Implementing artificial intelligence (AI) within the National Health Service (NHS) has emerged as a daunting endeavor, revealing significant challenges rarely anticipated by policymakers and healthcare leaders. A recent peer-reviewed qualitative study conducted by researchers at University College London (UCL) sheds light on the complexities involved in the procurement and early deployment of AI technologies tailored for diagnosing chest conditions, particularly lung cancer. The study surfaces amidst a broader national momentum aimed at integrating digital technology within healthcare systems as outlined in the UK Government’s ambitious 10-year NHS plan, which identifies digital transformation as pivotal for enhancing service delivery and improving patient experiences.

As artificial intelligence gains traction in healthcare diagnostics, NHS England launched a substantial initiative in 2023, whereby AI tools were introduced across 66 NHS hospital trusts, underpinned by a notable funding commitment of £21 million. This ambitious project aimed to establish twelve imaging diagnostic networks that could expand access to specialist healthcare opinions for a greater number of patients. The expected functionalities of these AI tools are significant, including prioritizing urgent cases for specialist review and assisting healthcare professionals by flagging abnormalities in radiological scans—tasks that could potentially ease the burden on overworked NHS staff.

However, two key aspects have emerged from this research, revealing that the rollout of AI systems has not proceeded as swiftly as NHS leadership had anticipated. Building on evidence gleaned from interviews with hospital personnel and AI suppliers, the UCL team identified procurement processes that were unanticipatedly protracted, with delays stretching from four to ten months beyond initial schedules. Strikingly, by June 2025—18 months post-anticipated completion—approximately a third of the participating hospital trusts had yet to integrate these AI tools into clinical practice. This delay emphasizes a critical gap between the technological promise of AI and the operational realities faced by healthcare institutions.

Compounding these challenges, clinical staff equipped with already high workloads have found it tough to engage wholeheartedly with the AI project. Many staff members expressed skepticism about the efficacy of AI technologies, rooted in concerns about their integration with existing healthcare workflows, and the compatibility of new AI tools with aging IT infrastructures that vary widely across numerous NHS hospitals. The researchers noted that many frontline workers struggled to perceive the full potential of AI, especially in environments that overly complicated the procurement and implementation processes.

In addition to identifying these hurdles, the study underscored several factors that proved beneficial in the smooth embedding of AI tools. Enthusiastic and committed local hospital teams played a significant role in facilitating project management, and strong national leadership was critical in guiding the transition. Hospitals that employed dedicated project managers to oversee the implementation found their involvement invaluable in navigating bureaucratic obstacles, indicating a clear advantage to having directed oversight in challenging integrations.

Dr. Angus Ramsay, the study’s first author, observed the lessons highlighted by this investigation, particularly within the context of the UK’s push toward digitizing the NHS. The study advocates for a recalibrated approach towards AI implementation—one that considers existing pressures within the healthcare system. Ramsay noted that the integration of AI technologies, while potentially transformative, requires tempered expectations regarding their ability to resolve deep-rooted challenges within healthcare services as policymakers might wish.

Throughout the evaluation, which spanned from March to September of last year, the research team analyzed how different NHS trusts approached AI deployment and their varied focal points, such as X-ray and CT scanning applications. They observed both the enthusiasm and the reluctance among staff to adapt to this novel technology, with senior clinical professionals expressing reservations over accountability and decision-making processes potentially being handed over to AI systems without adequate human oversight. This skepticism highlighted an urgent need for comprehensive training and guidance, as current onboarding processes were often inadequate for addressing the query-laden concerns of employees.

The analysis conducted by the UCL-led research team revealed that initial challenges, such as the overwhelming amount of technical information available, hampered effective procurement. Many involved in the selection process struggled to distill and comprehend essential elements contained within intricate AI proposals. This situation suggests the utility of establishing a national shortlist of approved AI suppliers to streamline procurement processes at local levels and alleviate the cognitive burdens faced by procurement teams.

Moreover, the emergence of widespread enthusiasm in some instances provided a counterbalance to initial skepticism. The collaborative nature of the imaging networks was particularly striking; team members freely exchanged knowledge and resources, which enriched the collective expertise as they navigated the implementation journey. The fact that many hospitals had staff committed to fostering interdepartmental collaboration made a substantial difference, aiding the mutual learning process involved in the integration of AI technologies.

One of the most pressing findings from the study was the realization that AI is unlikely to serve as a “silver bullet” for the multifaceted issues confronting the NHS. The variability in clinical requirements among the numerous organizations that compose the NHS creates an inherently complicated landscape for the introduction of diagnostic tools. Professor Naomi Fulop, a senior author of the study, emphasized that the diversity of clinical needs across numerous agencies complicates the implementation of diagnostic systems that can cater effectively to everyone. Lessons learned from this research will undoubtedly inform future endeavors in making AI tools more accessible while ensuring the NHS remains responsive to its staff and patients.

Moving forward, an essential next step will involve evaluating the use of AI tools post-implementation, aiming to understand their impact once they have been fully integrated into clinical operations. The researchers acknowledge that, while they successfully captured the procurement and initial deployment stages, further investigation is necessary to assess the experiences of patients and caregivers, thereby filling gaps in understanding around equity in healthcare delivery with AI involvement.

The implications of this study are profound, shedding light on the careful considerations necessary for effective AI introduction within healthcare systems, underscoring the urgency of embedding educational frameworks that equip staff not just with operational knowledge, but with an understanding of the philosophical, ethical, and practical nuances of AI in medicine. This nuanced understanding is pivotal as healthcare practitioners prepare for a future increasingly defined by technological integration and automation.

Faculty members involved in this transformative study, spanning various academic and research backgrounds, are poised to lead this critical discourse, attempting to bridge the knowledge gap that currently exists between technological innovation and clinical practice. As AI continues its trajectory toward becoming an integral part of healthcare, this analysis serves as a clarion call for future studies that prioritize patient experience, clinical accountability, and healthcare equity in the age of artificial intelligence.

Subject of Research: AI tools for chest diagnostics in NHS services.
Article Title: Procurement and early deployment of artificial intelligence tools for chest diagnostics in NHS services in England: A rapid, mixed method evaluation.
News Publication Date: 11-Sep-2025.
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Keywords

AI, NHS, healthcare, diagnostics, technology, implementation, policy, research, patient care, digital transformation.

Tags: AI integration challenges in NHS healthcareAI tools for urgent case prioritizationartificial intelligence in lung cancer diagnosiscomplexities of AI deployment in healthcareenhancing patient experience with AIfunding for AI in NHS hospitalshealthcare technology procurement difficultiesNHS digital transformation initiativesNHS imaging diagnostic networksNHS policy implications for AI technologiesrole of AI in improving healthcare deliveryUCL research on AI in healthcare

Cyber defenders need AI tools to fend off a new generation of AI-powered attacks, the head of the National Geospatial-Intelligence Agency said Wednesday.

“The concept of using AI to combat AI attack or something like that is very real to us. So this, again, is commanders’ business. You need to enable your [chief information security officer] with the tools that he or she needs in order to employ AI to properly handle AI-generated threats,” Vice Adm. Frank Whitworth said at the Billington Cybersecurity Summit Wednesday.

Artificial intelligence has reshaped cyber, making it easier for hackers to manipulate data and craft more convincing fraud campaigns, like phishing emails used in ransomware attacks. 

Whitworth spoke a day after Sean Cairncross, the White House’s new national cyber director, called for a “whole-of-nation” approach to ward off foreign-based cyberattacks. 

“Engagement and increased involvement with the private sector is necessary for our success,” Cairncross said Tuesday at the event. “I’m committed to marshalling a unified, whole-of-nation approach on this, working in lockstep with our allies who share our commitment to democratic values, privacy and liberty…Together, we’ll explore concepts of operation to enable our extremely capable private sector, from exposing malign actions to shifting adversaries’ risk calculus and bolstering resilience.”

The Pentagon has been incorporating AI, from administrative tasks to combat. The NGA has long used it to spot and predict threats; use of its signature Maven platform has doubled since January and quadrupled since March 2024. 

But the agency is also using “good old-fashioned automation” to more quickly make the military’s maps. 

“This year, we were able to produce 7,500 maps of the area involving Latin America and a little bit of Central America…that would have been 7.5 years of work, and we did it in 7.5 weeks,” Whitworth said. “Sometimes just good old-fashioned automation, better practices of using automation, it helps you achieve some of the speed, the velocity that we’re looking for.”

The military’s top officer also stressed the importance of using advanced tech to monitor and preempt modern threats.

“There’s always risk of unintended escalation, and that’s what’s so important about using advanced tech tools to understand the environment that we’re operating in and to help leaders see and sense the risk that we’re facing. And there’s really no shortage of those risks right now,” said Gen. Dan Caine, chairman of the Joint Chiefs of Staff, who has an extensive background in irregular warfare and special operations, which can lean heavily on cutting-edge technologies. 

“The fight is now centered in many ways around our ability to harvest all of the available information, put it into an appropriate data set, stack stuff on top of it—APIs and others—and end up with a single pane of glass that allows commanders at every echelon…to see that, those data bits at the time and place that we need to to be able to make smart tactical, operational and strategic decisions that will allow us to win and dominate on the battlefields of the future. And so AI is a big part of that,” Caine said. 

The Pentagon recently awarded $200 million in AI contracts while the Army doubled down on its partnership with Palantir with a decade-long contract potentially worth $10 billion. The Pentagon has also curbed development of its primary AI platform, Advana, and slashed staff in its chief data and AI office with plans of a reorganization that promises to “accelerate Department-wide AI transformation” and make the Defense Department “an AI-first enterprise.”

Implementing artificial intelligence (AI) into NHS hospitals is far harder than initially anticipated, with complications around governance, contracts, data collection, harmonization with old IT systems, finding the right AI tools and staff training, finds a major new UK study led by UCL researchers.

Authors of the study, published in eClinicalMedicine, say the findings should provide timely and useful learning for the UK Government, whose recent 10-year NHS plan identifies digital transformation, including AI, as a key platform to improving the service and patient experience.

In 2023, NHS England launched a program to introduce AI to help diagnose chest conditions, including lung cancer, across 66 NHS hospital trusts in England.

The trusts are grouped into 12 imaging diagnostic networks: these hospital networks mean more patients have access to specialist opinions. Key functions of these AI tools included prioritizing critical cases for specialist review and supporting specialists’ decisions by highlighting abnormalities on scans.

The research was conducted by a team from UCL, the Nuffield Trust, and the University of Cambridge, analyzing how procurement and early deployment of the AI tools went. The study is one of the first studies to analyze real-world implementation of AI in health care.

Evidence from previous studies, mostly laboratory-based, suggested that AI might benefit diagnostic services by supporting decisions, improving detection accuracy, reducing errors and easing workforce burdens.

In this UCL-led study, the researchers reviewed how the new diagnostic tools were procured and set up through interviews with hospital staff and AI suppliers, identifying any pitfalls but also any factors that helped smooth the process.

They found that setting up the AI tools took longer than anticipated by the program’s leadership. Contracting took between four and 10 months longer than anticipated and by June 2025, 18 months after contracting was meant to be completed, one-third (23 out of 66) of the hospital trusts were not yet using the tools in clinical practice.

Key challenges included engaging clinical staff with already high workloads in the project, embedding the new technology in aging and varied NHS IT systems across dozens of hospitals and a general lack of understanding, and skepticism, among staff about using AI in health care.

The study also identified important factors which helped embed AI, including national program leadership and local imaging networks sharing resources and expertise, high levels of commitment from hospital staff leading implementation, and dedicated project management.

The researchers concluded that while “AI tools may offer valuable support for diagnostic services, they may not address current health care service pressures as straightforwardly as policymakers may hope” and are recommending that NHS staff are trained in how AI can be used effectively and safely and that dedicated project management is used to implement schemes like this in the future.

First author Dr. Angus Ramsay (UCL Department of Behavioral Science and Health) said, “In July ministers unveiled the Government’s 10-year plan for the NHS, of which a digital transformation is a key platform.

“Our study provides important lessons that should help strengthen future approaches to implementing AI in the NHS.

“We found it took longer to introduce the new AI tools in this program than those leading the program had expected.

“A key problem was that clinical staff were already very busy—finding time to go through the selection process was a challenge, as was supporting integration of AI with local IT systems and obtaining local governance approvals. Services that used dedicated project managers found their support very helpful in implementing changes, but only some services were able to do this.

“Also, a common issue was the novelty of AI, suggesting a need for more guidance and education on AI and its implementation.

“AI tools can offer valuable support for diagnostic services, but they may not address current health care service pressures as simply as policymakers may hope.”

The researchers conducted their evaluation between March and September last year, studying 10 of the participating networks and focusing in depth on six NHS trusts. They interviewed network teams, trust staff and AI suppliers, observed planning, governance and training and analyzed relevant documents.

Some of the imaging networks and many of the hospital trusts within them were new to procuring and working with AI.

The problems involved in setting up the new tools varied—for example, in some cases, those procuring the tools were overwhelmed by a huge amount of very technical information, increasing the likelihood of key details being missed. Consideration should be given to creating a national approved shortlist of potential suppliers to facilitate procurement at local level, the researchers said.

Another problem was initial lack of enthusiasm among some NHS staff for the new technology in this early phase, with some more senior clinical staff raising concerns about the potential impact of AI making decisions without clinical input and on where accountability lay in the event a condition was missed.

The researchers found the training offered to staff did not address these issues sufficiently across the wider workforce—hence their call for early and ongoing training on future projects.

In contrast, however, the study team found the process of procurement was supported by advice from the national team and imaging networks learning from each other.

The researchers also observed high levels of commitment and collaboration between local hospital teams (including clinicians and IT) working with AI supplier teams to progress implementation within hospitals.

Senior author Professor Naomi Fulop (UCL Department of Behavioral Science and Health) said, “In this project, each hospital selected AI tools for different reasons, such as focusing on X-ray or CT scanning, and purposes, such as to prioritize urgent cases for review or to identify potential symptoms.

“The NHS is made up of hundreds of organizations with different clinical requirements and different IT systems and introducing any diagnostic tools that suit multiple hospitals is highly complex. These findings indicate AI might not be the silver bullet some have hoped for but the lessons from this study will help the NHS implement AI tools more effectively.”

While the study has added to the very limited body of evidence on the implementation and use of AI in real-world settings, it focused on procurement and early deployment. The researchers are now studying the use of AI tools following early deployment when they have had a chance to become more embedded.

Further, the researchers did not interview patients and caregivers and are therefore now conducting such interviews to address important gaps in knowledge about patient experiences and perspectives, as well as considerations of equity.