Technology
How AI can deliver on the cancer survivorship pledge
Dr. Shivan Sivakumar PhD FRCP, from the University of Birmingham, says that artificial intelligence is battling cancer but not how you think. He explains how AI is delivering on the NHS 10-Year Health Plan’s survivorship pledge, and urges the oncology community to rethink where we invest our resources and focus.
I’ve spent much of my career in oncology wrestling with a frustrating reality that while we’ve made remarkable strides in diagnosing cancer, our ability to treat it - especially in the survivorship phase - hasn’t kept pace. We’ve become very good at finding the specific genetic cancer variations through advanced diagnostics, yet cancer treatment can only target 46 of the 750 known mutations which impact the disease’s progression and recurrence. This gap directly echoes the newly published
NHS 10 Year Health Plan, which calls for earlier diagnosis and a pipeline of affordable personalised interventions to keep patients cancer-free. Specifically, this gap leaves patients in a
vulnerable state post-treatment, often handed the outdated advice to ‘watch and wait’ for the cancer to return. It’s a passive approach that fails both patients and clinicians and I believe it’s time to rethink how we address cancer care, particularly for individuals who have survived cancer.
Addressing the survivorship gap: AI-driven drug repurposing for cancer recurrence prevention Artificial intelligence (AI) does not just have a role to play in new drug discovery and diagnostics. It has the potential to have a major
impact on drug repurposing, which is a faster, cheaper solution and better for patients. We need to challenge the conventional
narrative that the future of cancer treatment lies solely in developing new drugs. Don’t get me wrong, new therapies are vital, but they often take over a decade and billions of pounds before they’re available. Meanwhile, cancer survivors are left without proactive options to prevent recurrence during a time when intervention could dramatically improve outcomes. This survivorship gap, historically underfunded compared to diagnostics and active treatment, represents our greatest opportunity to change lives. The real breakthrough isn’t in chasing elusive new compounds but in leveraging AI to unlock the potential of existing, well-understood drugs to target patient-specific mutations and delay or prevent the cancer’s return. Consider the scale of the problem. After
surgery or chemotherapy, most cancer survivors, across nearly all cancer types, have no standard maintenance treatment. They’re told to monitor for symptoms and hope for the best. This isn’t just a medical oversight; it’s an emotional burden, leaving patients feeling powerless when agency matters most. Using AI, it’s possible to bridge the gap by analysing over 100,000 peer-reviewed research
papers to identify low-cost, low-toxicity drugs already on the market that can target the full spectrum of 750 cancer driver mutations. Because these medicines are already generic, they directly support the NHS 10 Year Health Plan’s emphasis on cost-effective innovation that reduces health inequalities. This approach isn’t about replacing active treatment but about extending remission through personalised care that empower patients beyond ‘watching and waiting’.
Leveraging AI to discover multi-target drug combinations In this context, the power of AI lies in its ability to process vast datasets including biological, clinical and pharmacological, to uncover connections humans might miss. For instance, a study1
in the National Institutes of Health (NIH) database suggests that anti-inflammatory drugs may reduce the risk of disease recurrence in breast cancer patients by 42%. What AI can do is identify combinations of existing drugs that can simultaneously target multiple cancer vulnerabilities, say four or five at once, maximising impact while minimising toxicity. These aren’t speculative treatments; they’re based on existing evidence, repurposed for preventative care in ways that are safe, tolerable and affordable for long-term use. Unlike targeted therapies, which are often too toxic or costly for sustained application, the focus is on drugs that patients can integrate into their lives without the fear of debilitating side effects or financial ruin. This form of AI not only creates value, it democratises cancer care by making evidence-based interventions available to more patients sooner. It isn’t about profit through exclusivity, it’s about impact through accessibility.
A patient-centric approach to preventing cancer recurrence with AI I’m often asked how this aligns with broader
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www.clinicalservicesjournal.com I September 2025
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