Table 1. A summary table for the highlights and limitations of some of the notable related works published recently in the year 2022–2023.

Major works	Highlights	Limitations
Wang et al. (2022)	Deep learning-based system for multi-modal classification of skin diseases.	Limited evaluation of added architectures; no extensive comparison with single-modal models.
Liang et al. (2022)	Skin lesion recognition with part-whole relations and multi-instance learning.	The potential complexity of multi-instance learning; limited external validation.
Zhang, Litson & Feldon (2022)	Deep attention network for rare disease recognition in skin images.	Limited evaluation of common skin diseases; no extensive comparison with other models.
Chen et al. (2023)	Multi-modal deep learning for dermatological disease classification.	No extensive comparison with single-modal models; potential data fusion challenges.
Gong et al. (2022)	Self-supervised learning for skin disease classification using modified ResNet with triplet loss.	Limited external validation; potential complexity of self-supervised learning.
Bhatnagar et al. (2022)	Deep learning framework for histopathological image classification of skin diseases.	Limited external validation; focus on histopathological images.
Zhang, Litson & Feldon (2022)	Deep learning-based system for pediatric skin disease classification.	Limited evaluation on adult skin diseases; potential age-related differences.
Oliveira et al. (2023)	Introduces a multi-task convolutional neural network for the classification and segmentation of chronic venous disorders.	Limited evaluation of other skin diseases; potential task complexity.
Kalsotra & Arora (2023)	Conducts performance analysis of U-Net with hybrid loss for foreground detection.	Limited evaluation of other loss functions; potential sensitivity to hyper parameters.