Professor Sandeep Reddy brings exceptional expertise in implementing artificial intelligence in healthcare settings, with a particular strength in developing and evaluating AI-driven healthcare solutions. As the current Professor of Healthcare Management and Medical Informatics at the School of Public Health and the lead of the Health, Human Biology and MedTech Theme at the Centre of Data Science, both at the Queensland University of Technology, he has led numerous significant projects in AI applications and healthcare technology integration. His extensive experience includes serving on the WHO Digital Health Roster of Experts and the Focus Group on AI for Health, demonstrating his global influence in digital health implementation. Prof. Reddy’s research portfolio showcases a strong foundation in developing predictive models and implementing AI systems in healthcare, as evidenced by his recent work developing foundational model-based systems for clinical communication and medical education. His expertise in handling complex healthcare data and creating evaluation frameworks is demonstrated through multiple high-impact publications, including seminal works on AI implementation frameworks. As the author and editor of several authoritative books on AI in healthcare, he brings deep knowledge of both theoretical and practical aspects of AI implementation. His experience leading projects across diverse geographic and healthcare settings, combined with his work on clinical integration and governance frameworks for AI systems, positions him well to lead the development of AI systems.

Focus Area of Research: Translational AI in Healthcare The application of technology in Medicine is not new. Various forms of technology have been applied in Medicine for nearly 200 years, and specifically, Health Information Technology has been used since the 1960s. However, unlike other technologies, Artificial Intelligence and its promise of healthcare automation evoke different reactions and pose distinct implementation challenges. Of the thousands of AI applications announced so far, only a handful are used in routine clinical care, and none are cited as standards for use in professional clinical guidelines. Regulatory approval does not necessarily translate to routine clinical use. Does this mean AI has failed to cross the translational chasm? It is not, indeed, the approach has been exploratory rather than driven by implementation science. I focus my research on using evidence-based and novel approaches to enable the adoption and implementation of AI in healthcare.