AI - BOWEL
Research type
Research Study
Full title
AI – BOWEL (Artificial intelligence for Bowel Orientated Workflow Enhancement and Learning)
IRAS ID
351390
Contact name
Easan Anand
Contact email
Sponsor organisation
London North West University NHS Trust
Duration of Study in the UK
2 years, 0 months, 1 days
Research summary
Gastrointestinal (GI) diseases, such as inflammatory bowel disease (IBD) and bowel cancer, require lifelong monitoring through advanced imaging techniques like CT scans, MRI, and endoscopy. Currently, doctors carefully review these images to monitor disease progression, but this process takes time, can be subjective/expertise dependent and can delay patients receiving important health updates. Additionally, interpreting complex images can lead to differences in opinion among clinicians, which may affect patient care.
A new area of research is exploring the use of artificial intelligence (AI) to support these clinicians by helping interpret GI imaging quickly and consistently. AI tools can learn from expert interpretations to recognize patterns in images that are linked with specific diseases, offering reliable diagnostic support. This technology can also help ease the workload on healthcare providers and reduce wait times for patients.
Moreover, patients are increasingly accessing their medical results through online platforms like the NHS app. However, many patients struggle to understand the technical details of their radiology reports, which can feel confusing and limit their engagement in their own healthcare. Recent discussions with patients highlighted a strong need for clearer, more patient-friendly summaries of their results, especially for those with complex GI conditions.
This project seeks to address these challenges by developing AI systems that can analyze GI images and videos, providing both accurate diagnostic information for doctors and simplified, easy-to-understand reports for patients. The accuracy and usefulness of these AI-generated reports will be compared with traditional radiology reports, using data from real biopsies and surgical findings to verify AI interpretations.
Ultimately, the project aims to make healthcare more accessible, empowering patients to better understand their conditions and stay engaged in their care, while also improving efficiency and consistency in diagnosing GI diseases.
REC name
Wales REC 6
REC reference
24/WA/0374
Date of REC Opinion
19 Dec 2024
REC opinion
Favourable Opinion