From spotting damage to preventing it: AI at the start of labour
Our invited commentary argues for catching at-risk babies early enough to prevent injury, by combining several kinds of data.
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Multimodal health data
Linking images, text and records at population scale to support research and care.
A national library of brain scans linked to health records in Scotland
We describe how a huge set of Scottish head scans was prepared, linked to health records and made available for AI research.
Studying lung cancer in people who have never smoked
OLIVE is a UK study plan to learn what predicts, and what follows, lung cancer in people who have never smoked, using routine health records and language technology.
Teaching an AI to read brain scans over time and show its working
LoV3D reads a patient’s brain MRI scans over time, measures regions step by step, and only then gives a diagnosis, which cuts down on made-up conclusions.
Building multimodal AI that can actually be deployed
A perspective from many authors arguing that multimodal AI should be designed around real-world deployment constraints from the start, and reaching beyond images and text.
Showing AI not just where a radiologist looked, but in what order
Turning a radiologist's eye movements into a short video helped general-purpose vision-language models write better chest X-ray reports and diagnoses, in one case beating specialist medical models.
Teaching AI to read chest X-rays by following where radiologists look
Giving a multimodal model the places radiologists look and the regions they mark improves the reports it writes, without retraining it.
Letting a pathology AI learn new things without forgetting the old, and without keeping patient images
Generative latent replay plus attention-based regularisation reduced catastrophic forgetting in a histology image task without storing past examples.
Could blood pressure drugs change COVID-19 risk? A proof-of-concept with AI
A transformer-based model estimated what would have happened under different blood pressure treatments, using data from over 300,000 patients.
Spotting depression and anxiety early in people with type 2 diabetes
A deep learning model that reads both the structured and the free-text parts of regional health records to predict who with type 2 diabetes will develop depression or anxiety.
A chest X-ray assistant that learns from where the radiologist looks
Eye-fixation prompts improved a multimodal model for chest X-ray diagnosis, and fine-tuning with only 658 reports beat a model trained on 17,000.
What multimorbidity means for people arriving at the emergency department
In a study of 451,291 people, those with several long-term conditions were far more likely to die within 30 days, and the link was stronger in people under 65.
Letting AI see where the radiologist looks on a chest X-ray
Overlaying a radiologist’s eye-gaze heatmap on the X-ray improved the accuracy of vision-language models across several chest X-ray tasks.
Do medical vision-language AI models make things up? A benchmark
We built a benchmark of medical images with question-answer sets to test how often AI models hallucinate, and which prompting strategies help.
Can AI that sees X-rays catch mistakes in radiology reports?
We tested multimodal language models on spotting errors in chest X-ray reports by checking them against the radiograph.
Predicting depression and anxiety in diabetes with limited primary care data
We pre-trained a model on good-quality hospital records and fine-tuned it on primary care data, which predicted post-discharge depression and anxiety much better than baselines.
Spotting tiny lesions in eye scans for diabetic retinopathy
RMCA U-net is a neural network that outlines hard exudates in retinal photos more accurately than standard networks.
Teaching a spine-MRI model without hand-drawn outlines
Spine-GFlow segments vertebrae, discs and the spinal canal in lumbar MRI without any manual annotation, matching a fully supervised model (Dice 0.914 vs 0.916).
Following COVID-19 across 57 million people in England
Linking eight national NHS datasets, we defined ten COVID-19 stages and mapped how patients moved between them in the first two waves.
Did people with rare diseases face a higher risk of dying from COVID-19?
In 283 Genomics England participants, rare disease was linked to higher COVID-19 death, especially neurological and neurodevelopmental conditions, though numbers were small.
How physical illness adds to disability in people with severe mental illness
In nearly 14,000 people with severe mental illness, over 60% had several physical conditions, and each type of comorbidity was linked to higher disability.
Predicting who will deteriorate in hospital, even when it is rare
A two-part model learns what the first 24 hours of a patient looks like before a bad outcome and predicts mortality and ICU admission with PR-AUCs around 0.9.
Beyond mild or severe: finding hidden subtypes of COVID-19 outcomes
Two clustering methods using axes of prognosis revealed distinct subgroups of poor outcome, especially among respiratory patients, and predicted membership on admission.
The hidden toll of COVID-19 on people with heart disease
Hospital activity for cardiovascular disease fell by 60 to 100% during the pandemic, and we estimated tens of thousands of excess deaths in England, direct and indirect.
Finding the rare patterns in ICU data that signal trouble
A framework that spots rare, meaningful interactions between patient measurements in intensive care and uses them to predict outcomes such as sepsis, while explaining why.
Looking for the formal papers? Every story links to its paper. Browse publications →