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Related Experiment Video

Updated: Jun 7, 2026

Deep Dermal Injection As a Model of Candida albicans Skin Infection for Histological Analyses

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Published on: June 13, 2018

Agentic AI in Dermatology: A Call to Action.

Brian Chu¹, Heather Shen², Ivy Lee³

¹Department of Dermatology, Brown University, 593 Eddy St, APC 10th Floor, Providence, RI, 02903, United States, 1 4014447959.

JMIR Dermatology

|June 5, 2026

Summary

Agentic artificial intelligence (AI) systems offer autonomous capabilities in healthcare, with dermatology poised for significant benefits. A proposed risk framework aims to guide the safe and equitable adoption of these advanced AI tools.

Keywords:

AI agents AI in dermatology agentic AI artificial intelligence technology in dermatology

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Last Updated: Jun 7, 2026

Deep Dermal Injection As a Model of Candida albicans Skin Infection for Histological Analyses

Deep Dermal Injection As a Model of Candida albicans Skin Infection for Histological Analyses

Published on: June 13, 2018

Area of Science:

Medical Informatics
Artificial Intelligence in Healthcare

Background:

Artificial intelligence (AI) is evolving from passive tools to proactive, agentic systems capable of autonomous actions.
These agentic AI systems can gather information, execute tasks, and collaborate without constant human prompting.
Early healthcare adoption shows feasibility across specialties, with dermatology being a prime candidate due to high patient volumes and administrative burdens.

Purpose of the Study:

To propose a risk-stratification framework for the responsible adoption of agentic AI in clinical settings.
To identify key barriers hindering the widespread implementation of agentic AI in healthcare.

Main Methods:

Development of a risk-stratification framework based on clinical risk and task reversibility.
Analysis of current limitations and challenges for agentic AI adoption in healthcare.

Main Results:

Agentic AI systems demonstrate potential for autonomous, multi-step actions in healthcare settings.
Dermatology's workflows are well-suited for agentic AI integration.
A framework for risk stratification and identification of adoption barriers are presented.

Conclusions:

Agentic AI offers significant potential to enhance healthcare, particularly in dermatology.
Addressing limitations in reliability, interoperability, liability, privacy, and regulation is crucial for widespread adoption.
Proactive engagement by dermatologists is essential for the safe, equitable, and value-aligned development of agentic AI.