Charlene GOH Xing Le1, Eugene WANG1, Judy C. G. SNG2,*
1Yong Loo Lin School of Medicine (YLLSOM), NUS
2Department of Pharmacology, YLLSOM, NUS
Sng, J. C. G., Goh, C. X. L., & Wang, E. (2024). The usage of generative artificial intelligence (AI) avatars for psychiatric medical education [Lightning talk]. In Higher Education Conference in Singapore (HECS) 2024, 3 December, National University of Singapore. https://blog.nus.edu.sg/hecs/hecs2024-jcgsng-et-al/
SUB-THEME
Opportunities from Generative AI
KEYWORDS
Opportunities from Generative AI, video avatars, patient simulation, medical education
CATEGORY
Lightning Talk
BACKGROUND
The integration of generative artificial intelligence (AI) into our daily lives has significantly increased in recent years, with numerous healthcare institutions leveraging AI and medical technology to enhance diagnostic and treatment accuracy. However, its utilisation in medical education remains limited. This project aims to bridge this gap by promoting the incorporation of AI into medical education. This is done by supplementing existing resources with AI-generated videos of simulated patients. Currently, the range of available avatars online is limited and not entirely relevant to the context of Singapore. By providing virtual scenarios featuring patients with psychiatric medical conditions, we aim to develop essential skills in medical students, such as empathy and sensitivity, within a controlled environment. This approach seeks to enhance students’ clinical skills and decision-making, ensuring they are well-prepared for real-world patient interactions as practitioners.
METHODS
AI technology was employed at multiple stages of building the video avatars. Initially, a doctor- patient conversation is generated using ChatGPT-4, after inputting a prompt that corresponds to a specific psychiatric patient profile. The patient profile includes the positive and negative signs presented by the patient, their socio-economic background, past medical history, and the suggested treatment plan. Medical experts in the psychiatric field meticulously crafted the simulated patient’s information to ensure an accurate representation of the condition. Two main symptoms were chosen: low mood (including adjustment disorder, dysthymia, and major depressive disorder) and chest tightness (not amounting to chest pain, including adjustment disorder, panic disorder, and somatic symptom disorder). After fine-tuning the script to ensure the scenario was localised and culturally accurate to Singapore’s context, the D-iD software was utilised to generate an AI video of a patient, emulating sentiments and synchronising speech with facial expressions.
The video is then shown to students, whose objective is to accurately diagnose the patient. The patient avatar video aims to increase interactivity with students in a classroom setting, enhancing their diagnostic skills and understanding of psychiatric conditions.
RESULTS
Preliminary feedback from medical professionals suggests its great potential to become a staple resource for healthcare students. In the coming months, we plan to pilot this with a small group of healthcare students to gather feedback and suggestions to refine the ensemble of avatars we currently have. The data collection will include metrics such as diagnostic accuracy and effectiveness in improving the students’ knowledge. Further studies will be conducted to determine the usage and effectiveness in nurturing the holistic development of medical students, encompassing both academic and psychosocial skills.
CONCLUSION
As AI integrates more seamlessly into the medical curriculum, it is crucial to cultivate an engaging, interactive, and safe space for students, bridging the gap between theoretical knowledge and clinical practice. We believe that incorporating AI as early as possible in medical education is vital for developing holistic practitioners for a sustainable future, ultimately improving patient care in healthcare institutions.