Better answers, shakier reasoning in small medical AI
Small medical AI models trained to copy a larger model's reasoning get more answers right, but the steps they write out along the way contain more mistakes.
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AI reliability in medicine
Understanding what clinical AI knows, where it fails, and how to tell the difference.
Knowing when a rare-disease AI should hold back its answer
A diagnostic tool needs to know when to commit to its top guess and when to pass the case to a person, and for rare diseases that is hard to get right.
Does an AI memory really work better, or does it just look better on the page?
A fancier memory for AI seemed to beat a simple one, but most of the gain came from how the information was laid out, not from how the memory works.
When should an AI answer, look something up, ask for help or say it does not know?
A theory for choosing how clever the decision-maker in front of an AI model needs to be, based on how much data you have to set it up.
A fair arena for AI research assistants in biomedicine
BioMedArena is an open toolkit that makes it easy to build biomedical AI research agents and compare them fairly on the same footing.
Does a diabetes prediction model still work on new people? Testing it in prediabetes
We tested a machine learning model for predicting type 2 diabetes on 3,081 people with prediabetes, and checked whether it could guide who benefits most from treatment.
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.
A realistic test for AI that diagnoses rare diseases
RareArena is a large test built from real case reports, designed to show how well AI language models can spot rare diseases.
A checklist for reporting studies that use AI language models in radiology
Studies of AI language models in radiology are often reported in ways that make them hard to repeat or compare, so an international panel built a checklist.
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.
Can AI connect the dots in biomedicine? A test of multi-step reasoning
BioHopR tests whether AI models can chain several facts together to answer biomedical questions with more than one correct answer.
When AI writes fake patient records, whose bias does it copy?
Larger language models wrote more complete synthetic health records, but also showed stronger gender and racial bias.
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.
How do radiologists keep an eye on the AI they use?
Interviews with 16 radiologists show that checking AI tools in practice is mostly manual, slow and unsupported by clear guidelines.
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.
Prompting a language model to find and fix mistakes in clinical notes
Our entry to the MEDIQA-CORR 2024 shared task used step-by-step prompts to detect and correct medical errors, and placed in the top three for two of the three tasks.
Testing whether medical AI can explain its answers, not just choose them
MedExQA is a medical question benchmark with several reference explanations per answer, plus a small open model, MedPhi-2.
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.
More people with type 2 diabetes are prescribed antidepressants and antipsychotics
Between 2004 and 2021 in Scotland, antidepressant prescribing in people with type 2 diabetes rose from 20.0 to 33.3 per 100 person-years.
Can machine learning read a baby's heart-rate trace in labour?
A scoping review of 36 studies found promise for predicting fetal hypoxia from cardiotocography, but weak benchmarks and small, repeated datasets.
Predicting patient outcomes from the sequence of their diagnoses
A systematic review of deep learning studies that predict outcomes using sequences of diagnosis codes, and how well they generalise and explain themselves.
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.
Three ways to teach AI to curate a chemistry ontology, compared
For building a biomedical ontology, we compared in-context learning, fine-tuning and supervised learning on more than 1.8 million facts.
Four NLP tools, one brain-scan task: don't expect them to work out of the box
Comparing four off-the-shelf tools on brain radiology reports showed that performance varies widely, so tools need checking and adapting before clinical use.
Predicting which diseases come together, with AI that can explain itself
A systematic review of explainable AI for predicting comorbidity from health records, and what it offers clinicians.
Antidepressants, antipsychotics and diabetes: what does the evidence say?
A systematic review found little and mixed evidence on how these medicines relate to diabetes outcomes, so general diabetes guidance should apply for now.
When expert doctors disagree, which labels should an AI learn from?
Eleven intensive care consultants labelled the same patients differently, and the usual way of settling disagreements, a majority vote, gave worse AI models.
Do AI models predict heart disease in diabetes better than standard scores?
A systematic review found promising AI performance, but only one of the reviewed models had been externally validated and reporting quality was low.
A heart-risk score for people with type 2 diabetes, built in China and checked in Scotland
A five-year cardiovascular risk model built from routine Nanjing hospital data held up in a Scottish population (C-statistic 0.716 externally).
Measuring whether an AI model makes health inequalities worse
A generic framework quantifies inequality in data and AI models as the area between two curves, and found significant inequalities in real intensive care data and models.
Do liver disease prediction models miss more women?
Models built from a widely used liver disease dataset had a higher false-negative rate for women, which would mean more missed diagnoses.
Are language-technology studies on radiology reports described well enough to repeat?
Of 164 studies, few shared data or code or tested their work externally, so we called for reporting standards specific to clinical NLP.
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.
Explaining why an AI assigned a medical code
HLAN highlights the words and sentences behind each code an AI assigns to a discharge summary, and a label-embedding trick made most coding models more accurate.
Combining seven COVID-19 risk models so the best one is used for each patient
An ensemble that picks the most competent models for each patient beat every individual model across four cohorts in the UK and China.
Does the standard hospital warning score work for COVID-19?
The NEWS2 score was a weak predictor of severe COVID-19, but adding age and a few routine blood tests improved it across hospitals in the UK, Norway and China.
Predicting who will do badly in hospital with COVID-19, tested in two countries
A simple model using age, sex and routine blood tests worked well in Wuhan but less well in London, where patients were far sicker, showing why external validation matters.
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.
Not one sepsis but several: splitting patients by organ failure helps prediction
Grouping ICU patients by their pattern of organ dysfunction improved sepsis prediction across three machine-learning models, especially in specificity.
Reusing a language-processing model without starting from scratch
A phenotype-embedding approach shows where an existing model will work on new records and where it won't, saving around 80% of the effort of blind adaptation.
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