From Evidence To AI: Why The Future Of Oncology Decision Support Must Be Built On Living Evidence

How do oncologists decide which treatment to give their patients?

It’s rarely an easy choice. Physicians must weigh multiple levels of information, such as the patient’s disease stage, genetic markers, previous therapies, overall health in general and even personal preferences. Then comes the quest for evidence.

In order to validate the optimal way forward, oncologists need not only know what is effective, but also whether it is FDA-approved, guideline-adherent or available through a clinical trial. To find the best, most up-to-date information, that validation typically involves toggling between PubMed, society guidelines, journal notifications and conference summaries, and then rationalizing information that doesn’t always align.

All of this is tedious and time-consuming. Time that most oncologists don’t have. In a high-volume clinic, a medical oncologist may see 30 to 50 patients in a day. But even with all of those time pressures, each and every decision should be made with the latest, most complete and scientifically valid evidence available.

The stakes are high. With the mountain of new research and evidence published in oncology journals constantly expanding, evidence literally shifts by the day. Those shifts in evidence—the decisions between the right and wrong treatment—can be life or death.

The Enduring Value Of Evidence Hierarchies

Medicine has long recognized that not all evidence is created equal. A single case report may stimulate ideas, but it cannot guide practice. Observational studies provide associations but not certainty. Randomized controlled trials minimize bias and provide more insight. But at the very top of the hierarchy are systematic reviews and meta-analyses, which combine the entire weight of the evidence.

This hierarchy matters because medicine is complicated. If we relied on anecdotes or headlines in isolation, patients would be subjected to treatments that look promising by themselves but prove ineffective or even counterproductive when considered in context.

For this reason, organizations from the FDA to WHO mandate Systematic Literature Reviews (SLRs) when shaping guidelines, approvals and policies. Systematic reviews are the gold standard for evaluating medical evidence—the safety net for modern medicine. They prevent us from the risks of cherry-picking studies, overvaluing anecdotes or relying on unverified opinions.

The Lure And Risk Of Chatbots

Given the deluge of new medical information—and the tedium of just reading it all, let alone evaluating it—it’s no wonder that AI chatbots have captured attention. Faced with information overload, the idea of typing a quick question and receiving a fluent, confident paragraph or two is more than just appealing. It can be viewed as a lifeline for busy oncologists.

But that’s where the danger lies. Chatbots don’t conduct systematic reviews. They can’t distinguish between high-quality trials and weak studies. They don’t verify whether a therapy is FDA-approved or buried in an outdated guideline. And in some cases, they even fabricate references, miss key data or rank that data inappropriately.

Convenience can be seductive, but in oncology, where the margin for error is minute, the cost of error, or incomplete or inaccurate information, is disastrous. That convenience might be harmless if you’re asking Siri to find the nearest grocery store. But in cancer treatment, the right choice can extend life. The wrong choice can cut it short.

Evidence Hierarchies Matter Everywhere

The lesson extends well beyond oncology. In cardiology, guidelines for heart failure shift frequently. Missing an update could mean prescribing a less effective therapy. In infectious disease, choosing the wrong antibiotic fuels global resistance—making “tried and true” therapies less potent, and new approved therapies a better solution.

Outside of medicine, the same principle holds true. Financial advisors trust portfolio strategies grounded in decades of cumulative analysis, not a single trader’s hunch. Aviation safety regulations are shaped by the aggregation of countless investigations, not anecdotal exceptions. Across industries, systematic, comprehensive evidence beats selective inputs every time.

From Static Reviews To Living Evidence

If chatbots aren’t the solution, then what is?

The answer lies in bringing evidence hierarchies into the era of AI. Imagine a living systematic review in real-time, providing a comprehensive, up-to-date synthesis of the evidence—backed by AI and vetted by humans. Instead of replacing systematic reviews, AI in this new paradigm augments them. Algorithms filter through the sheer volume of new publications, screen for relevance, raise quality issues and update evidence maps in real time. And then experts evaluate the results before they reach the physician’s desktop.

This model is rigorous yet addresses medicine’s biggest bottleneck—time. Doctors would no longer be forced to sort through hundreds of studies manually. Instead, they would access a dynamic, physician-ready summary rooted in the totality of evidence. AI does the heavy lifting of scanning and sorting, while human experts remain the arbiters of interpretation.

A Human-AI Partnership

This combination is the future that I am dedicated to and the foundation of the work that my team is producing. At Oncoscope, we don’t rely on generative AI to spin out answers. Instead, we use a suite of AI models to reproduce and accelerate the standardized steps of a systematic review.

Think of it like a symphony. AI can tune the instruments, arrange the sheet music and keep the score updated in real time. But only the conductor—the oncologist—can interpret the music for the audience.

This collaboration leverages each party’s strength: Machines are better at speed and repetition, while humans are better at judgment and context. The end product is evidence, both thorough and up-to-date, that doesn’t overwhelm the clinicians who need to implement it.

Why Caution Matters Now

The enthusiasm around AI in healthcare is understandable. Physicians are busy, patients are better informed than ever and the pace of discovery keeps accelerating. But in our rush to adopt new technology, we risk abandoning the very safeguards that make modern medicine safe.

It would be unthinkable to prescribe chemotherapy based on a single press release, yet we risk doing something similar if we accept unverified chatbot outputs at face value. In oncology, where decisions can never be undone, shortcuts are dangerous.

Archibald Cochrane, the father of systematic literature reviews and modern, evidence-based medicine, taught us that without comprehensive, systematic evidence, we’re flying blind. Living evidence offers a path beyond the Cochrane method, combining the rigor of systematic reviews with the power of AI to ensure decisions are made on the totality of data, not the convenience of a single answer.

Why is this critical? Because trust is the currency of oncology. Patients need to trust that their oncologist is up-to-date and informed, just as oncologists need to trust that the evidence they consult is reliable. The systems they all rely on must earn that trust by proving they are comprehensive, transparent and continually refreshed.

Strengthening (Not Replacing) The Evidence Hierarchy

The future of oncology decision support must be built on living evidence. That means combining the speed of algorithms with the rigor of systematic reviews, creating a system where updates flow seamlessly into practice but are always filtered through an evidence-based lens.

AI should not replace the hierarchy of evidence. It should strengthen it.

If we succeed, oncologists will no longer face the impossible choice between speed and rigor. They will be equipped with tools that deliver both, empowering them to make decisions that are fast, safe and truly evidence-based. And in cancer care, that difference can be measured not in minutes saved, but in lives saved.