The conversation surrounding Generative AI continues unabated, but now the initial hype has somewhat worn off, our attention turns to the realistic long-term. That is, what it will mean for our day-to-day lives in every aspect. So, understandably, the role GenAI could play in healthcare is being carefully explored. In Edinburgh, the President of Canon Medical Research Europe Ltd (CMRE), Dr Ken Sutherland and his team are, of course, already deeply absorbed in the capabilities of Generative AI and actively examining and assessing its potential.
‘Potential’ is the key word here. Because when it comes to patient care, nothing can be left to chance. Generative AI has already created a step change in the way that many of us work. ChatGPT alone reports 100 million daily users and there is further evidence showing that almost 50% of healthcare professionals intend to adopt AI technologies in the future. Further, GenAI is being deployed into any number of task-related areas, such as code generation, product development and smart manufacturing. Because of this, Dr Alison O’Neil, a Principal Scientist in AI Research at Canon, sees the role of the AI Scientist changing significantly. “Up to now, supervised learning has been the predominant paradigm,” she says. Supervised learning in AI is where an algorithm is taught using expert-labelled examples (in the case of medicine, the experts are doctors). It learns from these examples and uses them as the basis for predictions or decisions when given new, similar inputs.
However, Generative AI models such as ChatGPT and GPT-4, are unsupervised, so do not use expert-labelled data. For some AI Scientists, this means that a huge chunk of the everyday work (“building from the ground up,” as Dr O’Neil calls it) of bringing an algorithm to life is no longer necessary, as the working model already exists. Instead, much work lies in interrogating the models, interacting with them and answering questions of safety and ethics. However, in the case of Dr O’Neil and her fellow AI Scientists, work will continue on supervised AI models, in order to have clear control of the data they use when developing medical imaging software.