Thursday, September 18, 2025 - Scientists announced on Wednesday, September 17, that they have developed an artificial intelligence model capable of predicting medical diagnoses years before they occur, using the same underlying technology that powers consumer chatbots like ChatGPT.
The new system, called Delphi-2M, can forecast “the rates of
more than 1,000 diseases” well into the future by analysing a patient’s medical
history, according to a paper published in the journal Nature by researchers
from institutions in Britain, Denmark, Germany, and Switzerland.
The model was trained using data from the UK Biobank, a
large-scale biomedical research project that contains detailed health and
genetic information on about half a million participants.
Delphi-2M is based on neural networks using transformer
architecture, the “T” in “ChatGPT”, which has been most prominently
used in language-based tasks, including generative chatbots. Researchers said
that deciphering medical records is not unlike learning the grammar of
language.
“Understanding a sequence of medical diagnoses is a bit like
learning the grammar in a text,” explained Moritz Gerstung, an AI expert at the
German Cancer Research Center. Delphi-2M, he said, “learns the patterns in
healthcare data, preceding diagnoses, in which combinations they occur and in
which succession, enabling very meaningful and health-relevant predictions.”
Charts presented by Gerstung showed that the AI could
identify individuals with a significantly higher or lower risk of experiencing
a heart attack than would typically be predicted based solely on age or other
conventional factors.
To verify the model’s accuracy, the team tested Delphi-2M
against health data from nearly two million people contained in Denmark’s
public health database. The results reinforced the system’s predictive
capabilities.
However, the researchers cautioned that Delphi-2M is not yet
ready for clinical use. “This is still a long way from improved healthcare,”
said Gerstung, emphasising that the datasets used so far from Britain and
Denmark are biased in terms of age, ethnicity, and health outcomes.
Peter Bannister, a health technology researcher and fellow
at Britain’s Institution of Engineering and Technology, also warned that the
limitations of the data need to be addressed. Still, he said the work
represents progress in harnessing AI for preventative medicine.
In the future, Gerstung suggested, systems like Delphi-2M
could guide patient monitoring and allow earlier interventions, effectively
advancing preventative care. On a broader scale, co-author Tom Fitzgerald of
the European Molecular Biology Laboratory said such tools could aid in
“optimisation of resources across a stretched healthcare system.”
Doctors in many countries already rely on computer tools to
predict disease risks, such as QRISK3, which helps UK general practitioners
estimate the likelihood of heart attack or stroke. But co-author Ewan Birney
said Delphi-2M is a significant leap forward because it “can do all diseases at
once and over a long time period.”
Gustavo Sudre, a professor at King’s College London
specialising in medical AI, described the work as “a significant step towards
scalable, interpretable and, most importantly, ethically responsible
predictive modelling.”
He noted that one of the major challenges in AI research is
explainability, as the internal decision-making processes of large AI models
often remain opaque even to their creators. The Delphi-2M project, he said,
shows promise in addressing that concern while opening new possibilities for
long-term healthcare innovation.
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