Large Language Models (LLMs) and Multimodal AI as Clinical Copilots in Allergy Clinics: Opportunities, Pitfalls, and Regulatory Horizons

The integration of LLMs as well as multimodal artificial intelligence (AI) into allergy clinics signifies a paradigm shift in treatment personalization, diagnostic precision, and clinical workflow optimization. Even as these technologies exhibit remarkable potential for recognizing complex allergic phenotypes, heading severe reactions, and optimizing allergen immunotherapy, notable challenges persist concerning diagnostic accuracy, patient privacy, regulatory compliance, and algorithmic transparency. This review scrutinizes current clinical applications, implementation barriers, diagnostic performance metrics, and regulatory frameworks governing LLM and multimodal AI deployment in allergology. The review highlights that although AI copilots can significantly enhance allergists’ clinical reasoning as well as efficiency, their optimal benefits require multi-stakeholder collaboration on transparency, validation, equity, and governance.