Recipient organisationImperial College LondonSource-published name: Imperial College of Science, Technology and Medicine
Funding£2.5M
PeriodSept 2024 — Aug 2027
In plain English
AI plain-English summary
Before a GP appointment, an animated virtual human will interview patients about their symptoms, then feed that information into an artificial intelligence that flags possible cancers. This matters because GPs currently have limited time and incomplete information during consultations, which can delay cancer diagnosis. Pancreatic and lung cancers are often caught late, when survival rates are poor. The system aims to reduce misdiagnosis by giving GPs a ranked list of possible conditions before they even see the patient. If successful, the AI could transform how GPs work. Patients would provide structured symptom histories before the appointment, and the system would integrate that data with their medical records to generate a differential diagnosis. The project will test the system in NHS Sussex, initially for lung and pancreatic cancers, then adapt it for other conditions. It also focuses on making the technology usable for disadvantaged groups who currently face longer delays in diagnosis. The work takes a US-developed system (SOAP Health) and validates it for the NHS, moving it from technology readiness level 5 to 8. This is applied research with a clear practical goal: faster, more equitable cancer diagnosis in general practice.
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Research question This proposal aims to validate and evaluate in the NHS Artificial Intelligence (AI) to enable the early diagnosis of cancer in general practice. Background Misdiagnosis in general practice, defined as a failure to enable definitive diagnosis of a patient within a prompt timescale, has significant repercussions for the care and survival of patients presenting with possible cancer. Differential diagnosis lists presented at the start of a consultation can improve diagnostic accuracy, but this is best deployed before the GP consultation. Recent developments in the USA with SOAP Health s Ideal Medical AI Assistant (IMAA) with the 'Perfect Medical Interviewer' (PMI) that can enable symptoms to be gathered pre-GP access (by an animated interactive virtual human) and seamlessly integrated with other data about that patient to provide a differential diagnosis list for the GP (RiskVue module). In addition, cutting edge developments in Machine Learning (ML) from multi-modal data including natural language processing (NLP) and Answer Set Programming will be incorporated over time to strengthen the knowledge engine of RiskVue. SOAP Health is currently TR5 in the USA and we will take this to TR8 in the UK NHS. We will use pancreatic and lung cancers as exemplars, as both carry a very poor survival rate on account of the late stage in diagnosis in the majority of patients. Both act as good exemplars for learning from multimodal data. Aims and objectives. Objectives 1. To validate in the NHS a virtual-human medical history assistant developed by SOAP Health in the US to collect structured medical history data from the patient prior to the GP consultation, with an emphasis on usability across disadvantaged groups. 2. To explore adaptation of the diagnostic risk model to the UK NHS by multimodal data analytics based on natural language processing and knowledge graphs (ILASP) to risk-stratify patients for early cancer presentation. 3. To fine-tune the risk model to UK patients by exploring methods for representing changes in symptom and clinical data over time. 4. To integrate the learning system within a demonstrator NHS Integrated Care Board 5. To evaluate the within-consultation medical assistant for supporting GP decision-making with reference to its impact on reducing inequalities in time to diagnosis. 6. To support entry to market of SOAP Health in the NHS. Methods Work will be distributed over five WS. Consisting of: 1. Adaptation and validation of the IMAA in the UK 2. Multimodal ML to generate explainable and extensible UK-specific models for cancer risk. 3. Clinical evaluation of the revised UK Riskvue module 4. Co-working with NHS Sussex in developing the system in an NHS England Digital Innovation Lab. 5. Patient co-design, management and IP protection for NHS deployment. Timelines for delivery PMI 12 months, RiskVue 36 months Anticipated impact and dissemination This proposal has the potential to transform the process by which GPs are supported in making accurate and timely diagnoses, initially for cancer, but also the many other conditions that are part of the wide differential diagnoses for lung and pancreatic cancer presentations.
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