10000560CanDetect: AI based early cancer detection using unstructured dataClosedCollaborative R&DInnovate UKUK cancer survival rates lag behind other developed countries(1), up to 10,000 excess deaths occur annually in comparison(2). Delayed diagnosis is thought to contribute to this(3). Currently nearly half of all cancers are diagnosed at a late stage(4). Approximately 20% of patients see their GP 3 or more times before their cancer diagnosis(7) with approximately 12% of avoidable diagnosis delay occurring in in this setting(6). To diagnose cancer, multiple features ranging from subtle findings to non-specific symptoms must be considered - these are recorded across different sections of the electronic health record. In addition, in up to 59% of records this data is 'hidden': doctors often write them in the free-text records(10) outside of rigid coding frameworks, where they cannot be identified by routine means. With increasing demand and complexity, primary care clinicians struggle in a resource-strained NHS: GPs have on average 9 minutes per patient(9). In this time, identifying this relevant data accurately and with speed has potential for error: indeed, often tell-tale patterns predictive of cancer are already present in the records long before a patient is finally diagnosed(8) There is a national drive towards earlier detection of cancer, ranging from new diagnostic centres, incentives for primary care and regional Cancer Alliances. Discussions with these stakeholders have shown us that there is a strong appetite and need for an intelligent method of supporting primary care cancer detection. Clinical decision support systems (CDSS) have been identified as a key tool for early diagnosis by major national cancer stakeholders in a Cancer Research UK 2020 report(11). Our aim is to develop a CDSS to diagnose cancer earlier, and reduce delayed or missed diagnoses, by using key information in health records. We will machine learning and natural language processing, AI methods which enable analysis of large, complex datasets. We will evaluate the CDSS to ensure it is able to detect cancers accurately, and then assess its performance in real-world clinical practice to assess its impact on cancer diagnosis by GPs. By leveraging powerful AI methods and the type of data unreachable by existing tools, we believe we can make a difference to cancer diagnosis whilst supporting clinicians and the health service. The end goal is for patients to be diagnoses earlier, treated more effectively, live longer, healthier lives with downstream impacts on the economy through reduced morbidity and pressures on the healthcare system.