People working in AI in Medicine (and indeed AI more generally) should be aware of the long history of previous work in this area. Our technology is much better in 2024, but real-world success is still challenging, as has been the case for the past 70 years (the first claims that models could be better than doctors were made in 1954).

I really liked a recent survey of gen AI in journalism, which looks at issues such as how journalists use/interact with LLMs, and what impact this has on journalists. Some unexpected (to me) findings, for example the most common ethical concern is that news organisations will use LLMs without human supervision.

A really important and interesting research challenge is how to effectively communicate complex information to patients. At Aberdeen we are working on this topic in several areas of medicine, and are looking for a research fellow to join us.

In 2019 I told students that neural language models produced texts which were fluent but could not be trusted content-wise. In 2024 I told them the same thing. My high-level message hasnt changed despite the huge improvements in tech, maybe this is a fundamental aspect of LLMs?

Communicating uncertainty to non-experts is very important but also very difficult. Problems in communicating risk probabilities are well known, but additional challenges arise in many real-world use cases, including communicating time-series of probabilities and explaining the impact of features which the model ignores.

There is a lot of justified concern about the risks of using LLMs in healthcare. But LLMs can also make medicine safer, if they are used to support doctors and help them make fewer medical errors.