Implementing Artificial Intelligence (AI) in healthcare presents significant challenges, primarily due to the risks associated with its deployment, safety, and acceptability. Current AI systems in healthcare often face issues such as data privacy concerns, bias in algorithms, and the potential for automation bias, where healthcare professionals may overly rely on AI recommendations, leading to errors. Additionally, many AI implementations are evaluated post-deployment, which can lead to unforeseen risks and inefficiencies in clinical settings, especially when it comes to workflow integration. The Validitron, based at the University of Melbourne, offers a solution to these challenges through its simulation lab and sandbox environment. The Validitron provides a flexible toolkit for pre-implementation simulation and evaluation of AI models, whether they are decision support tools or Generative AI applications. By simulating real-world clinical workflows, The Validitron allows for the assessment of AI efficacy and safety before actual deployment, thus mitigating risks associated with post-implementation evaluations.Such proactive evaluation not only ensures that AI tools are safe and effective but also facilitates smoother integration into healthcare systems. By addressing potential issues early, The Validitron helps prevent the costly and potentially harmful consequences of deploying inadequately tested AI models in healthcare environments.

This presentation will draw on the experiences and lessons learnt from current studies with academic, clinical, and commercial partners. Audiences are to expect tips on how to navigate the current clinical AI landscape and deciding whether a task is indeed AI appropriate.