Research
Generative AI-integrated Virtual Agent Applications and Simulations in Health Professions Education—A Systematic Literature Review.
Wang, X., O’Malley, A., Khalid, Sd., & Hughes, A. (2025). International Journal of Artificial Intelligence in Education (with editor).
Exploring the experiences and views of doctors working with Artificial Intelligence in English healthcare; a qualitative study
Ganapathi, S., and Duggal. S. (2023) , PLoS One, 18(3):e0282415. https://doi.org/10.1371/journal.pone.0282415
Enhancing diagnostic accuracy of ophthalmological conditions with complex prompts in GPT-4: comparative analysis of global and low- and middle-income country (LMIC)–specific pathologies
Enhancing undergraduate clinical communication teaching and learning through AI simulation.
Medical students’ and educators’ opinions of teleconsultation in practice and undergraduate education: a UK-based mixed-methods study.
Quality assurance and validity of AI-generated single best answer questions.
Solving educational capacity challenges with an AI-powered patient simulator
Assessing the quality of AI-authored exam questions.
Assessing the validity of AI-authored exam questions.
Ensuring appropriate representation in artificial intelligence – generated medical imagery: protocol for a methodological approach to address skin tone bias.
Investigating and combating gender bias in generative large language models.
Leveraging artificial intelligence in medical education: a study on AI-generated exam questions.
Skin tone bias in generative artificial intelligence for use in medical education.
Critical review of the uses of Technology Enhanced Learning (TEL) in distance undergraduate medical education.
Teleconsultation in health and social care professions education: a systematic review.
Teleconsultation training in undergraduate medical education: Students’ and educators’ opinions on and experiences with teleconsultation training.
Global experiences of teleconsultation training in undergraduate health care and social work education – a systematic review.
Using classic and technology-based methods to enhance the student learning experience in histology.

