Basic principles of artificial intelligence in dermatology explained using melanoma

Area of Science:

• Medical Informatics
• Artificial Intelligence in Healthcare
• Dermatology

Background:

• Artificial intelligence (AI) adoption in medicine is accelerating, yet healthcare professionals often lack foundational technical understanding.
• This knowledge gap hinders the effective clinical and research application of AI technologies.
• Malignant melanoma presents a significant healthcare challenge, with early detection crucial for improved patient prognosis.

Purpose of the Study:

• To elucidate the fundamental principles and classification of artificial intelligence (AI).
• To utilize melanoma detection as a specific, illustrative example for understanding AI mechanisms.
• To bridge the gap in understanding AI technology for clinical and research applications.

Main Methods:

• Review of artificial intelligence (AI) concepts and classifications.
• Detailed illustrations explaining AI technology.
• Case study focusing on AI-driven melanoma recognition from medical images.

Main Results:

• AI, particularly machine learning, demonstrates capability in identifying melanomas from images.
• AI performance in melanoma detection can approach that of experienced dermatologists under specific conditions.
• Elaborate illustrations aid in demystifying AI technology for a broader audience.

Conclusions:

• A clear understanding of AI's functioning is essential for its successful integration into clinical practice.
• AI offers a promising solution to address healthcare system burdens, such as the finite availability of dermatological experts.
• This review provides a foundational understanding of AI, using melanoma as a key example, to facilitate its clinical adoption.