Improving Clinical Communication Skills Through Immersive Roleplay Using AI-Based Standardized Patients: A Mini Review

Abstract

Effective communication in the clinical setting is the pillar of quality care, influencing diagnostic accuracy, patient satisfaction, and decision-making collaboration. Traditional teaching methods, predominantly with human standardized patients (SPs), have been used extensively to train for communication skills but are hampered by high cost and logistical burdens. With the advent of artificial intelligence (AI), specifically the intersection of large language models (LLMs), natural language processing (NLP), and emotional modelling, AI-based standardized patients (AI-SPs) have been created as an economically efficient and scalable solution. AI-SPs offer realistic, interactive clinical simulations that can be accessed on-demand and are not time or location dependent. This mini review elaborates the emerging roles of AI-SPs and the essential supporting technologies of LLMs and NLP-based models. Educational benefits such as immersive learning, variability of scenarios, personalization of learning, and incorporation of empathy, confidence, and clarity of communication are discussed. Assessment designs in the form of real-time feedback and behaviour analytics are described. Challenges of authenticity of interaction, algorithmic bias, and integration into medical school curricula are also tackled, as well as the need for culturally sensitive simulations and longitudinal outcomes studies. AI-SPs are expected to mature in the future through advances in multimodal AI and interprofessional simulation design. Overall, while still under development, AI-SPs hold tremendous promise as pedagogically effective and cost-effective tools to complement communication skills training in multiple healthcare education contexts.

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