Artificial Intelligence in Dermatology: Challenges and Perspectives.

Area of Science:

• Dermatology
• Medical Imaging
• Artificial Intelligence

Background:

• Non-melanoma skin cancer is the most common cancer globally, while melanoma is a leading cause of cancer death.
• Dermoscopy enhances skin cancer recognition accuracy but still presents diagnostic challenges for clinicians.
• Artificial intelligence (AI), particularly machine learning and convolutional neural networks (CNNs), is emerging as a significant tool in dermatology.

Discussion:

• CNN algorithms demonstrate performance comparable or superior to clinicians in classifying skin lesions from dermoscopic images.
• Reader studies highlight the potential of AI in skin cancer diagnosis, showing promising results.
• Generalizability and practical application of AI algorithms in routine clinical settings require further investigation.

Key Insights:

• AI offers potential for early evaluation and diagnosis of skin cancer, aiding dermatologists.
• Studies indicate CNNs can accurately classify skin lesions, supporting diagnostic efforts.
• Overcoming limitations in AI generalizability is crucial for clinical integration.

Outlook:

• Future research should focus on addressing pitfalls identified in reader studies to enhance AI applicability.
• Developing strategies to overcome current limitations will facilitate AI integration into clinical practice.
• AI presents significant opportunities to improve dermatological care and patient outcomes.