There has been much in the media recently around artificial intelligence (AI), both in terms of risks and opportunities. The potential benefits and pitfalls have been highlighted in recent medical grand rounds with presentations from Stephen Bacchi, Tilenka Thynne, and Josh Inglis, which are available to view online. Unsurprisingly, medical students seem ahead of the game, with multiple research contributions in this area. Some have undertaken AI-based projects whilst others continue to generate an array of research outputs in their MD Advanced Studies, well exemplified by two recent publications from Noel Dishnica and Jonathan James, supervised by Stephen Bacchi. MD Advanced Studies, led by George Barreto and involving many clinicians, is critical to the generation and delivery of exciting medical student research projects.
Academics are also quick off the mark when it comes to adopting AI technologies, with Zoe Adey-Wakeling and Lambert Schuwirth using chatGPT to generate multiple-choice questions for medical student assessment. How to manage assignment-based assessment or ‘homework’ with these rapidly developing innovations is challenging though.
As ever, it is difficult to predict the evolving roles of doctors and how medical student education will need to adapt in light of rapidly advancing technology, however with an innovative mindset, we are well-placed to adapt medical education delivery in parallel with these advances. Current clinical practice is not immune to the influence of AI either, with large-scale MRFF-funded randomised trials from A/Prof Sam Lehman, Kristina Lambrakis and Ehsan Khan investigating AI-driven clinical decision support and new AI-supported models of care for suspected cardiac chest pain being implemented in our state-wide health system, whilst A/Prof Niranjan Bidargaddi is utilising AI informed digital applications to predict deterioration in mental health, unlocking new opportunity for early intervention.
Prof Jonathan Gleadle, Professor of Medicine