Artificial Intelligence in Dermatology: Promise, Pitfalls, and Ethical Challenges

Conclusions

• Artificial intelligence is already deeply integrated into dermatology and is beginning to meaningfully improve documentation, triage, literature review, and other clinical workflows.
• In image-based dermatology, AI can sometimes reach dermatologist-level performance and may improve efficiency and diagnostic support, but its accuracy is highly dependent on training data quality and representation.
• AI should be viewed as an assistive tool that augments clinical judgment rather than replaces physicians, because overreliance can weaken skills and obscure accountability.
• A major conclusion is that current AI systems can amplify existing biases, especially for darker skin tones and out-of-distribution cases, so diverse representative datasets are essential.
• Patient use of AI is increasing, meaning clinicians will increasingly need to respond to AI-generated questions, self-diagnoses, and misinformation.
• Large language models are especially useful for tedious, template-based tasks like notes, letters, translations, and summaries, but their outputs still require careful human review.
• The major risks of AI in dermatology include bias, privacy breaches, weak transparency, misleading confidence, liability uncertainty, and inequitable access.
• The safest and most useful path forward is responsible deployment with robust regulation, HIPAA compliance, standardized consent, explainability, interdisciplinary oversight, and equitable access.
• AI may ultimately help humanize dermatology by reducing administrative burden and giving clinicians more time for direct patient care.
• Future AI systems, including agentic AI, may automate more complex coordination tasks, but they must be developed and used cautiously, with a strong focus on ethics and misinformation detection.