Advances in Dermatological Diagnostics: A Survey of Artificial Intelligence, Biological Homeostasis, and Optical Modalities

This survey explores the evolving landscape of dermatology, characterized by a significant shift from subjective visual observation toward objective, automated, and explainable diagnostic tools. By examining the intersection of advanced computational technologies and biological science, the survey synthesizes findings from five distinct research areas that address the limitations of current clinical workflows, such as visual variability and limited specialist availability.

The technological review details the implementation of a multiclass transfer learning system using EfficientNet-B5, which achieved a top-3 accuracy of 95.96% across ten disease categories. A critical innovation highlighted is the use of Grad-CAM visual heatmaps, which provide interpretability by revealing the specific image regions influencing the model’s predictions. Further technical evolution is traced from traditional machine learning methods (e.g., the ABCD rule and SVMs) to advanced Vision Transformers (ViT) and the FUSCANet architecture. These modern approaches utilize spatial-channel attention mechanisms to capture long-range dependencies in images, significantly outperforming traditional clinical diagnostic rates. Complementing the computational analysis, the survey examines the biological foundations of skin homeostasis, identifying a complex neuroendocrine-immune system and a "four-part" barrier structure responsible for maintaining the skin's steady state. It analyzes how external disruptors, such as UV radiation and air pollution, impair these barriers, leading to common conditions like acne, sensitivity, and premature aging. Finally, the survey reviews optical non-invasive modalities, including Confocal Microscopy and Optical Coherence Tomography (OCT), which provide high-resolution structural and molecular profiles to complement traditional invasive biopsies.

Ultimately, the survey concludes that the future of precise dermatological care lies in the integration of high-accuracy, interpretable AI systems with a deep understanding of biological homeostasis and advanced optical instrumentation.

Key Words: - EfficientNet-B5, Grad-CAM, Vision Transformers (ViT), FUSCANet