Comprehensive Analysis of Machine Learning Applications in Dermatology: Advancements in Diagnosis, Prognosis, and Personalized Treatment Strategies for Skin Disorders

Abstract

Objective: This research investigates 
the use of Artificial Intelligence (AI) 
and Machine Learning (ML) 
technologies in dermatology research and healthcare services. The 
evaluation examines how these technologies enhance diagnostic 
precision and treatment effectiveness, as well as patient results, while 
also considering the obstacles to incorporating these technologies into 
clinical settings.
Methods: The research was conducted through a systematic 
evaluation of current studies available on PubMed, IEEE 
Xplore, and Scopus from January 2018 to August 2024. This 
review focuses on the application of AI and ML in 
dermatology, with particular attention to disease diagnosis, 
treatment approaches, and customized patient care solutions. 
Furthermore, the study explored the ethical and regulatory 
considerations affecting the adoption of AI in the healthcare 
sector.
Results: The domain of dermatology has advanced greatly 
thanks to AI and machine learning technologies, allowing 
doctors to more efficiently identify skin cancers, especially 
melanomas. The diagnostic capabilities of machine learning 
algorithms exceed those of conventional assessment methods 
in recognizing malignant lesions from dermoscopic images.
Conclusion: Innovations in healthcare driven by AI and 
machine learning face obstacles stemming from subpar data 
quality, making the situation more complicated. Additionally, 
there is an urgent requirement for improved protections of 
patient privacy and more transparent descriptions of how 
algorithms operate.