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My-VIP: AI Generated Virtual Patient

Project No.: 

  1. AIM-B01-2025A

  2. AIM-B01-2025B

  3. AIM-B01-2025C

  4. AIM-B01-2025D   


Project Leader:     

  1. Professor Dato’ Dr Yang Faridah

  2. Associate Professor Dr Lee Yew Kong

  3. Associate Professor Dr Anand A/L Sanmugam

  4. Dr Saw Shier Nee


Project Collaborator(s):

  1. Professor Dr Lee Ping YeinDr Liong Siok Fuang

  2. Professor Dr Shireen Anne Nah

  3. Dr Ong Sim Ying

  4. Dr Hoo Wai Lam


Graduate Research Assistant(s):

Saman Sarker Joy


Research Assistant(s):       

  1. Mardiana binti Mardan

  2. Nur Hidayah binti Nor Isamuddin

​

Professor Dato’ Dr Yang Faridah

Department of Biomedical Imaging

Faculty of Medicine

Associate Professor

Dr Lee Yew Kong

Department of Primary Care Medicine

Faculty of Medicine

Associate Professor

Dr Anand A/L Sanmugam

Department of Surgery

Faculty of Medicine

Dr Saw Shier Nee

Department of Artificial Intelligence

Faculty of Computer Science and Information Technology

Clinical exposure during medical training is inherently variable, often shaped by chance, timing, and rotation structure. This can lead to uneven learning experiences, gaps in clinical reasoning, and heightened anxiety, particularly during transitions between clinical postings.

 

The UMHealth Virtual Patient (MyViP@UM) platform was developed as a structured, complementary learning environment to address these challenges. It provides students with a safe, consistent space to practise clinical reasoning, build confidence, and consolidate clerking skills, regardless of case-mix variability or rotation timing. The platform also serves as an accessible revision tool when students are rotating through other specialties, supporting continuity in clinical learning.

 

MyViP@UM is built around a faculty-curated, AI-powered dual-avatar system.

The Virtual Patient avatar enables voice-based clinical interaction grounded in expert-developed illness scripts, allowing learners to practise history-taking and patient clerking within realistic, locally contextualised scenarios.

The Virtual Clinical Teacher avatar provides structured, case-specific feedback using predefined rubrics, guiding students through diagnostic reasoning, appropriate investigations, and management planning in a manner that closely mirrors bedside teaching and case-based discussions.

 

A key differentiator of MyViP@UM is its commitment to responsible AI use. Unlike unsupervised AI platforms, all cases are developed, reviewed, and validated by Universiti Malaya faculty, ensuring educational rigour, cultural relevance, and patient safety. The platform is web-based, scalable, and designed for seamless integration across undergraduate clinical training programmes.

 

Capacity Building and Talent Development

 

MyViP@UM also serves as a platform for interdisciplinary capacity building, engaging early-career clinicians, biomedical scientists, and computer scientists in educational design and delivery. Through mentorship and collaboration, the project contributes to the development of future clinician-educators and researchers, strengthening Universiti Malaya’s leadership in innovative, human-centred medical education.

© 2025 Universiti Malaya. All Rights Reserved.

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