Agentic Artificial Intelligence in Dermatology

The rapid evolution of artificial intelligence (AI) in dermatology has progressed from passive diagnostic support to increasingly autonomous systems capable of independent reasoning and action. A particularly transformative development is agentic AI, which refers to AI systems that can set goals, make decisions, and execute multistep tasks with minimal human intervention. While conventional AI models in dermatology, primarily convolutional neural networks (CNNs) and supervised classifiers, have demonstrated high accuracy in lesion classification, they remain largely reactive tools. In contrast, agentic AI introduces proactive, context-aware, and iterative decision-making capabilities, potentially redefining the delivery of dermatologic care 1. Agentic AI systems use advances in large language models (LLMs), reinforcement learning, and tool-augmented architectures. These systems can integrate multimodal data including clinical images, dermoscopy, histopathology, and electronic health records (EHRs) to dynamically formulate diagnostic hypotheses, recommend management plans, and even initiate follow-up actions 2. A key scientific advancement enabling agentic AI is the integration of chain-of-thought reasoning and retrieval-augmented generation (RAG). These mechanisms allow AI systems to access external medical knowledge bases, clinical guidelines, and real-world datasets in real time, thereby improving diagnostic accuracy and contextual relevance. Furthermore, reinforcement learning with human feedback (RLHF) enhances the system's ability to match clinical reasoning patterns and ethical standards. In dermatology, where visual pattern recognition connects with clinical judgment, such hybrid architectures are particularly advantageous 3. Agentic AI also holds promise for longitudinal disease management. Chronic dermatologic conditions, such as psoriasis and atopic dermatitis, require continuous monitoring and treatment adjustments. An agentic system could track disease severity through patient-uploaded images, assess treatment response, adjust therapeutic regimens based on clinical guidelines, and alert clinicians to disease flares. This approach aligns with the concept of a “closed-loop dermatology system,” similar to automated insulin delivery systems in endocrinology. Despite these advancements, several challenges must be addressed before widespread clinical implementation. First, the reliability of agentic AI systems is heavily dependent on the quality and diversity of training data. Dermatology datasets are often biased toward lighter skin types, raising concerns about diagnostic disparities in darker skin. Second, the interpretability of agentic systems remains limited. While chain-of-thought reasoning improves transparency, the underlying decision pathways are still complex and may not fully satisfy regulatory requirements for explainability 4. Ethical and legal considerations are also critical. The autonomous nature of agentic AI raises questions regarding accountability in cases of misdiagnosis or inappropriate treatment recommendations. Current regulatory frameworks, including those from the U.S. Food and Drug Administration (FDA), are primarily designed for static AI models and may not adequately address the challenges posed by continuously learning, adaptive systems. Additionally, patient privacy and data security must be protected, especially when agentic systems interact with EHRs and cloud-based platforms 5. From a clinical perspective, agentic AI should be regarded not as a replacement for dermatologists but as a complement to clinical expertise. The optimal model is likely a human-AI collaborative framework, where agentic systems handle data integration, preliminary analysis, and routine decision-making, while dermatologists provide oversight, contextual judgment, and patient-centered care. This synergy has the potential to improve efficiency, expand access to dermatologic services, and enhance diagnostic precision. In conclusion, agentic AI represents a paradigm shift in dermatology, moving beyond static diagnostic tools to autonomous, adaptive clinical systems. While significant technical, ethical, and regulatory challenges remain, the integration of agentic AI into dermatologic practice holds promise for a more proactive, personalized, and scalable model of care. Future research should focus on robust validation studies, bias lowering strategies, and the development of regulatory frameworks customized to autonomous AI systems. The author has nothing to report. The author declares no conflicts of interest. Data sharing is not applicable to this article as no datasets were generated or analysed during the current study.