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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Language models for clinical text
Reasoning, summarisation and coding over health records, evaluated step by step.
Using language models to improve a traceable rare-disease diagnosis tool
Combining a language model with an ontology-based ranking tool improves rare-disease diagnosis while keeping the evidence trail.
Teaching computers to spot falls, frailty and delirium in hospital discharge letters
We built a hand-annotated collection of discharge summaries so that language models can learn to find geriatric syndromes in them.
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.
Can computers read the signs of frailty and falls in older adults’ records? A review
We reviewed how language technology has been used to find geriatric syndromes in the free text of health records, and what is still hard.
An AI proofreader for radiology reports
A three-stage method that detects, locates and corrects mistakes in radiology reports, using medical knowledge to help language models do it.
How do you judge an AI-written pathology report? HARE
HARE is a way to score generated histopathology reports by checking the key findings and how they relate, which matches pathologists better than standard metrics.
Teaching clinical knowledge to language models through how they split words
K-Tokeniser builds medical concepts such as drugs and diseases into the way a language model reads text, so it needs less training data.
Finding adverse events in the discharge letters of older patients
A hand-labelled collection of discharge summaries from older patients, covering 14 adverse events such as falls and delirium, shows where language models still struggle.
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.
Mining tumour test results from thousands of research abstracts with AI
IHC-LLMiner uses a language model to find and extract tumour staining profiles from PubMed abstracts, faster and more accurately than GPT-4o on this task.
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.
Helping clinical coders work faster with AI, without taking them out of the loop
CliniCoCo, a human-AI coding system, cut coding time by 40% and let professional coders reach an F1 score above 0.93.
Finding rare disease patients in clinical notes with a hybrid of dictionaries and language models
Combining dictionary-based language processing with large language models identified rare diseases better than either alone, and found patients missing from structured records.
A small language model learns to describe molecules after reading chemistry texts
Continued pretraining on chemistry text made a small language model far better at describing molecules, even though it never saw molecule notation in that stage.
Using retrieval to help language models correct radiology report errors
A three-stage method that detects, locates and corrects errors in radiology reports using internal and external knowledge retrieval.
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.
Teaching a language model radiology vocabulary to write better report summaries
RadBARTsum retrains a language model with medical-entity masking so it learns radiology knowledge, which improves summaries of radiology reports.
A unified view of how deep learning assigns medical codes
A survey that breaks automated medical coding models into four building blocks so the field can be understood and compared.
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.
Reading messy clinical text with a tool inspired by DNA sequence search
TBLAT borrows ideas from BLAST, the tool for searching DNA sequences, to match misspelt and abbreviated clinical terms to the right concepts.
Matching messy typed addresses to the right property
FLAP links free-text addresses to the UK's official property database with 99.2% adjusted accuracy and is released as open-source software.
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.
Summarising radiology reports: comparing language models in a shared challenge
Our entry to a BioNLP shared task compared generative language models on summarising radiology reports and used a two-stage fine-tuning approach.
Finding rare diseases in clinical notes without hand-labelled training data
By combining language technology with a disease ontology, we identified rare disease mentions in health records with over 90% accuracy, with no human annotation of the training data.
Finding traces of difficult childhoods in social media, with ontologies to guide the AI
An ontology-guided, self-supervised method for spotting adverse childhood experiences in social media posts, producing a public resource for further research.
Who is doing clinical language technology in the UK, and what is missing?
We mapped the UK landscape of clinical natural language processing by starting from the funded projects, and set out who is involved, what they build, and what holds the field back.
Automated clinical coding: what, why and where we are
A perspective on why coding clinical records is hard to automate, covering seven challenges and why knowledge-based methods should be combined with deep learning.
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.
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.
Derive insights from health data using knowledge graph technologies
How knowledge graph technologies help computers read free-text health records, told through real examples from an invited talk.
Why bother using free-text clinical notes for research or patient care?
Much of what hospitals record is free text. Here is why it is worth reading for research and patient care.
Telling types of stroke apart in UK Biobank by reading brain scan reports
Combining language processing with clinical rules on brain scan reports identified bleeding strokes in UK Biobank with high accuracy, improving on coded data alone.
Ten years of language technology for radiology reports, mapped
A systematic review of 164 studies shows rapid growth in NLP for radiology reports, but little sharing of data or code and little evidence of real clinical use.
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.
Physical illness and early death in people with schizophrenia
Two thirds of 9,775 people with schizophrenia-spectrum disorders had several physical conditions, and some combinations roughly doubled the risk of death.
Reading discharge letters to track anticoagulant use in atrial fibrillation
An open-source language-processing pipeline calculated stroke and bleeding risk scores from 10,030 discharge summaries and showed rising anticoagulant use over seven years.
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.
Hand-written rules or machine learning for reading brain scan reports?
On Scottish stroke imaging reports, a hand-crafted system was the most accurate, but machine learning was a feasible alternative when no one has time to write rules.
CogStack: bringing search and text mining to a hospital's records
CogStack, an open-source system deployed at King's College Hospital, has processed over 300 million lines of clinical data and is the only open-source, easily deployable option built for the UK.
SemEHR: turning weeks of hospital record searching into minutes
SemEHR is an open-source tool that finds what clinical notes say about patients, cutting recruitment-criteria checking from days to minutes.
Highlighting the key sentences in research papers to speed up curation
NapEasy automatically highlights the sentences a curator needs in Parkinson's and Alzheimer's papers; in about 65% of cases the highlights were enough without reading the full text.
Finding drug side effects hidden in psychiatric clinical notes
ADEPt, a rule-based pipeline built with clinicians and pharmacists, found 19 side effects of antipsychotics and antidepressants in free-text records, with an average F-measure of 0.83.
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