Research on Automatic Recognition and Auxiliary Diagnosis of Artificial Intelligence in Skin Diseases

Ping Li; Manyi Zhuang; Jun Tang; Jian Huang

doi:10.54097/a0askn96

Authors

Ping Li
Manyi Zhuang
Jun Tang
Jian Huang

DOI:

https://doi.org/10.54097/a0askn96

Keywords:

Artificial Intelligence (AI), Skin Disease Diagnosis, Multimodal Fusion, Personalized Diagnosis, Explainable AI (XAI), Data Augmentation

Abstract

Artificial intelligence (AI) has made significant strides in skin disease diagnosis, demonstrating impressive potential in the classification and detection of conditions such as skin cancer. However, several challenges remain that hinder the full clinical adoption of AI-driven diagnostic systems. This paper reviews the current advancements and key research areas aimed at optimizing AI models for dermatological applications. Key areas for future research include the development of more advanced deep learning architectures, such as multimodal fusion methods, which integrate dermoscopic images with structured clinical data for more comprehensive diagnostics. Additionally, there is a growing emphasis on creating personalized AI models that account for individual patient characteristics, such as age, gender, and genetics, to improve diagnostic accuracy. The integration of explainable AI (XAI) techniques, auxiliary tasks like lesion segmentation, and multi-center clinical research is essential to ensure transparency, trust, and generalizability across diverse populations. Moreover, ethical concerns related to bias, data privacy, and accountability must be addressed to ensure fairness and transparency in AI systems. This review highlights the importance of interdisciplinary collaboration and proposes future directions to enhance the reliability, scalability, and clinical applicability of AI-driven skin disease diagnosis, ultimately improving patient outcomes and accessibility to care.

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References

[1] Li, Xuhui, et al. Image analysis and diagnosis of skin diseases-a review. Current Medical Imaging Reviews 19.3 (2023): 199-242.

[2] Gautam, Vinay, et al. Early Skin Disease Identification Using eep Neural Network. Comput. Syst. Sci. Eng. 44.3 (2023): 2259-2275.

[3] Anand, Vatsala, et al. An automated deep learning models for classification of skin disease using Dermoscopy images: A comprehensive study. Multimedia Tools and Applications 81.26 (2022): 37379-37401.

[4] Combalia, Marc, et al. Validation of artificial intelligence prediction models for skin cancer diagnosis using dermoscopy images: the 2019 International Skin Imaging Collaboration Grand Challenge. The Lancet Digital Health 4.5 (2022): e330-e339.

[5] Nigar, Natasha, et al. A deep learning approach based on explainable artificial intelligence for skin lesion classification. IEEE Access 10 (2022): 113715-113725.

[6] AlDera, Shaden Abdulaziz, and Mohamed Tahar Ben Othman. A model for classification and diagnosis of skin disease using machine learning and image processing techniques. International Journal of Advanced Computer Science and Applications 13.5 (2022).

[7] Wei, Zenghui, Qiang Li, and Hong Song. Dual attention-based network for skin lesion classification with auxiliary learning. Biomedical Signal Processing and Control 74 (2022): 103549.

[8] Chen, Qian, et al. MDFNet: application of multimodal fusion method based on skin image and clinical data to skin cancer classification. Journal of Cancer Research and Clinical Oncology 149.7 (2023): 3287-3299.

[9] Xiao, Junsheng, Jirui Li, and Honghao Gao. FS3DCIoT: A few-shot incremental learning network for skin disease differential diagnosis in the consumer IoT. IEEE Transactions on Consumer Electronics 69.4 (2023): 786-797.

[10] Groh, Matthew, et al. Deep learning-aided decision support for diagnosis of skin disease across skin tones. Nature Medicine 30.2 (2024): 573-583.

[11] Jones, O. T., et al. Artificial intelligence and machine learning algorithms for early detection of skin cancer in community and primary care settings: a systematic review. The Lancet Digital Health 4.6 (2022): e466-e476.

[12] Wu, Renkai, et al. Mhorunet: High-order spatial interaction unet for skin lesion segmentation. Biomedical Signal Processing and Control 88 (2024): 105517.

[13] Almuayqil, Saleh Naif, Sameh Abd El-Ghany, and Mohammed Elmogy. Computer-Aided diagnosis for early signs of skin diseases using multi types feature fusion based on a hybrid deep learning model. Electronics 11.23 (2022): 4009.

[14] Khurshid, Mahapara, Mayank Vatsa, and Richa Singh. Optimizing Skin Lesion Classification Via Multimodal Data and Auxiliary Task Integration. 2024 IEEE International Symposium on Biomedical Imaging (ISBI). IEEE, 2024.

[15] Sun, Jie, et al. Machine learning methods in skin disease recognition: a systematic review. Processes 11.4 (2023): 1003.

[16] Agarwal, Raghav, and Deepthi Godavarthi. Skin disease classification using CNN algorithms. EAI Endorsed Transactions on Pervasive Health and Technology 9.1 (2023).

[17] Singh, Jagandeep, Jasminder Kaur Sandhu, and Yogesh Kumar. An analysis of detection and diagnosis of different classes of skin diseases using artificial intelligence-based learning approaches with hyper parameters. Archives of Computational Methods in Engineering 31.2 (2024): 1051-1078.

[18] Fraiwan, Mohammad, and Esraa Faouri. On the automatic detection and classification of skin cancer using deep transfer learning. Sensors 22.13 (2022): 4963.

[19] Escalé-Besa, Anna, et al. Exploring the potential of artificial intelligence in improving skin lesion diagnosis in primary care. Scientific Reports 13.1 (2023): 4293.

[20] Pesqué, David, et al. Differential diagnosis of contact dermatitis: a practical‐approach review by the EADV Task Force on contact dermatitis. Journal of the European Academy of Dermatology and Venereology 38.9 (2024): 1704-1722.

[21] Kshirsagar, Pravin R., et al. Deep learning approaches for prognosis of automated skin disease. Life 12.3 (2022): 426.

[22] Anand, Vatsala, Sheifali Gupta, and Deepika Koundal. Skin disease diagnosis: challenges and opportunities. Proceedings of Second Doctoral Symposium on Computational Intelligence: DoSCI 2021. Springer Singapore, 2022.

[23] Ahammed, Mostafiz, Md Al Mamun, and Mohammad Shorif Uddin. A machine learning approach for skin disease detection and classification using image segmentation. Healthcare Analytics 2 (2022): 100122.

[24] Sangers, Tobias E., et al. Position statement of the EADV Artificial Intelligence (AI) Task Force on AI‐assisted smartphone apps and web‐based services for skin disease. Journal of the European Academy of Dermatology and Venereology 38.1 (2024): 22-30.

[25] Li, Zhouxiao, et al. Artificial intelligence in dermatology image analysis: current developments and future trends. Journal of clinical medicine 11.22 (2022): 6826.

[26] Inthiyaz, Syed, et al. Skin disease detection using deep learning. Advances in Engineering Software 175 (2023): 103361.

[27] Li, Shiyang, et al. An actinic keratosis auxiliary diagnosis method based on an enhanced MobileNet model. Bioengineering 10.6 (2023): 732.

[28] Lu, Jianyun, et al. Image classification and auxiliary diagnosis system for hyperpigmented skin diseases based on deep learning. Heliyon 9.9 (2023).

[29] Ye, Shengzhen, and Mingling Chen. The emerging role of artificial intelligence in diagnosis and clinical analysis of dermatology. Dermatologica Sinica 41.3 (2023): 145-152.

[30] Muhaba, Kedir Ali, et al. Automatic skin disease diagnosis using deep learning from clinical image and patient information. Skin health and disease 2.1 (2022): ski2-81.

[31] Parashar, Tarun, et al. Skin disease detection using deep learning. 2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART). IEEE, 2022.

[32] Ghaffar Nia, Nafiseh, Erkan Kaplanoglu, and Ahad Nasab. Evaluation of artificial intelligence techniques in disease diagnosis and prediction. Discover Artificial Intelligence 3.1 (2023): 5.

[33] Li, Huanyu, et al. Deep skin diseases diagnostic system with Dual-channel Image and Extracted Text. Frontiers in Artificial Intelligence 6 (2023): 1213620.

[34] Li, Xiaoqiong, et al. Electret-based flexible pressure sensor for respiratory diseases auxiliary diagnosis system using machine learning technique. Nano Energy 114 (2023): 108652.

[35] Zhou, Yan-Jie, et al. A novel multi-task model imitating dermatologists for accurate differential diagnosis of skin diseases in clinical images. International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer Nature Switzerland, 2023.

[36] Vayadande, Kuldeep. Innovative approaches for skin disease identification in machine learning: A comprehensive study. Oral Oncology Reports (2024): 100365.

[37] Aruna R, Dr, et al. [Retracted] An Enhancement on Convolutional Artificial Intelligent Based Diagnosis for Skin Disease Using Nanotechnology Sensors. Computational Intelligence and Neuroscience 2022.1 (2022): 9539503.

[38] Zheng, Guiyue, et al. Research on skin disease health detection of college students based on deep learning. Journal of Physics: Conference Series. Vol. 2289. No. 1. IOP Publishing, 2022.

[39] Maduranga, M. W. P., and Dilshan Nandasena. Mobile-based skin disease diagnosis system using convolutional neural networks (CNN). International Journal of Image, Graphics and Signal Processing 12.3 (2022): 47.

[40] Ke, C. H. A. I., et al. Updated role of high-frequency ultrasound in assessing dermatological manifestations in autoimmune skin diseases. Acta Dermato-Venereologica 102 (2022): 1969.