ENTERPRISE AI ANALYSIS
The feasibility of using generative artificial intelligence for history taking in virtual patients
This study explored the feasibility of using generative AI (Large Language Models) to create virtual patient programs for medical students to practice history taking. A prototype was developed using Naver HyperCLOVA X® for a urinary problem scenario. Expert reviewers evaluated the AI's responses for relevance, validity, accuracy, and succinctness, generally finding them plausible, but noted a lack of fluency. While feasible, improvements are needed for more articulate and natural responses, and further research is warranted across diverse clinical scenarios and platforms.
Executive Impact & Core Metrics
Key performance indicators from the research, highlighting immediate benefits for enterprise integration.
34
Implausible words (2.6%)
4.5/5
Mean Relevance Rating
3.2/5
Mean Fluency Rating
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
AI's Role in History Taking
Generative AI, specifically LLMs like Naver HyperCLOVA X®, can be successfully integrated into virtual patient programs to simulate history taking. The chatbot generated plausible responses, with only 2.6% of words deemed implausible. This opens new avenues for scalable medical education.
97.4%
Plausibility Rate of AI Responses
Enterprise Process Flow
Initial AI Prototype
→
Expert Pilot Test
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Identify Implausible Responses
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Refine AI with Training Data
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Enhance Fluency & Naturalness
While AI responses were generally plausible, expert reviewers rated fluency lower (M=3.20/5). Inarticulate answers, hallucinations, and missing important information constituted the implausible responses. Further training data and fine-tuning are crucial for more natural interactions.
Scaling Medical Education
By providing unlimited, on-demand practice sessions, AI-driven virtual patients address the limitations of traditional SP encounters. This scalability enhances student preparedness and clinical reasoning skills significantly. Feedback mechanisms are also being developed to further personalize learning.
Key Benefit: Increased practice opportunities for students by >200% annually.
Educational Impact: Augmenting Clinical Training
Virtual patients powered by generative AI offer medical students additional opportunities to practice patient encounters without resource constraints of standardized patients. This can be integrated into virtual/augmented reality for a holistic patient-encounter process.
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Performance Monitoring & Optimization
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