NHS Shared Business Services is rolling out a massive £150 million tender, broken into six sections, to harness AI in medical imaging and analysis for faster diagnoses.
AI can analyze medical images in a matter of seconds. It helps spot conditions like strokes, hidden issues, future diseases, odd growths, tiny tumors, and even subtle changes. These systems give real-time, evidence-backed recommendations during patient consultations, suggesting possible diagnoses and treatment plans based on current clinical guidelines and specific patient data.
Kelly Bevington, the senior category manager for digital and IT at NHS Shared Business Services, explained that the current AI tools for neuroscience and imaging are set to expire, leading to this new comprehensive offering. She referred to a review by Lord Darzi that highlights the need for the NHS to advance its digital transformation.
This framework aims to enhance how AI can be applied across different medical specialties, driving efficiency in preventing, diagnosing, and treating illnesses while streamlining clinical workflows.
The first section is all about AI image analysis. It focuses on diagnosing neurological disorders like Alzheimer’s, Parkinson’s, and multiple sclerosis through imaging and other tests. It also looks at using AI for detecting lumps, ovarian cancer, endometriosis, fetal structures, and early pregnancy complications.
In oncology, AI plays a key role in spotting various cancers early by examining imaging data, genetic information, and patient records. Other applications include aiding in musculoskeletal conditions, cardiology, gastroenterology, urology, ophthalmology, and analyzing neurological data to assess stroke risks.
The second section emphasizes adopting AI algorithms and deep learning models that analyze large datasets to identify patterns missed in a typical lab or clinical setting. For example, AI can evaluate digital biopsy slides, tissues, blood, and bone marrow, revolutionizing how pathology labs work.
The third section is about using predictive analytics to enhance patient flow and experiences through better integration with health records. The fourth section focuses on research and development to foster cross-institution collaboration, especially in drug discovery and clinical trials. The fifth section targets operational efficiency, while the sixth deals with AI consultancy, implementation, and training.