I am often asked about my experience blogging, sometimes by people who are considering writing their own blog. In this “meta” blog, I summarise my thoughts and experiences about my blog.

I am very excited by recent positive evaluations of NLG apps developed by my students to encourage safer driving in UK and Nigeria. We see statistically significant reductions in unsafe driving incidents in both UK and Nigeria. This has real potential to help address a major worldwide problem!

People will make much better use of LLMs if they understand what the technology can and can not do. Unfortunately many people have little understanding of this; I make a few suggestions which perhaps could help a bit.

Nikolay Babakov has recently published several papers on Bayesian networks, including challenges in reusing BNs, ideas for explaining BNs (work with Jaime Sevilla), and using LLMs to help build BNs. I help to supervise Nikolai, and think BNs can potentially be a useful way to do reasoning with uncertainty which is configurable and explainable.

I think there is enormous potential in using AI personal health assistants to improve health, including things like helping patients manage chronic illness, live more healthily, make informed decisions, and communicate with clinicians. There are huge challenges (technical and non-technical), but if this could be done well, it could radically improve health and enable healthcare systems to cope with increasingly elderly populations.

I’ve written a new book on NLG which was published in late October 2024. It tries to present a broad perspective on NLG, including requirements, evaluation, and use cases as well as technology. Perspective is similar to my blog in many ways, and I hope blog readers find it useful.

AI has many promising applications in healthcare, but adoption of AI in healthcare is very slow. One message from a recent workshop I attended is that it would help if AI researchers had a better understanding of requirements of the health sector, including evaluation, challenges, and business cases.

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.