KATHMANDU: Scientists said Wednesday that they had created an AI model able to predict medical diagnoses years in advance, building on the same technology behind consumer chatbots like ChatGPT.
Based on a patient’s case history, the Delphi-2M AI “predicts the rates of more than 1,000 diseases” years into the future, the team from British, Danish, German and Swiss institutions wrote in a paper published in the journal Nature.
Researchers trained the model on data from Britain’s UK Biobank — a large-scale biomedical research database with details on about half a million participants.
Neural networks based on so-called “transformer” architecture — the “T” in “ChatGPT” — most famously tackle language-based tasks, as in the chatbot and its many imitators and competitors.
But understanding a sequence of medical diagnoses is “a bit like learning the grammar in a text,” German Cancer Research Center AI expert Moritz Gerstung told journalists.
Delphi-2M “learns the patterns in healthcare data, preceding diagnoses, in which combinations they occur and in which succession”, he said, enabling “very meaningful and health-relevant predictions”.
Gerstung presented charts suggesting the AI could single out people at far higher or lower risk of suffering a heart attack than their age and other factors would predict.
The team verified Delphi-2M’s performance by testing it against data from almost two million people in Denmark’s public health database.
But Gerstung and fellow team members stressed that the Delphi-2M tool needed further testing and was not yet ready for clinical use.
“This is still a long way from improved healthcare as the authors acknowledge that both (British and Danish) datasets are biased in terms of age, ethnicity and current healthcare outcomes,” commented health technology researcher Peter Bannister, a fellow at Britain’s Institution of Engineering and Technology.
But in future systems like Delphi-2M could help “guide the monitoring and possibly earlier clinical interventions for effectively a preventative type of medicine”, Gerstung said.
On a larger scale, such tools could help with “optimisation of resources across a stretched healthcare system”, European Molecular Biology Laboratory co-author Tom Fitzgerald said.
Doctors in many countries already use computer tools to predict risk of disease, such as the QRISK3 programme that British family doctors use to assess the danger of heart attack or stroke.
Delphi-2M, by contrast, “can do all diseases at once and over a long time period”, said co-author Ewan Birney.
Gustavo Sudre, a King’s College London professor specialising in medical AI, commented that the research “looks to be a significant step towards scalable, interpretable and — most importantly — ethically responsible predictive modelling”.
“Interpretable” or “explainable” AI is one of the top research goals in the field, as the full inner workings of many large AI models currently remain mysterious even to their creators.
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