Diagnostic AI for the Detection of Fractures
Introduction
The Diagnostic AI for the Detection of Fractures framework is managed by the Hull University Teaching Hospitals NHS Trust and falls within the Health & Social Care category. Unlike a Dynamic Purchasing System (DPS), this framework does not allow for continuous application over its lifetime but provides a structured procurement route for contracting authorities to access innovative artificial intelligence (AI) solutions specifically designed for the healthcare sector. Although the framework's end date is not specified, it serves the essential purpose of enabling NHS organisations to integrate advanced AI technologies into their radiology departments, thereby enhancing diagnostic accuracy and efficiency.
Context & Use Case
The framework is particularly focused on improving the diagnostic processes within healthcare institutions by employing AI technologies to detect fractures in medical imaging, such as X-rays. Contracting authorities, including hospitals and other NHS bodies, utilise this framework to procure AI solutions that augment their radiology services. The use of AI in this context is pivotal, as it can significantly reduce the time required to analyse imaging results, increase accuracy in detecting fractures, and ultimately enhance patient outcomes. The framework is part of broader efforts to modernise healthcare delivery through the adoption of digital solutions.
Scope of Services
The Diagnostic AI for the Detection of Fractures framework encompasses a variety of AI-driven services and technologies designed to support the healthcare sector. The services typically covered under this framework include: 1. AI software for fracture detection in X-ray imaging 2. Integration support for AI solutions within existing hospital IT systems 3. Training and support for radiology staff on AI tools 4. Ongoing software updates and maintenance services 5. Data analytics and reporting tools to assist in clinical decision-making
Who Can Apply
The framework is open to suppliers who specialise in developing and providing AI solutions tailored to the healthcare sector. Suitable suppliers are those with a proven track record in AI technology, particularly in medical imaging and diagnostics. As it is not a DPS, the application window does not remain open continuously; hence, interested suppliers should be vigilant about application timelines and criteria set by the Hull University Teaching Hospitals NHS Trust. Suppliers must pass a capability assessment to ensure their solutions meet the rigorous standards required for healthcare applications.
Buyer Benefits
Public sector buyers, such as NHS trusts and hospitals, benefit from this framework by gaining streamlined access to cutting-edge AI technologies that are essential for modernising their diagnostic processes. The framework ensures compliance with NHS procurement standards and offers a competitive environment for suppliers, which can lead to more favourable terms and pricing. By using this framework, buyers can expedite the procurement process, reduce the administrative burden, and focus on implementing solutions that improve patient care and operational efficiency.
Lots
Not applicable
Next Steps
Suppliers interested in this opportunity should first review the specific requirements and criteria outlined by the Hull University Teaching Hospitals NHS Trust. It is advisable to contact the contracting authority directly for detailed information regarding application procedures and deadlines. This will ensure that suppliers are adequately prepared to submit a comprehensive and competitive application.
How Biddable Can Help
Biddable supports suppliers by offering valuable insights into discovering suitable frameworks and preparing for successful applications. By providing visibility into the procurement pipeline, Biddable helps suppliers anticipate opportunities and align their offerings with public sector needs. Our aim is to equip suppliers with the knowledge and tools necessary to navigate the procurement landscape efficiently and effectively.