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NIHR Funds Oxford AI Studies to Cut NHS Waiting Times
Imagine a hospital where a scan can be interpreted in seconds, a diagnosis can be issued before the patient leaves the emergency department, and a lung‑nodules patient receives a risk assessment in real time. That future is getting closer, thanks to an £8 million investment from the National Institute for Health and Care Research (NIHR). The institute has awarded £8,136,409 to six artificial‑intelligence projects that aim to shrink waiting times and improve patient care across the National Health Service.
The money comes through the NIHR’s Invention for Innovation (i4i) programme and backs three studies led by Oxford University Hospitals (OUH). Together, the projects—SAMURAI‑CT, SMART‑XR and SWIFT LUNG—form part of a broader Oxford‑led platform that evaluates clinical AI technologies across the entire translation pathway.
SAMURAI‑CT, run by Oxford Clinical Artificial Intelligence Research (OxCAIR), will test an AI system that assists clinicians in interpreting non‑contrast head CT scans in emergency departments. By determining whether the system can help doctors spot urgent brain abnormalities faster, the study seeks to improve patient flow and cut delays in care.
SMART‑XR is a partnership between OUH and MedTech company Harrison.ai. It will assess whether an AI model can safely generate chest X‑ray reports autonomously. The evaluation will compare AI outputs against 12 months of imaging data from OUH and Manchester University NHS Foundation Trust.
SWIFT LUNG, another OUH‑linked project, is testing an AI tool developed by the Oxford spin‑out Optellum. The tool predicts lung‑cancer risk in patients who have lung nodules.
Professor Lucy Chappell, chief scientific adviser to the Department of Health and Social Care and chief executive of the NIHR, said the backing would help “drive the fundamental shift from an analogue to a digital health service and deliver the government’s 10‑year health plan”. She added that the investment would “cut NHS waiting times, fast‑track diagnoses and ensure patients receive more accessible, efficient, and high‑quality care.”
Dr Alex Novak, co‑director of OxCAIR, explained that the studies cover all aspects of AI use in healthcare. He said the research will look at ethical, governance and data‑infrastructure requirements, evaluate how AI affects clinician performance, and assess real‑world clinical impact in routine practice. He added that the work will provide a framework for generating evidence needed for the safe, effective and scalable adoption of AI across the NHS.
The SAMURAI programme, of which SAMURAI‑CT is a part, is a coordinated portfolio of studies designed to evaluate AI technologies across the entire clinical translation pathway. According to the NIHR, the programme aims to address the challenge of generating robust evidence for AI systems in real‑world clinical settings.
The six projects funded by the i4i programme represent a mix of diagnostic imaging, risk prediction and workflow optimisation. All are based at OUH, which has a long history of research in medical imaging and AI. The NIHR’s investment is part of a broader effort to support translational research that can move from laboratory prototypes to clinical deployment.
The studies will run over the next two to three years, with interim reports expected to be published in peer‑reviewed journals. The outcomes will inform NHS policy makers, clinicians and technology developers about the safety, efficacy and operational impact of AI tools in emergency and diagnostic settings.
In the short term, the projects will generate data on diagnostic accuracy, workflow efficiency and patient outcomes. In the longer term, the evidence produced could influence NHS procurement decisions, regulatory approvals and the design of future AI‑enabled clinical pathways.
The NIHR’s funding of these projects underscores the importance of evidence‑based evaluation in the adoption of AI in healthcare. While the technology promises faster diagnoses and reduced waiting times, the studies will also examine governance, data security and ethical considerations that accompany the deployment of AI in clinical practice.
As the NHS continues to explore digital transformation, the results of SAMURAI‑CT, SMART‑XR and SWIFT LUNG will provide a benchmark for other institutions considering similar AI solutions. The projects also illustrate how academic research, industry partnership and public funding can collaborate to advance patient care.
The NIHR’s investment is expected to accelerate the transition from prototype to practice, potentially leading to wider adoption of AI in diagnostic imaging and risk assessment across the NHS. The findings will also inform future funding decisions and policy frameworks aimed at integrating AI into routine care.
The projects are currently in the early phases of data collection and analysis. The NIHR and the participating institutions will publish detailed results once the studies reach completion.
The money comes through the NIHR’s Invention for Innovation (i4i) programme and backs three studies led by Oxford University Hospitals (OUH). Together, the projects—SAMURAI‑CT, SMART‑XR and SWIFT LUNG—form part of a broader Oxford‑led platform that evaluates clinical AI technologies across the entire translation pathway.
SAMURAI‑CT, run by Oxford Clinical Artificial Intelligence Research (OxCAIR), will test an AI system that assists clinicians in interpreting non‑contrast head CT scans in emergency departments. By determining whether the system can help doctors spot urgent brain abnormalities faster, the study seeks to improve patient flow and cut delays in care.
SMART‑XR is a partnership between OUH and MedTech company Harrison.ai. It will assess whether an AI model can safely generate chest X‑ray reports autonomously. The evaluation will compare AI outputs against 12 months of imaging data from OUH and Manchester University NHS Foundation Trust.
SWIFT LUNG, another OUH‑linked project, is testing an AI tool developed by the Oxford spin‑out Optellum. The tool predicts lung‑cancer risk in patients who have lung nodules.
Professor Lucy Chappell, chief scientific adviser to the Department of Health and Social Care and chief executive of the NIHR, said the backing would help “drive the fundamental shift from an analogue to a digital health service and deliver the government’s 10‑year health plan”. She added that the investment would “cut NHS waiting times, fast‑track diagnoses and ensure patients receive more accessible, efficient, and high‑quality care.”
Dr Alex Novak, co‑director of OxCAIR, explained that the studies cover all aspects of AI use in healthcare. He said the research will look at ethical, governance and data‑infrastructure requirements, evaluate how AI affects clinician performance, and assess real‑world clinical impact in routine practice. He added that the work will provide a framework for generating evidence needed for the safe, effective and scalable adoption of AI across the NHS.
The SAMURAI programme, of which SAMURAI‑CT is a part, is a coordinated portfolio of studies designed to evaluate AI technologies across the entire clinical translation pathway. According to the NIHR, the programme aims to address the challenge of generating robust evidence for AI systems in real‑world clinical settings.
The six projects funded by the i4i programme represent a mix of diagnostic imaging, risk prediction and workflow optimisation. All are based at OUH, which has a long history of research in medical imaging and AI. The NIHR’s investment is part of a broader effort to support translational research that can move from laboratory prototypes to clinical deployment.
The studies will run over the next two to three years, with interim reports expected to be published in peer‑reviewed journals. The outcomes will inform NHS policy makers, clinicians and technology developers about the safety, efficacy and operational impact of AI tools in emergency and diagnostic settings.
In the short term, the projects will generate data on diagnostic accuracy, workflow efficiency and patient outcomes. In the longer term, the evidence produced could influence NHS procurement decisions, regulatory approvals and the design of future AI‑enabled clinical pathways.
The NIHR’s funding of these projects underscores the importance of evidence‑based evaluation in the adoption of AI in healthcare. While the technology promises faster diagnoses and reduced waiting times, the studies will also examine governance, data security and ethical considerations that accompany the deployment of AI in clinical practice.
As the NHS continues to explore digital transformation, the results of SAMURAI‑CT, SMART‑XR and SWIFT LUNG will provide a benchmark for other institutions considering similar AI solutions. The projects also illustrate how academic research, industry partnership and public funding can collaborate to advance patient care.
The NIHR’s investment is expected to accelerate the transition from prototype to practice, potentially leading to wider adoption of AI in diagnostic imaging and risk assessment across the NHS. The findings will also inform future funding decisions and policy frameworks aimed at integrating AI into routine care.
The projects are currently in the early phases of data collection and analysis. The NIHR and the participating institutions will publish detailed results once the studies reach completion.
