Practical Considerations for Adopting AI Clinical Decision Support Tools and Large Language Models in Dermatology.

Authors

Ishani Rao Dhamsania, Albert E Zhou, Danielle M Khalilzadeh, Nahid Y Vidal, Laura K Ferris, Jane M Grant-Kels

Published in

Clinics in dermatology. Sep 21, 2026. Epub Sep 21, 2026.

Abstract

Artificial Intelligence (AI) in dermatology is a rapidly evolving field, offering promising advancements in the diagnosis and management of skin cancers. As global skin cancer incidence rises, tools like machine-learning based systems and large language models (LLMs) have demonstrated significant potential in supporting triage of skin cancer patients. Yet several key practical and ethical considerations stand in the way of widespread adoption. Limitations in dataset diversity, medicolegal ambiguity, and concerns regarding transparency of AI-generated outputs are challenges that must be addressed. This review explores the current landscape of AI technologies in dermatology, including diagnostic imaging tools, workflow-integrated systems, and generative AI models. We evaluate the advantages and limitations of these tools, workflow optimization, patient trust in AI-driven preliminary diagnoses, and the importance of preserving the physician-patient relationship with increasing integration of AI in dermatology.

PMID:
42767470
Bibliographic data and abstract were imported from PubMed on 22 Sep 2026.

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