10086170Large Language Models based algorithms for personalised cancer detectionClosedCollaborative R&DInnovate UKMore than a thousand patients are diagnosed with cancer daily in the UK - **1 in 2 people will get this diagnosis in their lifetime**. Many cancers being diagnosed at an advanced stage when treatments are less effective. The UK has poorer cancer survival rates compared to other high-income countries. The long-term plan aims to be diagnosing 75% of cancer at an early stage -- currently **less than half of all cancers diagnosed at this stage when treatment could be more effective and less costly**. Delayed diagnosis has a significant impact on survival. In bowel cancer as an example, it can reduce survival rates from 91% to 10%. **Primary care (general practices) play an important role in raising suspicion of a hidden cancer** and referring a patient for diagnostic testing as early as possible. To be able to make an appropriate referral, the clinician needs to be able to estimate the risk for each patient based on their specific symptoms. Once identified, patients require further confirmatory tests including blood tests, scans and biopsies. **Improving this individual risk assessment would improve both the pickup rate and the speed through this pathway meaning more patients are identified, fewer patients are unnecessarily tested and diagnosis is made as soon as possible.** Electronic Health Records store patients' health conditions, tests requested, results and referrals. Information about early signals of cancer can be found in structured variables often stored in tables (vital signs, lab results, measurements). However**, key information is more often in clinical notes**, which contain narrative text used by General Practitioners (GPs) to record patients' symptoms. Recent advancements in statistical techniques and machine learning have improved the ability of **machines to understand data stored as language**. These Large Language Models can "read" large volumes of text rapidly, potentially for every patient, every day, to look for signs which might suggest they have developed a disease like cancer. This grant will fund a health-tech company, as well as GP practices and experts in evaluating the clinical safety and impact of medical algorithms. This group will use machine learning techniques including Large Language Models to detect early signs of cancer months in advance by continuously scanning patient records and warning their doctors when concerning signs are identified. The resulting software and new medical approach is **expected deliver 60,000 cancer diagnoses earlier in the UK annually. The technology will then be expanded to other disease types.**