The AI in Health and Care Award is making more than £100 million available over 4 years to accelerate the most promising AI technologies for health and social care. We have currently given a total 80 awards across the following 4 phases:
Phase 1 projects
Phase 1 is intended to show the technical and clinical feasibility of the proposed concept, product or service. Awards are for a maximum of £150,000 over a 6 to 12 month period. If the phase 1 project is successful, companies can bid for phase 2, subject to budget availability.
Round one: phase 1 winners
Stewardship of Antimicrobials using Real-Time Artificial Intelligence (SamurAI)
University College London
The SamurAI system will use AI to combine historical data for patients prescribed with antibiotics with the findings of specialists in infection as they review prescriptions. The system will learn when to stop or change the use of antibiotics to ensure they are only used when really necessary.
Deep learning for effective triaging of skin disease in the NHS
University of Dundee
This project is developing an AI (deep learning) system to distinguish common benign skin lesions from common skin cancers with state-of-the-art accuracy. This research will develop the system with representative image data from NHS clinics.
A fully automated ultrasound tool to screen for fetal growth restriction (FGR) in the first trimester
University of Oxford
This project will further develop fully automated ultrasound tools (the OxNNet toolkit) that can provide reliable measurements of placental size and shape in the first trimester, as well as estimating the blood flow within it. This can form the basis of a population-based screening test for fetal growth restriction (FGR). By identifying women at high risk of FGR early, we can increase monitoring and deliver the baby before it is stillborn and, in the future, test new treatments that could help prevent FGR from developing.
Personalised Preoperative (Neoadjuvant) Chemotherapy (NACT) to optimize curative treatment in breast cancer
University of Nottingham
This project will identify features from MRI scans in breast cancer patients, in combination with routine clinical data and advanced computational modelling, to predict the response to NACT. This will help clinicians to make better decisions for each individual patient and minimise unnecessary treatment.
OCTAHEDRON : Optical Coherence Tomography Automated Heuristics for Early Diagnosis via Retina in Ophthalmology and Neurology
The OCTAHEDRON project aims to use machine learning to detect early signs of neurodegenerative disease - such as Parkinson’s - in OCT scans of the retina. NHS doctors’ input and analysis of thousands of OCT scans will teach the computer system how to recognise changes in the retina that could help to detect neurological disorders sooner when treatment can be most effective.
Improving diagnostic yields of the Faecal Immunochemical Test using artificial intelligence and machine learning
Advanced Expert Systems Limited
This project will develop a computer model to identify potential cases of bowel cancer or polyps using results from FIT and other patient data, to enhance the NHS bowel cancer screening programme. The aim is for this system to help identify high-risk patients who can be prioritised for colonoscopy, reducing unnecessary, costly procedures.
Development of AI techniques to predict eye cancer using big longitudinal data
University of Liverpool
This project will further develop a novel, fully-automatic AI-powered diagnostic tool to support the accurate diagnosis and monitoring of choroidal naevi (patches of pigment at the back of the eye) and to predict the risk of ocular melanoma, the major form of eye cancer. The aim is to help to streamline the management of patients and reduce cost for the NHS by assessing and monitoring in the community for low-risk lesions, and follow up conditions with high risk factors in secondary care.
An artificial intelligence macular disease treatment decision tool for patients with wet age-related macular degeneration, diabetic macular oedema, and retinal vein occlusion
This project will complete development of computer software that will be able to tell whether the patient’s eye is stable or not without them needing to see eye doctors for regular check-ups. The software will allow more patients to be seen and treated, by creating efficiency and capacity for busy eye clinics
Project Rhapsody: Investigating the clinical feasibility of using AI-based deep audio and language processing techniques to diagnose neurological and psychiatric diseases
This AI technology offers a novel way to analyse complex speech or language patterns from free speech to detect common neurological and psychiatric diseases. A key differentiator is the use of proprietary speech representation methods to build generalisability and robustness into the tool–this research will help establish the clinical feasibility of this first-of-its-kind modality.
AI-enabled point-of-care technology for radiotherapy planning peer review
Mirada Medical Ltd
This project will determine the clinical and technical feasibility of using AI for review of RT treatment plans for cancer patients. This will be tested using anonymised clinical images to evaluate how effective the approach is, with the ultimate aim of conducting effective, faster checks to improve treatment.
Prognosis of epilepsy using at-home EEG monitoring
This project will develop a smartphone-based app that can receive and segment EEG recordings from wireless headsets to assist with assessing how well epilepsy treatment is working. The project will deliver a prototype device and a roadmap for product development.
Autonomous cardiac MR acquisition
Barts Health NHS Trust
This project aims to use AI to fully automate cardiac MRI scans. Autoscanning and autoanalysis will add precision and help to predict clinical outcomes better than current care, as well as speeding up scanning, reducing waiting times, saving money and freeing up scarce resources.
Senti: Wearable technology to enable remote precision and predictive medicine for respiratory patients
Senti Tech Limited
This project is developing a device which enables remote chest examination for respiratory patients through sensors embedded into a jacket. The research will use AI to develop ways to predict deterioration in patients with long-term respiratory conditions, personalise treatment and enable patients to make more informed healthcare choices.
An artificial intelligence algorithm for diagnosing attention deficit hyperactivity disorder (ADHD) in adults
University of Huddersfield
This project will develop a first-of-its-kind AI solution for diagnosing ADHD in adults. This will shorten the time people will need to wait for a diagnosis because a broader range of health professionals will be able to complete the diagnostic assessments quicker. The AI will use clinical data to guide health professionals about who requires extra assessment and who doesn't.
Woubot: An AI predictive system to produce personalised care recommendations for chronic lower limb wounds
Nine Health Global Ltd
This project will create a suite of automated software tools for community and wound clinics, with a user-friendly mobile application designed by doctors and nurses for their own use within the NHS. The app will generate a personalised care pathway for each patient, and use image and other automated software to monitor progress and outcomes.
Round two: phase 1 winners
Diagnosis of ‘glue ear’ with AI
Cardiff Metropolitan University
This project aims to test the use of AI to accurately diagnose ‘glue ear’ (otitis media with effusion) in children, preventing delayed or incorrect diagnoses, and reducing complications and recurrent issues.
Imperial College London
R-CANCER will improve the quality of decisions made by doctors when deciding how best to detect and diagnose cancer, by intelligently collating, analysing and interpreting new data on cancer from academic and open data sources.
Predicting pre-term labour
This project will explore the use of electrohysterography sensing to predict the pre-term labour of women giving birth before 37 weeks, using AI to provide more accurate data than is currently available.
Detecting coronary artery calcification in CT scans
Golden Jubilee National Hospital and University of Glasgow
Many CT scans include the heart, even if it is not the primary focus of the image. This project aims to train AI in the detection of coronary artery calcification, so that early care and treatment can be provided in advance of the patient reporting heart problems.
Decision-making for less-than-perfect kidney transplant matches
University of Oxford
Deceased kidney donors often have pre-existing medical problems that can affect the outcome for recipients after transplant. The decision whether to accept a kidney offer, or wait for one which is potentially better, can be challenging for both surgeon and patient. This project aims to train AI to predict the outcome for a patient, to aid in decision-making.
CyberLiver proposes using AI to examine the factors that influence development of new liver-related complications and clinical outcomes in patients with advanced cirrhosis, in the hope of developing a predictive tool to guide which patients will benefit from early hospital intervention versus continuation of care at home.
Measuring hip dysplasia in children with cerebral palsy
University of Manchester
This project seeks to use machine learning to assess X-ray images of the hips of children with cerebral palsy, to determine whether they are at risk of hip dislocation, a process which can be time-consuming when carried out by clinicians.
PREVAIL - PhototheRapy Enhanced Via Artificially Intelligent Lasers
University of Southampton
The PREVAIL project is developing automated techniques for the treatment of psoriasis via targeted delivery of laser light. This could reduce the risk of skin cancer in adjacent skin caused by current treatments with UVB where the unaffected as well as affected skin has often been exposed to UVB rays.
King’s College London
This project plans to combine the existing PIERS (Pre-eclampsia Integrated Estimate of Risk Score) tools, miniPIERS and fullPIERS, together with AI, into an app to calculate an individual woman’s risk of the complications of pre-eclampsia, including following birth.
Developing the Blood Pressure Index for improved blood pressure control
Imperial College London
Developing the Blood Pressure Index will provide the public with more useful and AI-driven blood pressure data for self-monitoring, in order to reduce high blood pressure, which is the leading cause of strokes, heart disease and deaths.
Pathpoint Detect is a novel, transparent decision support tool for image-based diagnosis in dermatology. This can be integrated directly into the Pathpoint suite of care pathway management and clinical workflow solutions.
Monitoring slow-growing brain tumours
University of Cambridge
Certain types of brain tumour are deemed low-risk, as they grow so slowly. This project aims to develop AI to measure the volume of tumours from scans, and learn which are at risk of growth, to ensure those patients are monitored more frequently, and others can be reassured that their tumour is lower risk.
Machine learning to improve the diagnosis of heart attacks
University of Edinburgh
This project is developing an AI-guided tool to help doctors and nurses interpret a patient’s troponin levels to diagnose heart attacks more accurately. A web app can be used on a mobile device at the bedside or embedded into hospital computer systems.
Issues and themes analysis in complaints
This project aims to use AI and Natural Language Processing to improve the speed, responsiveness and learning from the management of healthcare complaints, picking up key issues in individual cases, and recurring patterns across a service or area.
Phase 2 projects
Phase 2 is intended to develop and evaluate prototypes of demonstration units and generate early clinical safety or efficacy data. Award amounts are uncapped, funding awards are per product, typically for 12 to 36 months. If the phase 2 project is successful, companies can bid for phase 3, subject to budget availability.
Round one: phase 2 winners
Odin Vision Limited
Doctors miss up to 25% of cancerous or pre-cancerous polyps during colonoscopy procedures. Odin Vision’s award winning AI technology assists doctors to detect and characterise polyps. Better early detection and instant diagnosis have the potential to improve patient outcomes, reduce costs and improve the patient experience. The FORE-AI project will evaluate this AI technology across multiple hospitals to analyse the benefits for patients and the potential cost savings.
Clinical validation of the AI Clinician decision support system for sepsis treatment
Imperial College London
This project will test a method to automatically and continuously recommend to clinicians the correct dose of medications for treating sepsis in individual patients, personalising treatment and potentially improving survival.
Developing Lifelight: A contactless vital signs monitor for CVD screening
Lifelight is software technology that completely contactlessly measures blood pressure, heart rate, respiratory rate and oxygen levels in the blood using the camera on any smartphone. This project will collect data to allow Lifelight to measure blood pressure more accurately so it can diagnose high blood pressure more effectively.
Digitally adapted, hyper-local real-time bed forecasting to manage flow for NHS wards
University College London
This project aims to improve a model that predicts future demand for hospital beds, allowing local teams to adjust staffing levels or reschedule operations in line with future demand. The model will be tested with clinical and operational teams to make sure it is reliable, easy to use and safe.
Interactively trained ‘human-in-the-loop’ deep learning approach to improve cardiac CT and MRI assessment for accurate therapy response and mortality prediction
University of Sheffield
This project will develop an interactive deep learning method to measure heart health in large groups of patients, using MRI or CT scans. Existing detection algorithms will be used on these scans and the data will then be edited by experienced consultants to improve the measurements. Ultimately this could provide better predictions of responses to treatment and survival in patients with heart disease.
Artificial Intelligence to improve cardiometabolic risk evaluation using CT (ACRE-CT)
Caristo Diagnostics Limited
Caristo Diagnostics’ FatHealth technology is using standard CT scans combined with AI techniques to detect fat tissue inflammation, which can indicate a higher risk of developing diabetes or dying from heart disease. The project will analyse 20,000 CT scans to train the AI algorithm and help develop accurate risk predictions.
Dem Dx triage support platform for ophthalmology referrals
Dem Dx Limited
This project will develop and test a new technology to gather and process information about patients’ eye symptoms, to help healthcare staff make accurate and safe triage decisions and deal with the most common eye problems. This could help free up specialist time for urgent and complicated cases that need faster treatment.
BioEP: From prototype to clinical evaluation
BioEP is a computer biomarker of epilepsy that is designed to augment electroencephalograms (EEGs) to address delays in diagnosis of the condition. The project will further develop a prototype of a diagnostic decision support tool that can provide a risk score showing how easy it is for seizures to occur.
SMARTT critical care pathways (Safe, Machine Assisted, Real Time Transfer): an artificial intelligence based decision support tool to enable safer and more timely critical care transfer
University Hospitals Bristol and Weston NHS Foundation Trust
This research is developing an AI tool that will help to decide which patients are well enough to leave intensive care, helping to free up bed spaces and provide better care. The tool uses data from monitors, ventilators and blood tests and will help clinicians make more accurate and timely decisions. It will be tested in a real intensive care unit as part of this project.
Prediction and prevention of asthma attacks in children
Albus Health have developed a small table-top device that can automatically monitor a range of symptoms and metrics without patients having to do or wear anything, helping to predict preventable asthma attacks in children. Alongside Birmingham Children's Hospital, Imperial College London, Asthma UK and Oxford AHSN, this project will test the system within existing NHS infrastructure to generate real world evidence of clinical benefit and economic value.
Autonomous telemedicine - cataract surgery follow-up at two NHS trusts
The project is using Ufonia's natural-language AI assistant delivered via a normal telephone call, to follow up with patients after cataract surgery at two large NHS hospitals. This study will evaluate Ufonia's clinical decisions with those of expert clinicians and assess how acceptable the system is for patients and clinicians.
Natural language processing for real-time data capture in electronic health records to improve clinical care and operational efficiency
University College London Hospitals NHS Foundation Trust
This project is developing a natural language processing system to support the conversion of clinician’s text in electronic health care records into a structured format that can be processed by computers to help support clinical decision making, planning and research. The system works at the point of care, during data entry, and clinicians are given the opportunity to validate the suggestions before they are added to the patient record. The system will be tested in a simulation environment and then tested at University College London Hospitals and Great Ormond Street Hospital.
Round two: phase 2 winners
Eye2Gene is exploring the use of AI to determine which genetic condition is causing a patient’s inherited retinal disease, by examining eye scans. With more than 300 possible genetic causes, requiring differing management or treatment options, swift diagnosis is crucial.
The CESCAIL project is testing how effective AI can be in performing a preliminary analysis on the hours of images taken during capsule endoscopy, saving clinicians up to 80% of the time they would usually spend on this work. The project will allow this more flexible type of endoscopy to be rolled out further in the community.
University of Oxford
A machine learning-based clinical decision support for ‘digital triage’ in secondary mental health care. CHRONOS will be able to review a patient’s electronic medical record alongside their referral documents, to deliver a summary of relevant clinical information and suggest a suitable treatment team. This will assist secondary mental healthcare teams to identify appropriate care in a timely, safe and transparent way.
The First PLUS project, led by University of Oxford, Perspectum Group and The Fetal Medicine Foundation, uses AI to analyse the size of the placenta during the first trimester as a predictor for Fetal Growth Restriction, a risk factor for stillbirth and other neonatal conditions.
A centralised, AI-based solution for faster and more accurate testing on cancer biopsy tissue for colorectal, lung and other cancers.
Patients with Chronic Obstructive Pulmonary Disease (COPD) are being supported to use home monitoring of various health markers, and report them using the MyCOPD app. The data are analysed by AI to predict ‘exacerbation events’, where a patient’s condition suddenly declines, in order to prevent or lessen these events.
Advance notice of deterioration in cystic fibrosis
This project is using AI with home monitoring to predict sudden dips in the health of adults with cystic fibrosis, enabling early intervention and supporting patients to stay well without repeated hospital check-ups.
AI Systems for precision blood group matching
University of Cambridge
This project will develop AI-systems for genetic blood group typing, automated stocking of blood of different types, and precision matching of patients to blood units. The new AI systems aim to transform the quality and efficiency of blood matching, reduce complications of blood transfusions, and improve clinical care for patients.
MyWay Digital health is testing an AI tool for predicting diabetes complications, subtype diagnosis and treatment choices, to support clinicians including non-specialist GPs with managing their diabetes patients. The aim is to prevent complications, like heart attacks and foot ulcers.
Phase 3 projects
Phase 3 is intended to support first real-world testing in health and social care settings to develop evidence of efficacy and preliminary proof of effectiveness, including evidence for routes to implementation to enable more rapid adoption. Awards are uncapped, funding awards are per product, typically for 12 to 24 months. If the phase 3 project is successful, companies can bid for phase 4, subject to budget availability.
Round one: phase 3 winners
A study to assess the clinical and cost-effectiveness of the Ibex Medical Analytics - AI System: Histology system in diagnosing clinically important prostate cancer in prostate biopsy tissue
Imperial College London
Investigating the accuracy of a new AI tool (Ibex Medical Analytics - AI system) in detecting cancer and other clinically important features in prostate biopsy slides from 600 men across 6 NHS hospitals, and comparing with assessment from trained pathologists.
Real world testing of PreSize Neurovascular: medical device software to optimise stenting surgeries to reduce complications
Testing medical device software that can help doctors plan for high-risk brain surgeries by choosing the best stent for each patient - finding out how the software works across five hospitals, with the aim of improving the standard of care while minimising cost to the healthcare system.
EchoGo Pro: NHS impact of automating coronary artery disease risk prediction in stress echocardiogram clinics
EchoGo Pro uses AI to analyse stress echocardiograms to help more accurately diagnose heart problems such as blood vessel blockages. This project will assess how the device will benefit the NHS and patients in 12 hospitals and compares results with patients assessed normally by doctors, as well as seeing if the device can save money.
Point-of-care heart failure diagnosis for GP use: Implementation and evaluation of a simple AI-tool into the folio of care pathways
Imperial’s Connected Care national GP network
Testing an AI tool to help GPs diagnose heart failure - the Eko DUO device is a ‘smart’ stethoscope that records an ELECTROcardiogram as well as heart sounds, and is used like a standard stethoscope. It can provide an immediate diagnosis of heart failure using an AI algorithm. This will be evaluated in 500 patients in GPs and secondary care, comparing results to current NHS heart failure care pathways.
Evaluation of the DEONTICS AI platform for personalised, evidence-based treatment planning in multidisciplinary cancer care: Increasing compliance with national standards of care and streamlining MDTs in prostate cancer
Guy’s and St Thomas’ NHS Foundation Trust
The DEONTICS AI platform is designed to increase the efficiency and effectiveness of multidisciplinary team meetings that make decisions on cancer care for individual patients. This study will evaluate how the platform works to triage less complex patients straight to the treating clinicians, whilst also supporting decisions on care for prostate cancer patients with more complex needs.
Round two: phase 3 winners
Real-world testing of an AI app as an early intervention and support tool for mental health, to be used by patients on the waiting list for regular care. The aim is to reduce symptoms of anxiety and depression, and detect people experiencing severe mental health difficulties, so that they can be prioritised for treatment.
Lenus chronic obstructive pulmonary disease (COPD) Management Service
A digital healthcare service that shifts the management of COPD patients to a proactive and preventative care model. It uses AI to analyse output from patients’ daily monitoring and wearable devices to predict deterioration, and enable targeted intervention by care teams for those patients at most risk.
Optellum’s AI decision support helps doctors make optimal decisions for patients with potentially cancerous lung lesions found in CT scans. The aim is to reduce the time to cancer treatment, increase survival rates, and reduce unnecessary invasive procedures.
Open-source AI to augment and accelerate radiotherapy workflows
Cambridge University Hospitals NHS Trust and University Hospitals Birmingham NHS Trust are leveraging open source AI tools from Microsoft Project InnerEye to differentiate between tumour and healthy tissue on cancer scans (called ‘segmenting’), prior to radiotherapy treatment. The aim is to evaluate how this could save clinicians’ time, with the potential to reduce the time between the scan and starting treatment.
Analysing breast screening X-rays
Evaluating the potential for the use of AI for analysing X-ray images of routine mammograms (breast screening). This will improve accuracy, safety, cost-effectiveness and patient experience, giving results faster, and helping mitigate the shortage of radiographers available to analyse mammograms.
Workforce deployment solutions
Using AI to implement workforce solutions, ensuring that both logistics and clinical support teams are in the right place at the right time within a hospital, to maximise efficiency. Built on Oxford University-originated and infrastructure-free indoor location AI technology that simply uses smartphones to sensitively automate the deployment of teams.
Testing the use of AI to interpret and evaluate the spirometry test used to determine lung function, freeing up clinician time, and reducing incorrect diagnoses. Part of the NHS’s Long Term Plan to combat lung disease, and reduce health inequality.
Evaluation of the use of AI to support emergency department clinicians to analyse CT scans in patients with head injuries, leading to faster treatments and better outcomes for the patients. This can be vital in areas where there is a shortage of trained radiologists to analyse the scan images immediately, particularly during after-hours.
Cogstack Natural Language Processing
This AI-based clinical coding of medical records aims to enable more efficient analysis, remove errors, free up staff time, and improve research. Recruitment for clinical trials will be improved, and individual clinicians will be able to analyse patient records more efficiently.
Using AI to detect the invisible signatures of inflammation in the heart as shown in regular CT scans. This gives a better prediction of the risk of cardiovascular disease, allowing more efficient targeting of medication and treatment.
Phase 4 projects
Phase 4 is intended to identify medium stage AI technologies that have market authorisation but insufficient evidence to merit large-scale commissioning or deployment. Award amounts are uncapped, awards are per technology. The delivery team will work with NHS sites to support their adoption of these technologies, to stress test and evaluate the AI technology within routine clinical or operational pathways to determine efficacy or accuracy, and clinical and economic impact.
Round one: phase 4 winners
An AI platform to optimise oncology pathways, which can be integrated into existing software systems. Veye Chest, the first clinical application, is unique in its ability to currently automate early lung cancer detection, and soon also support treatment response assessment.
A set of tools that uses AI methods to interpret acute stroke brain scans, and helps doctors make the right choices about treatment and the need for specialist transfer of patients with confidence. It also provides a platform for doctors to share information between hospitals in real-time avoiding the delays that can occur.
RITA: Referral Intelligence and Triage Automation
An AI solution to automate the triage of GP referrals – assessing the urgency and next step for the referral and sending through directly to the next step in the process. In addition the solution includes a virtual assistant that supports clinicians in writing letters back to GPs, significantly speeding up this process.
Smartphone albuminuria self-testing
Using a home test kit and mobile app, Healthy.io’s solution empowers patients to self-test at home with clinical grade results. Fully integrated to the Electronic Medical Record (EMR), real-time results are available for clinician review and follow-up. Shifting testing to the home increases uptake, improves quality, reduces workload in primary care, and creates savings.
DrDoctor uses AI to get the greatest use from every scheduled appointment within a hospital. It ensures attendance is as high as possible by using past appointment attendance and demographic data to predict those less likely to attend in the future and customising communication with these demographics accordingly.
A complete and clinically proven ambulatory ECG monitoring service, utilising powerful AI-led processing and analysis to support clinical workflows and improve the diagnostic yield and timeliness of cardiac monitoring.
Mia Mammography Intelligent Assessment
Deep learning software that has been developed to solve critical challenges in the NHS Breast Screening Programme (NHSBSP), including reducing missed cancers, tackling the escalating shortage of radiologists and improving delays that put women's lives at risk.
DLCExpert uses artificial intelligence software to automate the time-consuming and skill-intensive task of outlining (or “contouring”) healthy organs on medical images for radiotherapy planning so that they are not irradiated during treatment.
Automated diabetic retinal image analysis software
OptosAI uses a machine learning algorithm to analyse images of the back of the eye for the presence or severity of any diabetic retinopathy, and then advises if referral to an eye care specialist is needed (based on the local clinical pathway).
A fully automated and scalable application for quantification and interpretation of stress echocardiograms that autonomously processes “real world” echocardiographic image studies to predict prognostically significant cardiac disease.
Round two: phase 4 winners
Designing innovative pathways, leveraging AI in the analysis of images of skin lesions, distinguishing between cancerous, pre-cancerous and benign lesions. DERM sets out to highlight the most likely cancers, and aid in swift and appropriate treatment being offered, reducing backlogs in this service and reducing premature deaths.
Using AI to intelligently triage and automate GP e-consultation requests, reducing staff time to manage the system. eHub aims to improve clinician efficiency, and allows easier interface for GPs and admin staff with eConsult software, reducing errors and improving patient safety.
Chest X-ray analysis
Real-world testing of an AI algorithm to fast-track the diagnosis of suspected lung cancer patients, offering them same-day CT scans. Patients whose chest X-rays show no abnormalities will receive a diagnostic report in seconds, reducing the radiology department's workload.
Paige prostate cancer detection tool
Using AI-based diagnostic software to support the interpretation of pathology sample images, in order to more efficiently detect, grade and quantify cancer in prostate biopsies. This helps address a rise in caseload while there are too few qualified pathologists, which has led to resource shortages in the NHS
Bone Health Solutions
A multi-centre project using AI to analyse any CT scan to catch undiagnosed spinal fractures, which can be a marker for osteoporosis. Patients will be directed to fracture prevention programmes, where they will receive lifestyle advice, and medications where appropriate, to reduce future fracture risks associated with the disease.
Further information and calls for applications to future rounds can be found on the AAC website.