International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Generative AI-Based Virtual Patient for Medical Training |
|---|---|
| ABSTRACT | Medical education requires continuous exposure to diverse clinical scenarios, yet traditional training methods often limit hands-on patient interaction and real-time diagnostic practice. This project presents a Generative AI-Based Virtual Patient System designed to simulate realistic patient–doctor interactions for medical training and clinical skill enhancement. The system leverages large language models, natural language processing, speech-to-text and text-to- speech technologies to enable dynamic conversational engagement between users and an AI-driven virtual patient. By integrating structured healthcare standards such as FHIR and EHR, the platform ensures clinically relevant responses and supports accurate medical reasoning. The web-based architecture provides accessibility, scalability, and secure interaction, allowing medical students and trainees to practice diagnosis, questioning, and decision-making in a risk-free environment. The proposed system demonstrates the potential of generative AI to transform medical education by improving learning efficiency, clinical confidence, and healthcare training outcomes. |
| AUTHOR | CHANDAN G R, MUTTANNA D M, RATHIPA V, RAVIKUMARA, ARCHANA K N Dept. of CSE, Jain Institute of Technology, Davanagere, Karnataka, India Assistant Professor, Dept. of CSE, Jain Institute of Technology, Davanagere, Karnataka, India |
| VOLUME | 177 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1312132 |
| pdf/132_Generative AI-Based Virtual Patient for Medical Training.pdf | |
| KEYWORDS | |
| References | [1] Large language models improve clinical decision making of medical students through patient simulation and structured feedback: a randomized controlled trial BMC Medical Education (2024). LLMs used for patient simulation + feedback show improved clinical decision- making. https://bmcmededuc.biomedcentral.com/articles/10.1186/s129 09-024-06399-7 [2] AIPatient: Simulating Patients with EHRs and LLM Powered Agentic Workflow (ArXiv preprint) (2024). An agentic simulated patient system using EHR data and LLMs. https://arxiv.org/abs/2409.18924 [3] MedSimAI: Simulation and Formative Feedback Generation to Enhance Deliberate Practice in Medical Education (ArXiv) (2025). AI powered patient simulations with feedback loops. https://arxiv.org/abs/2503.05793 [4] CLiVR: Conversational Learning System in Virtual Reality with AI-Powered Patients (ArXiv) (2025). Combines LLMs and immersive VR for clinical training. https://arxiv.org/abs/2510.19031 [5] Designing and Evaluating an AI-Driven Immersive Multidisciplinary Simulation (AIMS) (ArXiv) (2025). LLM integration for interprofessional education simulations. https://arxiv.org/abs/2510.08891 |
