Revolutionizing the Diagnosis of Psoriasis: Harnessing the Power of Artificial Intelligence for Early Detection and Improved Patient Outcomes

Abstract

Objective: Timely recognition of 
psoriasis is essential since it 
contributes to more effective 
treatment outcomes and enhances the 
quality of life for those affected by 
the condition. The approaches employed in diagnosing psoriasis 
include visual inspection and biopsy, yet these methods require a 
labour-intensive subjective evaluation.
Methods: The study performed an extensive evaluation of 
multiple sources from databases like PubMed, IEEE Xplore, 
and Scopus, spanning from January 2018 to August 2024. It 
investigated three main uses of AI/ML in psoriasis diagnosis: 
predictive early detection models and systems that assist in 
making tailored treatment choices. The report delved into the 
regulatory, technical, and ethical challenges faced by 
dermatology in implementing AI technologies.
Results: By leveraging artificial intelligence, machine learning 
techniques, and deep learning models, doctors can achieve 
exceptional precision in diagnosing psoriasis by analyzing 
images of patients' skin in both clinical and microscopic 
settings. The diagnostic tool known as Convolutional Neural 
Networks (CNNs) within artificial intelligence facilitates 
quicker and more precise differentiation between psoriasis and 
other skin disorders.
Conclusion: Through the combination of AI and ML 
technologies, healthcare providers now have a sophisticated 
approach to diagnosing psoriasis, enabling early identification 
of the disease and the development of personalized treatment 
plans for patients. Advancements in the diagnosis of psoriasis 
are being achieved by tackling modeling challenges, ensuring 
data security, and fostering acceptance within healthcare 
institutions.