Following an AI leadership session at Yorkshire Housing in July 2024, there was engaging discussion about good uses of AI – “good” in terms of both ethics and appropriate (or impactful) use of AI. I’ve put together a few examples here, covering a range of methods and application areas, to show the art of the possible and hopefully inspire those closest to the work to consider their own possible “good” uses of AI.
Alphafold
This does a scientific problem, modelling the 3D structure of a protein. Each of these would be a PhD to find experimentally, and at the time of writing over 200 million proteins have been modelled. This includes things involving DNA, and has massive applications for medicine and health. An additional “nice” thing about this is that the earlier code and all data has been made open source, so scientists can validate and improve the system. This is a highly specialised machine learning system.
More info: https://alphafold.ebi.ac.uk/
AI assistant for customer support agents
Lots of companies provide this – in one business I’m working with a startup called Ducky at present. These are like plugins, in Ducky’s case in your browser, and based on the support query it surfaces relevant policy documents, and summarises an email thread. It can also draft a response for editing by the customer support agent. It saves the typing out of some boilerplate and we’ve found it really helps support agents get through things more easily (especially where an obscure policy is needed).
More info: https://www.ducky.ai/
AI alert for heart anomalies in hospitals
This AI is hooked into ECG’s monitoring heartbeats of people in hospital, and alerts clinicians if there is a high risk of dying. This led to a 31% reduction in deaths during clinical trials, more than would be expected with a new medicine, even though this is “just” a monitoring system.
Breast cancer detection
This one is about computer vision. The system picks out whether a mammogram is likely to be displaying early stage cancer. The particularly smart part here is the implementation: Mammograms are studied as usual by two radiologists, then put into the system, which flags up any ones that they think have been missed to a third human radiologist, who then decides whether to recall the woman. This keeps the human central to the process, whilst still gaining the benefits of early identification of breast cancer. No jobs are at risk, and the human still catches some cases where the AI would miss an identification.
More info: https://www.imperial.ac.uk/news/249573/new-ai-tool-detects-13-more/
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