Pharma’s Biggest Missed AI Opportunity Is Living Evidence
This article was originally published by Anna Forsythe in Forbes on 29 January 2026. At this year’s J.P. Morgan Healthcare Conference, the largest healthcare investment symposium in the industry, it is no surprise that artificial intelligence featured prominently across a wide range of discussions from drug discovery, target identification and molecule design to clinical trial optimization and operational efficiency. AI applications are now fully embedded in each of these core pharmaceutical R&D strategies. What was far less visible, however, was the role AI could play in the systematic evaluation of scientific literature that underpins nearly every strategic, regulatory and reimbursement decision in modern pharma—or evidence generation. This omission is notable, in fact critical, at a time when AI-assisted evidence generation represents one of the industry’s most immediate and measurable opportunities for return on AI investment. Where AI Is Being Applied Today Current AI adoption in pharma tends to focus on highly visible areas closely associated with innovation, such as accelerating discovery timelines, improving trial execution and supporting internal productivity. These use cases already demonstrate long-term value and competitive differentiation. Still, the majority of high-stakes decisions in pharma do not hinge on discovery algorithms alone. Instead, they depend, as they have for decades, on structured assessments of existing evidence about disease burden and unmet need, historical endpoints and comparator performance, safety signals and evolving standards of care. These traditional assessments inform decisions ranging from trial design and asset valuation to regulatory strategy and pricing. Despite their importance, evidence workflows remain largely manual and highly fragmented. Navigating Using Outdated Maps A useful analogy is navigation. When trying to reach a destination, no one relies on an outdated static map (remember MapQuest?) printed years ago. Roads change, traffic patterns evolve and more efficient routes emerge constantly. Modern navigation relies on GPS systems that update continuously and reroute in real time. Pharma, however, still navigates critical decisions using static evidence reviews. Systematic Literature Reviews (SLRs), which have long been the gold standard for evidence synthesis, continue to be conducted as project-based exercises. This one-off approach is expensive and time-consuming, and the results are quickly outdated as new publications appear, guidelines are revised or new therapies enter the market. Once completed, these product-based exercises often live in disconnected siloes, requiring tweaking or partial reconstruction to support the next decision In a scientific environment that evolves daily, this reliance on static evidence is an increasingly poor and outdated solution, especially at a time when living, continuously updated maps offer a cost-effective solution. Increasing Regulatory And Reimbursement Pressure The limitations of static evidence are becoming more consequential as medical reimbursement systems evolve. In the United States, Medicare price negotiations are now in their third cycle under the Inflation Reduction Act. Medicare Part B drugs in oncology, for example, once largely insulated from pricing negotiations, are now fully in scope as of 2026. Manufacturers are expected to justify pricing not only based on evidence available at launch but also relative to new comparators and changing standards of care that continuously emerge over time. In Europe, the Joint Clinical Assessments (JCA), designed to create a unified, cross-national analysis of the efficacy of new drugs, raise needs and expectations further. Companies must consider all relevant comparators across all EU member states, address multiple subpopulations and present comprehensive, transparent evidence syntheses that can withstand scrutiny across multiple jurisdictions. In both settings, evidence is no longer assessed at a single point-in-time. At a time when regulatory and reimbursement demands are continuously being re-evaluated, conventional static snapshots struggle to keep pace with these demands as they evolve. The Cost Of Fragmentation Despite this pressure, evidence generation in pharma remains highly fragmented. Different functions (R&D, regulatory, health economics, market access, commercial) often commission their own literature reviews for similar questions. Reviews are modified, repeated and localized across regions, frequently by different external vendors and internal teams. Assumptions diverge. Institutional knowledge is lost. Redundancy accumulates. That redundancy is costly. A single high-quality SLR routinely costs six figures and takes months to complete. For global organizations with large portfolios, the cumulative cost of duplicated effort is substantial. More importantly, fragmented evidence increases the risk of inconsistency at moments when alignment matters most. Why General-Purpose AI Falls Short Generative AI tools like ChatGPT and chatbots are often cited as a solution. While useful for summarization or exploration, they are not designed to produce regulatory-grade evidence. Regulatory and reimbursement decisions require predefined methods, transparent inclusion criteria, traceable citations, reproducibility and alignment with established systematic review standards. Outputs must be auditable and defensible. General-purpose AI systems prioritize fluency over traceability and cannot replace structured evidence synthesis. In low-risk settings, speed may outweigh rigor. In regulated environments, rigor is non-negotiable. The Case For Living Evidence The alternative is a shift from static reviews to living evidence. A living evidence approach treats evidence as shared infrastructure rather than as a series of isolated projects. Evidence is continuously updated as new data emerges, centrally governed, and organized by indication, population, comparator and endpoint. Updates are incremental rather than repetitive, and changes are transparent. Functionally, this mirrors how GPS systems work: always current, responsive to new information and capable of supporting multiple routes and decisions from the same underlying map. Such an approach could support better decision-making across the product life cycle, reduce duplication and improve consistency under increasing regulatory and reimbursement scrutiny. Why The Shift Has Been Slow If the potential benefits are clear, why has adoption been limited? One reason is organizational structure. Evidence budgets are typically allocated by function, by brand and by project. Living evidence, by contrast, is shared longitudinally and is cross-functional. Adoption requires investment at an enterprise level rather than ownership by a single team. Living evidence is also, by its nature, less visible than discovery breakthroughs or novel technologies. Yet visibility and return are not the same. As AI continues to reshape pharma, the most impactful opportunities may lie not only in discovering new drugs faster, but in navigating the increasingly complex evidence landscape more intelligently. In an industry under growing pressure to
Why Chatbots Aren’t Enough In Oncology
This article was originally published by Anna Forsythe in Forbes on 13 November 2025. 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. Anna Forsythe Anna Forsythe, pharmacist & health economist, is the Founder & CEO of Oncoscope-AI
From Evidence To AI: Why The Future Of Oncology Decision Support Must Be Built On Living Evidence
This article was originally published in Forbes on 18 September 2025. 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
From Cochrane To Chatbots: Why Evidence Matters Now More Than Ever In The AI Era
This article was originally published in Forbes on 02 September 2025. In oncology, discovery is moving at warp speed. Hundreds of new studies are published each week, sometimes more than a dozen in a single day. For patients and providers, this fire hose of information could equate to life-saving breakthroughs—a new biomarker, a novel dosing schedule or a survival-prolonging treatment. But it also equates to risk. Misinterpret a flawed trial, overtrust anecdotal experience or trust the wrong person, and patients are left at the mercy of ineffective or even harmful treatments. And so, as this deluge of data overwhelms us with such velocity, the imperative is ensuring that decisions are well-informed by precise, whole and transparent evidence. That all starts with one man: Archibald Leman Cochrane. Archibald Cochrane: The Original Evidence Disruptor Archibald Cochrane was a British physician and epidemiologist who served as a prisoner-of-war doctor in World War II. With effectively no medicines at his disposal, he watched patients suffer because care was based more on habit than proof. After the war, he became an outspoken advocate for a revolutionary principle: Medicine must be based on evidence that has been tested and proven, not on practice or expert opinion. In 1972, he published Effectiveness and Efficiency: Random Reflections on Health Services, a text that shook the medical establishment through criticism of its reliance on anecdotes. He argued that randomized controlled trials (RCTs) were required to determine whether treatments were effective and called for doctors and patients to be presented with objective summaries of all pertinent evidence. His vision inspired the Cochrane Collaboration, founded in 1993, which remains the global leader in producing systematic reviews. Pre-Cochrane, the concept of a controlled clinical trial did not exist. He was the one who established the use of controlled clinical trials, which have now become the gold standard for evaluating new treatments. Building On Solid Ground Cochrane’s work gave us the pyramid of evidence, often used to illustrate the hierarchy of reliability. The higher you climb in the pyramid, the stronger the foundation for life-and-death decisions. Why Systematic Reviews Save Lives In oncology, where treatment options evolve daily, systematic reviews are essential. A single study, whether positive or negative, rarely tells the full story. Systematic reviews, however, consider the entire body of evidence and account for consistency, quality and nuance. Nearly every modern guideline (from the World Health Organization to the FDA) requires systematic reviews as the foundation of clinical recommendations. These methods are necessary because lives depend on them. When Chatbots Pretend To Be Experts We’ve now entered the AI era, where large language models and chatbots can generate fluent answers to intricate medical questions in seconds. To overwhelmed oncologists confronting a deluge of literature, this might look like salvation. But these tools don’t follow the hierarchy of evidence. They don’t methodically scan all the studies, grade the quality of trials or reveal how they arrived at their conclusions. They generate responses based on text patterns that are sometimes accurate, sometimes incomplete and sometimes entirely fabricated. I’ve heard an example just last week from one of my medical colleagues: An AI tool produced a reference under his name for an article he had never written. It was a perfect illustration of how these systems behave like an overeager student desperate to provide an answer, even if that means inventing one. A chatbot’s “best guess” could mean proposing a treatment that introduces unnecessary toxicity to patients or worse, missing a well-documented new trial that would improve survival. The irony is that most of us would never hand over the task of booking a flight to a chatbot without carefully double-checking everything—the departure point, the destination city, the time of day. Yet somehow, when it comes to medical treatment, many are willing to accept incomplete or outdated chatbot outputs at face value. That disconnect should give us pause. Fluency Isn’t Truth One of the greatest risks of AI in medicine is that it sounds authoritative. Chatbots excel at fluency, but fluency is not the same as truth. At this time, AI simply isn’t ready for the weight we’re placing on it. Too many people are so captivated by what it can do that they forget the most basic principle of science and medicine. You must double-check the information. That fascination is dangerous. It encourages blind trust in answers that may be incomplete, misleading or outright wrong. If we allow fluency to masquerade as reliability, medicine risks sliding backward to the pre-Cochrane era, when anecdotes and authority carried more weight than solid data. Smarter Together I’m not saying that AI has no place in evidence-based medicine. Far from it. Let machines handle the tedious but essential work of scanning thousands of papers, formatting endless references and keeping reviews continuously updated as new trials are published. These are the repetitive housekeeping tasks that often slow researchers down, yet must be done with precision. I often compare it to household chores. AI should be doing the vacuuming so humans can spend their time on meaningful work. That’s exactly how my team approaches it. We’ve built a model that uses 36 different AI systems, integrated under the supervision of PhD-level experts, to make systematic reviews dramatically faster without compromising accuracy. Don’t outsource judgment to machines. Give human experts more bandwidth to do what only they can do: Interpret the evidence, weigh the nuances and make the right decisions for patients. Evidence Still Rules Cochrane warned half a century ago that a great deal of medicine lacked solid evidence. His admonition is even more urgent today. With AI, the danger is overconfidence in fluent machines that sound convincing but aren’t built on rigorous evidence. AI must serve evidence, not replace it. Healthcare leaders, policymakers and clinicians need to insist on transparency, rigor and comprehensiveness as absolute necessities. Because in oncology, and medicine in general, lives are at stake. And regardless of how much the tech advances, one thing will always be true: Evidence still rules. This article is