On 2 July 2026, Cancer Research UK hosted an online event to address an increasingly urgent question facing the cancer community: how do we navigate the rapidly expanding Artificial Intelligence (AI) landscape and decide which tools to adopt to support the earlier diagnosis of cancer?
The event report summaries the key themes and discussion points from the event and concludes with the questions and challenges posed throughout the webinar.
Policy makers are heralding AI as a means of alleviating pressures on health systems and supporting cancer diagnosis. Whilst the event affirmed that AI likely holds promise for the cancer community, a core message was that the ambition to embrace AI must be matched by health system readiness to do so.
The presentations and discussions emphasised that meaningful patient benefit will only be achieved if health systems are well-equipped to prioritise, evaluate and identify the most promising innovations. This is particularly important given the complexity, scale and competitiveness of the AI marketplace. Our expert speakers drew on real-world initiatives and activities to illustrate how health systems are approaching this task at a regional and national level. They highlighted what system readiness needs to look like if we’re to make evidence-based decisions about AI.
Presentations highlighted several barriers to effectively evaluating AI technologies. These include issues around evidence generation and variation in evidence appraisal capabilities across health systems. While these issues are not unique to AI, they are exacerbated by due to the rapid pace of technological developments, pressure for adoption and the need for ongoing performance monitoring.
The speakers described the need to adapt approaches to evidence generation and evaluation to support decision making. Decisions on implementation must take account of different considerations, such as the tool’s safety, value and advantage over competing innovations. Speakers described the role of head to-head comparisons at a local level, as well as the efforts underway to set up national innovation pathways and adapt regulatory frameworks to help enable more robust appraisals of AI technologies.
Other enablers for the prioritisation and adoption of AI included well-informed and confident adopters or users of technology and strong demand signalling from the health system to innovators. Several speakers underlined the opportunities for the cancer community to set and communicate clinical priorities, so that emerging technologies (AI and non-AI alike) are developed to address the most pressing concerns in cancer.
Indeed, the need for a strategic, clear-sighted and collaborative approach to AI was at the heart of the event. Moving forwards, it is imperative that researchers, policymakers and system leaders build on the vital work that’s underway to better navigate and regulate a busy AI marketplace and step up efforts to tackle the many remaining questions in a coordinated way.
We have summarised the key themes, steers and takeaways from the event below.
The event’s golden thread was the idea that our ability to successfully harness AI will depend not only on whether AI technologies are ready for use, but whether the health system is equipped to make evidence-based decisions about which tools and use cases to prioritise and how to evaluate them appropriately.
Many of the issues discussed are longstanding challenges in innovation adoption, which AI is making more visible.
The health system must get better at clarifying, prioritising and signalling our clinical priorities and not let the market set the agenda with a technology-first approach.
A recurring theme was that AI tools should be held to the same evidential principles as other diagnostic technologies. However, health systems need to be more agile to enable more dynamic assessment and validation of AI technologies.
Regulatory frameworks for innovations need to accommodate the distinct challenges associated with AI technologies. Work is underway to address this via the National Commission into the Regulation of AI in Healthcare.
Panellists stressed that regulatory approval for an AI technology provides assurance around aspects such as safety and technical performance, but it does not tell decision markers whether a tool improves outcomes, represents good value for money, or should be prioritised over competing innovations. We need a range of data points to determine the answers to those questions.
We need to better coordinate evidence generation with regards to AI technologies and standardise how evidence is appraised across the system.
There must be clarity and transparency on the evidence required to substantiate industry claims, which is a challenge for both health system decision makers and innovators to navigate.
Overall, collaboration and coordination will be key to translating the promise of AI into patient benefit. We need greater coordination across the cancer community and health system to identify clinical priorities, determine and generate the evidence required to assess AI technologies, and share learnings as we continue to prioritise, appraise and adopt new innovations.
Read the event report for a full summary of the talks, reflections and key themes.
Read the report in full