Utility of artificial intelligence in dermatology: Challenges and perspectives

Medicine is entering a transformative era with disruptive technologies such as virtual reality, genomic prediction, data analytics, personalized medicine, stem cell therapy, 3-D printing, and nanorobotics. Dermatology is significantly impacted by these advancements, particularly through artificial intelligence (AI). AI, defined as devices performing functions typically requiring human intelligence, plays an increasingly prominent role in healthcare. John McCarthy coined the term AI in 1956. In dermatology, AI aids in diagnosis, treatment planning, and understanding diseases across communities. Machine learning and deep learning, subsets of AI, require extensive datasets and robust analysis to improve accuracy and performance. AI's integration into dermatology is revolutionizing the field by enabling precision, reducing errors, and minimizing staffing needs. AI tools support dermatologists in diagnosing and treating various conditions, from psoriasis and acne to dermatitis and ulcers. Convolutional neural networks (CNNs) enhance the classification of skin lesions, while predictive models optimize treatment strategies based on patient data. AI's role extends to oncology, where it improves skin cancer detection through image analysis and histopathological assessment. Despite its potential, AI in dermatology faces challenges such as data quality, representativeness, algorithm transparency, and ethical considerations. Addressing biases, standardizing imaging protocols, and enhancing human-machine collaboration are crucial for maximizing AI's benefits. AI holds immense promise in dermatology, offering innovative solutions to enhance patient care and diagnostic accuracy. The future of AI in dermatology includes advancements in vision-language models, federated learning, and precision medicine approaches. Overcoming challenges related to data privacy, regulatory standards, and model evaluation is essential for successful integration into clinical practice. Collaborative efforts among stakeholders are vital to drive progress and realize the full potential of AI, ultimately improving patient outcomes globally.