Why Chatbots Aren’t Enough In Oncology

In the fast-moving world of oncology, clinical decision making has never been more complex—or more urgent. Thousands of new cancer studies are published every month, each with findings that could alter treatment pathways or reshape guidelines.

For oncologists, research teams, hospitals and payers, the challenge isn’t simply finding information—it’s finding the right information, quickly and confidently.

The market is full of AI-powered tools promising help. Many rely on large language models (LLMs) and chatbot-style interfaces that offer answers in conversational form. The appeal is obvious: type in a query, get an instant response. But in oncology—where the stakes are measured in survival rates—ease of use is not enough.

Why Decisions Are So Complicated

Consider a patient with late-stage lung cancer whose tumor harbors a rare genetic mutation. This is the reality of modern oncology, which offers targeted therapies for specific genetic mutations. The physician must weigh the disease stage, prior therapies, co-morbidities and preferences. They must verify whether a targeted therapy exists, check FDA approvals, review guideline recommendations and explore whether a clinical trial could provide access to the latest investigational drug.

This involves combing through journal articles, conference abstracts and regulatory documents—each a piece of the puzzle. There is no “one-size-fits-all” solution in an era where targeted therapies produce individualized pathways.

A chatbot might return a single response based on an editorial or opinion piece it “remembers,” presenting it as definitive. The nuance—say, that another trial showed limited efficacy in heavily pre-treated patients, or that guidelines recommend a different approach after immunotherapy—can easily be lost.

The Gold Standard: Systematic, Comprehensive, Expert-Vetted

Medicine relies on the hierarchy of evidence. At its peak sit systematic reviews and meta-analyses—studies that evaluate and synthesize all available research. Regulatory agencies like the FDA, as well as organizations such as the American Society of Clinical Oncology (ASCO) and the National Comprehensive Cancer Network (NCCN), have long required systematic reviews as the foundation for guidelines and approvals.

An effective oncology decision support tool must therefore also be systematic, with transparent, reproducible searches of all relevant research. It must be comprehensive, drawing from peer-reviewed journals, guidelines, conference abstracts and regulatory filings. It must be robust in distinguishing between high-quality randomized trials and weaker evidence.

Just as importantly, it must update continuously (ideally daily) to reflect the latest research. Medical decisions based on outdated knowledge risk outdated care. Trained oncologists and other specialists can ensure the conclusions are accurate.

Where Chatbots Fall Short

I’ve found that even the most advanced LLMs cannot meet those criteria. Their weaknesses are structural. Built for speed and limited in transparency, chatbots rarely disclose their sources. They may omit references entirely, and without systematic searching, key studies are often missed. Their datasets often exclude recent guideline updates or pivotal conference results.

Moreover, as black boxes reliant on opaque algorithms, chatbots provide no evidence grading. An editorial can appear with the same weight as a phase three trial. They may even fabricate references—so-called and largely reported on “hallucinations.” In my experiments, queries have sometimes led to outdated and false information. In one instance, a chatbot cited a non-existent study to me.

Transparency of the dataset is critical, especially in a field where thousands of new studies are published each month. Using AI on an iPhone to call a taxi is convenient, but in oncology, where each decision can alter survival, these shortcomings aren’t just inconvenient; for a patient with a rare mutation, it can mean the difference between hope and harm.

Beyond Oncology: A Universal Lesson

The risk of relying on incomplete or unverified evidence isn’t unique to cancer care. In finance, successful portfolio managers don’t bet other people’s money on one analyst’s hunch; they use meta-analyses of market data. In aviation, flight safety depends on synthesizing thousands of reports and assessments. No pilot would fly based on a chatbot’s opinion about turbulence. In public health, vaccine rollouts depend on systematic reviews of global trial data, not a handful of preliminary studies.

Across industries, convenience cannot replace rigor. The ideal system in oncology—and other data-driven fields—is an expert-driven partner that can provide trustworthy insights.

The Human and AI Solution

Despite certain limitations in its use within chatbots, the beauty of AI is how it can scan millions of documents in seconds, helping detect patterns and surface relevant studies. With the mountain of data produced every day, that feature is undeniably important. But human experts are needed to bring judgment, clinical context and critical thinking to the mix.

I think the winning model is a living systematic literature review (SLR)—continuously updated by AI, structured through reproducible methodology and validated by experts. (Disclosure: I lead an AI-assisted oncology evidence platform this type of approach.)

LLMs power today’s chatbots—but they can also hallucinate or misread complex evidence. The approach I champion still uses LLMs, but with continuous expert oversight. Every data point should be verified by trained analysts and clinicians, eliminating hallucinations and ensuring full transparency.

That said, I find this hybrid model effective but demanding. It requires capital, expertise and time to build for each cancer type. And even then, people still prefer someone or something they can talk to. The future may lie in combining both approaches—a conversational chatbot connected to a rigorously curated, expert-verified database.

But by working to overcome these hurdles, pharmaceutical companies, payers and healthcare networks stand to benefit as much as clinicians. Beyond oncology, systematic, AI-augmented evidence synthesis has the potential to streamline internal decision making, support value-based care initiatives, strengthen negotiations and reduce duplication across research teams.

The Bottom Line

AI is here to stay, and its potential in healthcare is enormous. But in oncology—and in every field where lives or livelihoods are at stake—it must be deployed with discipline. Chatbots may offer instant, conversational answers, but approachability is not the same as reliability.