura, M., Sorino, J., Colagrande, A., Daruish, M., Ingravallo, G., Massaro, A., Cazzato, G., Lupo, C., Casatta, N., Ribatti, D., et al. (2025) · Medical Sciences · doi:10.3390/medsci13020070 · Cited by 6 · see citing works
“The accuracy of deep learning is confirmed to achieve optimal outcomes in skin disease diagnosis [48], but a lot of work on AI analysis in relation to histopathologic data remains investigational.”
Multimodal Time-Frequency Pseudo Anomalies for Atrial Fibrillation Anomaly Detection
Fan, H., Han, H., Chang, H., & Wang, Z. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3547531 · Cited by 6 · see citing works
Mata-Castillo, M., Hernández-Villegas, A., Gordillo, N., & Díaz, J. (2025) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-025-03345-7 · Cited by 5 · see citing works
Kapoor, D., Gupta, D., Gulzar, Y., Alkanan, M., Mir, M. S., Soomro, A. B., Uppal, M., & Reshma, R. (2025) · The Open Bioinformatics Journal · doi:10.2174/0118750362387304250716055022 · Cited by 5 · see citing works
Artificial intelligence-enabled precision medicine for inflammatory skin diseasesPreprint
Tang, A., Wei, M. L., Haemel, A. K., La, C., Sirota, M., & Lee, E. Y. (2025) · PubMed · doi:10.48550/arxiv.2505.09527 · Cited by 5 · see citing works
Chang, C.-Y., Khanum, A., Hsu, H.-H., Tsai, M., Chen, W., & Su, S.-G. (2024) · IEEE International Conference on Image Processing Workshops (ICIPW) · doi:10.1109/icipcw64161.2024.10769129 · Cited by 4 · see citing works
AI-Powered Diagnostic Tools in Dermatology
Venkatesh, K. P., Mehta, A., Hijaz, B. A., & Kvedar, J. C. (2025) · Dermatologic Clinics · doi:10.1016/j.det.2025.05.002 · Cited by 3 · see citing works
Clinical research on artificial intelligence medical diagnostic devices: A scoping review
Zhang, X., Liu, C., Sun, Y., Liangzhen, Y. O. U., Zhang, X., & Shang, H. (2026) · EngMedicine · doi:10.1016/j.engmed.2026.100120 · Cited by 3 · see citing works
Gong, C., Su, Y., Ruan, S., & Hu, H. (2026) · Medical Image Analysis · doi:10.1016/j.media.2026.104017 · Cited by 3 · see citing works
Skin Disease Recognition Based on Deep Learning Algorithms: A Review
Hayder, A. D., & Mohsin, A. (2024) · Indonesian Journal of Computer Science · doi:10.33022/ijcs.v13i3.3991 · Cited by 3 · see citing works
Xu, J., Huang, Y., Zhao, Z., Wang, D., Yang, C., & Zhang, K. (2024) · Advanced Healthcare Materials · doi:10.1002/adhm.202402891 · Cited by 3 · see citing works
Deep Learning Algorithms for Assessment of Post‐Thyroidectomy Scar Subtype
Chu, Y., Jung, S., Lee, S., Lee, S. G., Heo, Y., Lee, S.-H., Chang, H., Lee, Y. S., Kim, S., Lee, S. E., et al. (2025) · Dermatologic Therapy · doi:10.1155/dth/4636142 · Cited by 3 · see citing works
Advancements in diagnostics for visceral leishmaniasis: Current landscape and future directions
Azam, M., Singhal, B., & Singh, R. (2025) · Annals of Medical Science and Research · doi:10.4103/amsr.amsr_47_24 · Cited by 3 · see citing works
Ahmed, S. T., Guthur, A. S., Rai, P. K., & Pranava Swaroop, N. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.03.344 · Cited by 2 · see citing works
Skin Disease Detection Using Deep Learning
Sakthiradha, M., Sharvanthika, K. S., Priyanka, V., Sivanivitha, T., & Sriya, G. S. (2025) · International Conference on Intelligent Technologies (CONIT) · doi:10.1109/conit65521.2025.11166843 · Cited by 2 · see citing works
Indra, Z., Kurnia, R., Jusman, Y., Melia, T., Mahdiyah, E., & Candra, F. (2025) · International Conference on Information Technology and Computing (ICITCOM) · doi:10.1109/icitcom66635.2025.11265335 · Cited by 2 · see citing works
Regenerative Strategies for Androgenetic Alopecia: Evidence, Mechanisms, and Translational Pathways
Britva, R. L., & Gilhar, A. (2026) · Cosmetics · doi:10.3390/cosmetics13010019 · Cited by 2 · see citing works
Szondy, I., Martyin, K., Mohos, G., Kiss, T., Szabo, K., Boostani, M., Goldust, M., Grzybowski, A. E., Bánvölgyi, A., & Kiss, N. (2026) · La Presse Médicale · doi:10.1016/j.lpm.2026.104376 · Cited by 2 · see citing works
Sapaico-Alberto, A. F., Olaya-Cotera, S., & Flores-Castañeda, R. O. (2024) · Archives of Dermatological Research · doi:10.1007/s00403-024-03650-5 · Cited by 2 · see citing works
Big Data Analysis in Personalized Medicine
Banu, A., Balyan, P., Nisamudheen, N. A., Velayutham, D., Balyan, M. K., & Jithesh, P. V. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95502-7.00279-7 · Cited by 2 · see citing works
Bamorovat, M., Sharifi, I., Tahmouresi, A., Afshari, S. A. K., & Rashedi, E. (2025) · Transboundary and Emerging Diseases · doi:10.1155/tbed/5018632 · Cited by 1 · see citing works
Wijerama, N. S., Asanka, D., & Mahanama, T. V. (2025) · International Conference on Advanced Research in Computing (ICARC) · doi:10.1109/icarc64760.2025.10963207 · Cited by 1 · see citing works
A Novel High-Performance Deep-Learning-Based Approach for the Detection of Arsenic Poisoning
Moenks, N., Penava, P., & Buettner, R. (2025) · IEEE Access · doi:10.1109/access.2025.3646117 · Cited by 1 · see citing works
Fernández-Gutiérrez, G., Diego-Calagua, A., Leandro-Mendoza, A., & Pacheco, A. (2025) · F1000Research · doi:10.12688/f1000research.172054.1 · Cited by 1 · see citing works
Advanced Skin Disease Detection: A Deep Learning Approach with Real-Time Deployment
Apurupa, M., Sharma, Y., & Jothilakshmi (2025) · International Conference for Advancement in Technology (ICONAT) · doi:10.1109/iconat66879.2025.11362516 · Cited by 1 · see citing works
Graph-enhanced neighborhood association learning model for semi-supervised skin lesion diagnosis
Yu, C. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.109886 · Cited by 1 · see citing works
In-Hoc Concept Representations to Regularise Deep Learning in Medical Imaging
Corbetta, V., Dijkstra, F. S., Beets‐Tan, R. G. H., Kervadec, H., Wickstrøm, K., & Silva, W. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00759 · Cited by 1 · see citing works
Sudharson, K., Pavan, V. E. N. V. M., & Likhitha, P. (2026) · IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS) · doi:10.1109/sceecs68810.2026.11430168 · Cited by 1 · see citing works
An Efficient Deep Learning Approach for Skin Disease Prediction with On-Device Mobile Integration
Chaudhary, A. K. (2025) · International Conference on Artificial Intelligence and Speech Technology (AIST) · doi:10.1109/aist68591.2025.11441493 · Cited by 1 · see citing works
Dığış, M., Kaya, K., Demir, B., Cicek, I. N., Akdağ, D., & Akdağ, Ş. (2026) · Cutaneous and Ocular Toxicology · doi:10.1080/15569527.2026.2711643 · Cited by 1 · see citing works
Self -adaptive neural networks for domain generalization in medical image segmentation
Wang, Y., Wang, Z., Feng, Y., Zhang, L., Goh, R. S. M., Liu, Y., & Zhen, L. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.132242 · Cited by 1 · see citing works
Evaluating No-Code and Low-Code Platforms for Medical Image Classification: A Systematic Review
Abdulkareem, A. W., Ahmed, A. M., Hassan, B. A., Rassul, Y. H., & Rashid, T. A. (2026) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-026-01919-x · Cited by 1 · see citing works
CBAM-Xception: An Attention-Guided Framework for Skin Cancer Classification
Ahmmed, F., Alaly, A., Mehnaj, S., Antik, A. R., Hossen, M. J., & Mridha, M. F. (2026) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-026-01999-9 · Cited by 1 · see citing works
Marini, N., Liang, Z., Rajaraman, S., Xue, Z., & Antani, S. (2026) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2026.105055 · Cited by 1 · see citing works
Planner–Executor Architecture for Teledermatology Triage with Safety Guardrails
Saxena, T., Reddy, B. H., Bansal, H., Mudgal, I., Devendiran, R., & Puralkar, H. (2026) · International Conference on Computing for Sustainable Global Development · doi:10.23919/indiacom70271.2026.11526130 · Cited by 1 · see citing works
Wu, H. L. A., Floch, P. L., Duverdier, A., Irvine, A. D., Dubrac, S., & Tanaka, R. J. (2026) · JID Innovations · doi:10.1016/j.xjidi.2026.100513 · Cited by 1 · see citing works
Virtual Simulation-Based Study on Sports Anatomy: Technological Applications and Future Development
Xiao, H., Ren, J., Ling, H., Jiang, C., & Wang, P. (2023) · American Journal of Sports Science · doi:10.11648/j.ajss.20231104.11 · Cited by 1 · see citing works
Enhancing CT Segmentation Security against Adversarial Attack: Most Activated Filter Approach
Lee, W., & Kim, Y. (2024) · Applied Sciences · doi:10.3390/app14052130 · Cited by 1 · see citing works
Kucharski, D., Wijata, A. M., Fu, L., Lin, W., Xue, Y., Kawa, J., Zheng, Y., Lip, G. Y. H., & Nalepa, J. (2024) · Proceedings - International Conference on Image Processing · doi:10.1109/icip51287.2024.10647780 · Cited by 1 · see citing works
Wang, H., Liu, Z., Pan, H., Liu, K., Wen, Y., Qin, Y., Dang, J., Li, M., Cui, Z., Jiang, T., et al. (2025) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2025.06.1567 · Cited by 1 · see citing works
From benchmark to bedside: can multimodal artificial intelligence withstand real-world dermatology?
Lei, Z. (2025) · British Journal of Dermatology · doi:10.1093/bjd/ljaf299 · Cited by 1 · see citing works
Extensive Review on Humidity and Moisture on Skin Device Analysis
Lazim, N. A. M., & Setapar, S. H. M. (2025) · Journal of Human Centered Technology · doi:10.11113/humentech.v4n2.104 · Cited by 1 · see citing works
Diagnose Skin Face Problems by Comparing Classification Algorithms
Ardini, M., Kinanta, A. R., Palagoro, V. B., Wibowo, M. A. K., & Aripin, A. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-97-1463-6_24
(2024) · British Journal of Dermatology · doi:10.1093/bjd/ljae360.020
Ke, Z., Wang, Y., Guo, R., Du, M., Ji-sheng, Z., Wang, G., & Zhang, Y. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-5809-1_7
Kabir, M. S., Tanvir, K., Sintheia, T. S., Gomes, D. J., & Jony, A. I. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.110911
Swarnkar, S. K., Guru, A., Dixit, R. R., Devarajan, H. R., Balasubramanian, S., & Jindal, R. K. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-2329-7_41
Quantifying Cutaneous Dermatomyositis: A Novel 3D Image–Based Approach
Pongtarakulpanit, N., Bishnoi, A., Chandra, T., Dzanko, S., Gkiaouraki, E., Keret, S., Silva, R. L., Sriram, S., Saygın, D., Liarski, V. M., et al. (2025) · The Journal of Rheumatology · doi:10.3899/jrheum.2025-0537
MF-Mamba: An Efficient Multi-Feature Fusion Mamba Model for Medical Image Segmentation
Xing, W., Geng, J., Yao, X., Liu, M., Xie, Y., & Yuan, M. (2025) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc67675.2025.00061
Image-based skin disease detection: Psoriasis, Eczema, and Acne using VGG16 and ResNet-50
Kumbhar, A., Jagadale, S., Pol, G., Budaragade, A. P., & Gurav, U. (2025) · EPJ Web of Conferences · doi:10.1051/epjconf/202534101017
Artificial Intelligence in Acne Diagnosis and Management: Current Applications and Future Directions
Choy, S.-P., & Rampes, S. (2025) · International Journal of Dermatology · doi:10.1111/ijd.70215
Self-citationFeatures of dermatoglyphics as a genetic marker of longevity predisposition
Асадов, Ш. А.-О., & Єна, М. С. (2025) · doi:10.71012/pro-ageing-2025-3-04
Signer, B., Mokhtari, A., Cazzaniga, S., Brand, F. L., Caro, G., de Viragh, P. A., Heidemeyer, K., Hosseini, A., Iorizzo, M., Junge, A., et al. (2026) · Diagnostics · doi:10.3390/diagnostics16010169
Kasukurthi, A., & Davuluri, R. L. (2026) · IEEE Access · doi:10.1109/access.2026.3664686
Smart-Derma: A Deep Learning Based Intelligent Skincare Recommendation System
Sree, K. D., Rao, M. N., Lingheswarrao, M. T., Avinash, G., & Sekhar, B. V. V. H. C. (2026) · International Journal of Artificial Intelligence Engineering and Transformation · doi:10.54660/ijaiet.2026.7.1.49-54
Automated Detection of Inflammatory Skin Disorders Through Image-Based Deep Learning
Pattnayak, P., Mohanty, A., Das, T., Patnaik, S., Sahu, S., & Mallick, N. (2025) · International Conference on Advancement in Futuristic Technologies (ICAFT) · doi:10.1109/icaft66710.2025.11453186
Skin Disease Detection Using Convolutional Neural Networks and Image Processing
Chettri, Y. R., & Shah, R. (2025) · International Conference on Emerging Technologies in Electronics and Green Energy (ICETEG) · doi:10.1109/iceteg66194.2025.11473221
Deep CNN Framework for Multi-Class Skin Identification Using Datasets
Dhoble, G. (2026) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2026.80293
Artificial Intelligence for Dermatological Diagnosis and Care: A Comprehensive Overview
Asadollahi, S., & Tarikhi, M. (2026) · Studies in computational intelligence · doi:10.1007/978-3-032-17975-3_6
Emran, T., Karmoker, S., Deya, I. H., Akter, H., Keya, K. K., Partho, P. K. R., & Bhowmik, P. (2025) · International Conference on Computer and Information Technology (ICCIT) · doi:10.1109/iccit68739.2025.11490438
Zhang, J., Hong, P., Wang, N., Liu, Y., Wang, Y., & Chen, L. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1829629
Glow-Code: A Hybrid AI-Powered Platform for Personalized Skincare Analysis and Recommendation
Bhandary, J., Bhat, B., Ishwarya, Shetty, A., Goveas, A. L., & Shukla, A. (2026) · International Conference on Artificial Intelligence and Data Engineering (AIDE) · doi:10.1109/aide69088.2026.11544688
Retracted: Skin Lesion Classification for Melanoma Detection Using Pretrained CNN Architectures
Sarma, C. A., Lakshmi, P. S., Jose, S. D., Sujithra, L. R., Prathiba, M. K., & Subramanyam, M. V. (2026) · International Conference on Signal, Systems, and Computing for Next-Gen Automation (ICSSCNA) · doi:10.1109/icsscna68616.2026.11546834
RetractedAlshalhoob, H., Albelowi, L. M., Alotaibi, A. S., Alanazi, S. K., Alturki, L. E., Alsindi, Z. S., Alduraywish, W. A., Almulla, S. A., Almulla, A. A., Almagushi, N. A., et al. (2026) · Frontiers in Drug Safety and Regulation · doi:10.3389/fdsfr.2026.1825005
Cheng, Y., Zhou, C., Wang, P., Liu, H., & Han, Y. (2026) · Global Health & Medicine · doi:10.35772/ghm.2026.01058
- Authors
- Choy, S. P., Kim, B. J., Paolino, A., Tan, W. R., Lim, S. M. L., Seo, J., Tan, S. P., Francis, L., Tsakok, T., Simpson, M., Barker, J. N. W. N., Lynch, M. D., Corbett, M. S., Smith, C. H., & Mahil, S. K.
- Affiliations
- King's College London (GB)
- St George’s University Hospitals NHS Foundation Trust (GB)
- Maidstone and Tunbridge Wells NHS Trust (GB)
- Imperial College London (GB)
- Barking, Havering And Redbridge University Hospitals NHS Trust (GB)
- University of York (GB)
- Corresponding author
- Mahil, Satveer K.
- ORCID
- Venue
- npj Digital Medicine
- Published
- 2023-09-27
- Volume
- 6
- Issue
- 1
- Pages
- 180-180
- ISSN
- 2398-6352 (ISSN-L)
- PMC
- PMC10533565
- OpenAlex
- W4387104519
- Cited by
- 105
- References
- 29
- Citations by year
- 2026: 35 · 2025: 43 · 2024: 25 · 2023: 2
- FWCI
- 3.40
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- npj Digital Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (acceptedVersion, public-domain)
- Research Portal (King's College London) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- DH | National Institute for Health Research (NIHR302258)
- Psoriasis Association (PhD Studentship)
- RCUK | Medical Research Council ([MR/R001839/1])
- Topics
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Health Sciences › Medicine › Dermatology › Dermatology and Skin Diseases
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- deep learning, dermatological diagnosis, skin lesion image analysis, acne, psoriasis, atopic dermatitis, rosacea, vitiligo, urticaria, disease severity classification, diagnostic accuracy, risk of bias, external validation, skin type, dermatology
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