The use of large language model in patient education
Patient education is a crucial aspect of healthcare, but traditional methods often fail to account for individual differences in literacy and comprehension. Recent advances in natural language processing offer a solution.
This presentation will discuss the application of large language models to generate personalized patient education materials tailored to specific patient needs and literacy levels. Our research demonstrates significant improvements in patient understanding, engagement, and health outcomes.
Additionally, we explore the potential for these models to address health disparities by providing culturally sensitive and multilingual resources. This innovative approach has far-reaching implications for patient empowerment, health literacy, and healthcare outcomes.
Join us to explore the cutting-edge intersection of AI and patient education.
Yee Ang
Consultant at National Healthcare Group
Dr Ang Yee Gary obtained his MBBS from NUS in 2006, MPH, NUS in 2011 and completed his public health residency in 2011. He has diplomas in accounting & finance, family medicine, mental health, family practice (Dermatology), fund management and administration. He is currently a Nanyang Fellow MBA candidate with NTU. He received the Clinician Leadership in Research award in 2012, NHG CMTI Clinician Innovatory Preparatory Program in 2023 and has been a health services researcher since 2011.
Currently, he has an additional role in promoting workplace health. He has also been appointed as an academic editor for PLOS ONE in 2023. His recent projects are healthcare utilization management, chatbot for patient education in eczema, improving treatment adherence via artificial intelligence and behavioural science, addressing modifiable risk factors in the workplace.
