£150m AI framework agreement set to drive NHS forward

NHS Shared Business Services has released a tender valued at £150m, divided into six lots, focused on utilizing artificial intelligence (AI) in medical imaging and analysis to expedite the diagnostic process.

AI systems can rapidly analyze medical images to assist in identifying conditions like strokes, asymptomatic ailments, future diseases, suspicious areas, small tumors, or subtle abnormalities. These systems can offer real-time, evidence-based recommendations during patient consultations. NHS Shared Business Services highlighted that these AI systems can propose potential diagnoses and treatment options based on up-to-date clinical guidelines and patient data.

In a statement on the NHS Shared Business Services website, Kelly Bevington, the senior category manager of digital and IT, expressed, “With our current artificial intelligence offerings set to expire, we aimed to consolidate the experience gained from these framework agreements into a comprehensive offering that aligns with Lord Darzi’s independent review, propelling the NHS towards digital transformation.”

The framework agreement will concentrate on the application of AI across various specialties to enhance prevention, diagnosis, treatment, and clinical workflow optimization.

The initial lot focuses on AI image analysis for diagnosing neurological disorders like Alzheimer’s disease, Parkinson’s disease, and multiple sclerosis through imaging and other diagnostic tests. Additionally, it covers the use of AI in medical imaging for detecting various conditions such as lumps, ovarian cancer, endometriosis, fetal structures, anatomical development, and early identification of pregnancy complications.

AI can also aid in the early detection and diagnosis of different cancers in oncology by analyzing imaging data, genetic information, and patient records. Other applications include supporting musculoskeletal conditions, cardiology, gastroenterology, urology, ophthalmology, and analyzing neurological data to assess stroke risk.

The second lot centers on adopting AI algorithms and deep learning models trained on extensive datasets to identify patterns that may go unnoticed in a typical clinical or laboratory setting. For instance, AI can analyze digital slides from biopsies, tissues, cells, blood, and bone marrow to aid in disease detection, enhancing workflow in pathology labs.

Lot three encompasses predictive analytics to enhance patient flow and experience by improving interoperability with health records. Lot four focuses on research and development to facilitate collaboration and research across institutions, such as drug discovery and clinical trials. Lot five emphasizes operational efficiency, while lot six covers AI consultancy, implementation, and training.