A new generative AI model predicts the risk and timing of over 1,000 diseases using large-scale health record data from the UK and Denmark.
Key Details
- 1The model was trained on anonymised data from 400,000 UK Biobank participants and validated on 1.9 million Danish registry patients.
- 2It leverages generative transformer techniques similar to those in large language models to process health event sequences.
- 3Excels at forecasting diseases with clear progression patterns (e.g., heart attacks, cancer); less effective for variable conditions.
- 4Produces probabilistic, population-level risk estimates rather than individual certainties; shorter-term forecasts are more reliable.
- 5Underrepresents childhood/adolescent events and some ethnic groups due to training data limitations.
- 6Not yet ready for clinical use, but helps study disease progression and simulate outcomes for research.
Why It Matters
This research demonstrates the growing potential of AI to model disease trajectories at scale, paving the way for more preventive, personalized medicine. While not clinic-ready, such approaches could support risk stratification, resource planning, and ultimately improve patient outcomes in radiology and beyond.
Source
EurekAlert
Related News
•EurekAlert
AI Decision Support Proves Helpful, Yet Contentious, in Emergency Medicine
Researchers found that AI-driven decision support improved correct decision rates among emergency care doctors, but physician acceptance of AI recommendations remains split.
•EurekAlert
Machine Learning and 3D Imaging Reveal Magma Dynamics Beneath Santorini
Researchers used machine learning and 3D imaging to map the cause of Santorini's 2025 seismic unrest, revealing dynamic magma dike activity.
•EurekAlert
Equity and Bias Concerns Raised for AI in Neurological Imaging
A new report urges equitable development and oversight of AI in neurological imaging to avoid worsening healthcare disparities.