OncoDaily Interview: Could Oncoscope-AI Save Clinicians Hours – and Spare Patients Side Effects?
It’s good to talk about our đđ”đ sometimes. Thatâs why we appreciate Emma Ter-Azaryanâs interview so much. Not just for her insightful questions, but for giving us an opportunity to publicly reflect on đđ”đźđ đąđ»đ°đŒđđ°đŒđœđČ-đđ đșđČđźđ»đ đđŒ đđ. In this interview with OncoDaily, Oncoscope-AI Founder & CEO Anna Forsythe shares what drives her personally, an example of an oncologist using the tool and the impact it had, and the frustration of seeing people we love treated with chemotherapy because their doctors weren’t aware of updates in the guidelines and the research behind them. You can watch the full interview âCould Oncoscope-AI Save Clinicians Hours – and Spare Patients Side Effects?â here: From OncoDaily: In this episode of OncoDaily TV, host Emma Ter-Azaryan speaks with Anna Forsythe, CEO & Founder of Oncoscope-AI, to unpack how clinicians can cut through oncologyâs data overloadâFDA labels, guidelines, congress abstracts, and papersâand get to the right evidence in just a few clicks. What youâll learn: â What OncoScope AI is (in simple terms): a clinician-friendly âExpedia for evidenceâ that pulls from major medical databases, guidelines, regulatory updates, and congress outputsâcross-linked in one place.â Essential vs. Edge: two workflowsâpatient-first decision support vs. deep-dive topic exploration (e.g., ADCs in lung cancer, mutation-specific updates).â Power features: clickable disease maps, filter by congress (ASCO, World Lung, etc.), tumor-board prep, and one-click prior-auth reports with citations.â Real-world impact: how a brand-new FDA approval surfaced that week and helped a patient access a better-tolerated therapy sooner.”
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? Itâs rarely an easy choice. Physicians must weigh multiple levels of information, such as the patientâs 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 doesnât always align. All of this is tedious and time-consuming. Time that most oncologists donât 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 evidenceâthe decisions between the right and wrong treatmentâcan 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 evidenceâthe 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 informationâand the tedium of just reading it all, let alone evaluating itâitâs 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 thatâs where the danger lies. Chatbots donât conduct systematic reviews. They canât distinguish between high-quality trials and weak studies. They donât 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 youâre 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 resistanceâmaking âtried and trueâ 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 traderâs 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 arenât 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 evidenceâbacked 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 physicianâs desktop. This model is rigorous yet addresses medicineâs biggest bottleneckâtime. 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 donât 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 conductorâthe oncologistâcan 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 doesnât 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
Systematic Literature Review Versus Chatbots: Why In Oncology, Itâs Not a Choice
In the age of artificial intelligence, speed is often mistaken for rigor. Nowhere is this more dangerous than in oncology, where treatment decisions can mean the difference between life and death. Some technology companies tout âsystematic literature reviewsâ (SLRs) generated in minutes by chatbots that claim to scan thousands of papers across the internet. The appeal is obvious: quick, accessible, and seemingly comprehensive. But in reality, these outputs are neither systematic nor reliable. For oncologists, payers, and researchers, understanding the distinction between a true SLR and a chatbotâs surface-level search is not just academicâitâs essential. The Gold Standard: What a True SLR Involves Systematic literature review is the gold standard for evidence synthesis in medicine. It is the foundation of evidence-based practice because it minimizes bias, ensures completeness, and enables decisions to rest on the strongest available science. A rigorous SLR begins with a protocol: a predefined roadmap that frames the research question and methods. It requires carefully constructed search strategies, typically using combinations of keywords and controlled vocabulary, to capture every relevant publication across peer-reviewed databases. The process doesnât stop there. Grey literatureâsuch as abstracts from scientific conferencesâmust also be included, since cutting-edge oncology data often appears in congress presentations long before it reaches a journal. From there, studies undergo multi-step screening against strict inclusion and exclusion criteria: patient population, interventions, comparators, outcomes, and study design (the classic PICO framework). Each selected paper is then critically appraised for quality and relevance. Only after this painstaking filtering does the work of synthesis and interpretation begin. This is not a clerical exercise. It requires advanced training, sound judgment, and clinical insight to evaluate conflicting results, contextualize findings, and translate them into actionable conclusions. Why Chatbots Fall Short Chatbots, even those powered by large language models (LLMs), cannot replicate this process. At best, they skim unstructured text. At worst, they hallucinate citations or omit critical studies. They lack protocols, inclusion criteria, appraisal of study quality, or a transparent audit trail. What results may look convincing on the surfaceâbut lacks the depth and reliability required in oncology. When a chatbot says it can âreview 1,000 studies in seconds,â what itâs really doing is producing a text summary based on whatever sources it happens to ingest. There is no guarantee that the sources are peer-reviewed, complete, current, or even real. That is not an SLR. Why It Matters in Oncology Oncology is not forgiving of shortcuts. Selecting the right therapy for a patient is an exercise in precision: choosing between regimens, sequencing targeted therapies, balancing efficacy and toxicity, and staying current on breakthroughs that can extend survival or improve quality of life. In this context, incomplete, outdated, or fabricated evidence isnât a minor flawâitâs a threat to patient safety. The rigor of a systematic literature review is not a ânice to haveâ; itâs the foundation for making responsible decisions in cancer care. The Path Forward AI absolutely has a role to play in evidence synthesis. When paired with human expertise and transparent methodology, it can accelerate searches, streamline screening, and reduce administrative burden. But AI must serve the processânot replace it. In oncology, the choice isnât between a chatbot and a systematic literature review. Itâs between cutting corners and saving lives. The stakes are too high for anything less than living, rigorous, and human-guided evidence. Anna Forsythe Anna Forsythe is the Founder and President of Oncoscope-AI, the first platform to bring together real-time oncology treatment data, clinical guidelines, research publications, and regulatory approvals â all in one place, just like Expedia for cancer care. Available free to oncology professionals worldwide, Oncoscope-AI is redefining how cancer care information is accessed and applied.
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 breakthroughsâa 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 Cochraneâs 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 Weâve 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 donât 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. Iâve 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 everythingâthe 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 Isnât 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 isnât ready for the weight weâre 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 Iâm 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. Thatâs exactly how my team approaches it. Weâve 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. Donât 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 arenât 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
The Healthy Enterprise Podcast: AIâs Role in Modern Oncology – A New Era of Evidence-Based Cancer Care
Our very own Founder & CEO Anna Forsythe was recently on The Healthy Enterprise podcast to talk about Oncoscope-AI and how our practice-changing platform delivers real-time treatment insights, clinical guidelines, and research to support oncologists in an ever-evolving cancer landscape. Watch on YouTube: Listen on Spotify: Listen on Pocket Casts: From The Healthy Enterprise Podcast: In this episode of The Healthy Enterprise, Anna Forsythe, founder and CEO of Oncoscope-AI, shares her journey from manually processing oncology data to creating an AI-powered platform that delivers real-time treatment insights, clinical guidelines, and research to support oncologists in an ever-evolving cancer landscape. She discusses the importance of reliable evidence in decision-making, the positive response from physicians using the free platform, and the role of systematic data reviews in building trust and efficiency. Beyond the technology, Anna reflects on her entrepreneurial path, the challenges and opportunities of leading in healthcare, and the importance of cultivating talent, mission-driven culture, and visionary leadership. The conversation concludes with her insights on business models, community impact, and the future of AI in advancing cancer care. Chapters: 00:00 Introduction to Oncoscope-AI and Anna Forsythe03:04 The Need for Real-Time Oncology Data05:59 Transitioning from Manual to Automated Data Processing09:11 The Role of AI in Oncology12:13 Feedback and Response from Healthcare Professionals14:45 Data Sources and Systematic Reviews in Oncology18:02 Future Developments and Expanding Services21:53 AI in Research: Balancing Trust and Efficiency25:52 Business Model and Revenue Streams29:44 Personal Journey and Altruism in Business32:56 Challenges and Opportunities in Healthcare35:34 Leadership and Team Dynamics39:29 Visionary Leadership and Future Aspirations Guest Information: Takeaways: Keywords: Oncology, AI, healthcare, data processing, clinical guidelines, cancer treatment, healthcare technology, real-time data, systematic reviews, healthcare professionals, AI, healthcare, business model, cancer treatment, research, leadership, altruism, revenue streams, technology, innovation Enjoy this clip from the episode:
Oncoscope-AI Launches Edge for Leaders Shaping the Next Standard of Cancer Care
This press release was originally published to EINPresswire on 28 August 2025. NEW YORK, NY, UNITED STATES, August 28, 2025 /EINPresswire.com/ — Oncoscope-AI, a pioneer in real-time oncology evidence solutions, today announced the launch of Oncoscope Edge, a premium decision-support platform designed for oncology leaders and decision-makers who demand comprehensive, flexible, and actionable insights. In todayâs fast-moving oncology landscape, specialists face an overwhelming volume of dataâfrom thousands of new clinical trials to rapidly evolving guidelines and regulatory updates. Oncoscope Edge empowers oncologists, fellows-in-training, researchers, and educators with advanced tools to find, filter, and synthesize the evidence that matters most. âOncoscope Edge is built for the leaders shaping the next standard of care,â said Anna Forsythe, PharmD, MBA, Founder and CEO of Oncoscope-AI. âWhether you are preparing for a congress talk, conducting a scientific project, guiding a tumor board, or training the next generation of oncologists, Edge delivers clarity from complexityâinstantly.â Advanced Features of Oncoscope Edge include: With these capabilities, Oncoscope Edge goes beyond surface-level searches, available now in Oncology-AIâs Essential tool, to deliver deeper, customizable searches and user-generated reportsâenabling oncology leaders to stay ahead in a field where evidence evolves daily. About Oncoscope-AI Oncoscope-AI is the first real-time oncology information platform integrating treatment data, guidelines, peer-reviewed publications, and regulatory approvals. By combining AI-powered systematic literature reviews with expert human validation, Oncoscope ensures that oncology decision-making is grounded in the most current and reliable evidence. Sign up for a free license key (verified health care professionals only) or to receive a demo.
In the AI Era, Are Doctors Still at the Cutting Edge of Medicine? Oncoscope-AI Founder Anna Forsythe Asks
This article was originally published in Success Magazine. Artificial intelligence (AI) has revolutionized industries, from finance and logistics to advertising and agriculture. It has offered new efficiencies, insights and capabilities that were seemingly unimaginable just a few decades ago. Healthcare is no exception. In oncology, especially, where the volume of emerging research and the complexity of personalized treatments are accelerating, intelligent tools have become integral. Oncoscope-AI, an advanced oncology intelligence platform, was created to respond to this need. The catalyst behind the founding of the company was a profound question: How can AI help doctors keep up with and apply the flood of cancer breakthroughs to improve real-world patient care? The company has made answering that question its mission. Refining the approach to cancer treatment Anna Forsythe, founder of Oncoscope-AI, believes the cancer landscape today is vastly different from what it was 10 years ago. This is particularly evident in breast and lung cancer, two of the most commonly diagnosed cancers worldwide. âBefore, a diagnosis of breast cancer meant a standard treatment regimen for nearly all patients. Individual biological differences didnât matter. But our understanding has evolved,â Forsythe explains. Indeed, what was called âbreast cancerâ is now understood as multiple subtypes. Each has its own biological behavior, prognosis and treatment response. Similarly, lung cancer has splintered into a web of genetically and immunologically distinct conditions. The era of the one-size-fits-all approach, dominated by blanket chemotherapy, surgery and radiation, is fading. A more refined model of cancer treatment has emerged in its place. Forsythe states that todayâs oncology is driven by two approaches. One is immunotherapy, which amplifies the bodyâs immune response to fight the disease from within. The other is targeted therapy, which focuses on specific biomarkers or genetic mutations within the tumor itself. Theyâre usually used in tandem with the goal of turning terminal-stage cancers into manageable, chronic conditions. Consider the arrival of CDK4/6 inhibitors palbociclib, ribociclib and abemaciclib and how they changed the landscape for breast cancer treatment. According to research based on trials, CDK4/6 inhibitors are considered revolutionary in breast cancer treatment and have played a key role in slowing progression. Progression-free survival, or how long a patient can live without the cancer worsening, has become the new benchmark of success. New challenges for physicians This progress introduces a new kind of pressure, however. âWith every new biomarker discovered, every new targeted therapy or immunotherapy approved, the challenge for physicians multiplies,â Forsythe states. âHow can any oncologist stay up to date with thousands of new studies, frequent regulatory updates and a deluge of clinical data?â At a single oncology conference, countless new abstracts may be released in just one cancer type. The expectation is that doctors will somehow process all of this while continuing to see patients, manage treatments and fulfill legal requirements for ongoing medical education. In theory, continuous medical education (CME) should help doctors stay current. In practice, itâs difficult for health professionals to retain all the information. Enter Oncoscope-AI Oncoscope-AI has emerged to empower healthcare professionals. Forsythe, a pharmacist, health economist and former pharmaceutical executive, designed it to bridge the divide between cutting-edge research and practical clinical application. The platform scans thousands of oncology publications using proprietary AI, filters them through an evidence-based framework and presents only the most clinically actionable insights. According to Oncoscope-AI, these findings are reviewed and contextualized by a human research team before being published in the system. The impact of Oncoscope-AI is best felt in patient stories. A friend Forsythe met while traveling was diagnosed with metastatic breast cancer. She had undergone traditional chemotherapy and surgery that left her physically and emotionally drained. âShe was then prescribed one of the breakthrough CDK4/6 inhibitors. Her life changed. She regained energy, joy, and time,â Forsythe shares. However, when her cancer began to progress again, her oncologist defaulted to standard chemotherapy, believing there were no more options. There were. However, the doctor didnât know. Forsythe sent her friend research summaries and articles through Oncoscope-AI, which she later shared with her doctor. She was then prescribed a new-generation therapy. âSheâs thriving now,â says Forsythe. âShe just returned from a trip to France and is booking another.â Aiming to help health professionals and patients thrive This story raises a question. How many more patients are out there who could live longer, better lives if only their doctors had access to the right information at the right time? âThe problem is not the doctors. Itâs the data overload,â Forsythe emphasizes. âThe solution isnât replacing physicians with AI, but equipping them with AI tools that distill noise into knowledge and confusion into clarity.â Oncoscope-AI is striving to do just that. Itâs working to help oncologists across the globe turn the latest science into tangible outcomes for patients in the exam room, not months later when guidelines catch up. This article is for informational purposes only and does not substitute for professional medical advice. If you are seeking medical advice, diagnosis or treatment, please consult a medical professional or healthcare provider.
From Cochrane to Chatbots: Have We Forgotten the Evidence?
If medicine had a godfather of evidence, his name would be Archie. Archibald Leman Cochrane, that is. Cochrane wasnât just a physicianâhe was a rebel against tradition. In the 1970s, he looked around at the medical world and said, essentially: âToo much of what we do is based on habit and authority, not actual proof.â He proposed a radical idea: letâs rank evidence by reliability. At the bottom sat case reports and expert opinionsâinteresting, but hardly solid. Above that came observational studies, then randomized controlled trials (RCTs). And sitting proudly at the top of the pyramid? Systematic literature reviews (SLRs): structured evaluations that capture all the studies, critique their quality, and synthesize their findings. That hierarchy became the foundation of what we now call Evidence-Based Medicine (EBM). Why Systematic Reviews Matter Systematic reviews arenât academic busywork. Theyâre the reason guidelines from the World Health Organization, NICE, and the American Society of Clinical Oncology (ASCO) are trustworthy. Theyâre why the FDA demands comprehensive, systematic evidence before approving a therapy. The principle is simple: no single trial tells the full story, but when you put the whole picture togetherâcarefully, transparently, reproduciblyâyou can make decisions that change lives. But Have We Forgotten Cochrane? Fast forward to today. AI chatbots can answer clinical questions in seconds. They sound authoritative, but they donât follow Cochraneâs hierarchy. They donât systematically review literature. They donât grade evidence for quality. And they definitely donât show their work. In oncology, where new studies are published daily, this is not a minor issue. A chatbot that casually cites one studyâor worse, invents a citationâcould mislead a physician into recommending something harmful, or missing a life-saving option. Itâs like we built the evidence pyramid over decades, and now, dazzled by shiny AI, weâre forgetting why we built it in the first place. The Way Forward The future of evidence isnât abandoning systematic reviews. Itâs making them living: constantly updated, rigorous, and transparent. AI does have a role to play hereânot as a chatbot dispensing unverified answers, but as a tool that accelerates and augments systematic reviews. Thatâs exactly what weâre building at Oncoscope: living SLRs, human-vetted and PhD-curated, augmented by AI. Reliable, current, and ready to support the most important decisions in oncology. Because in cancer care, evidence isnât an academic debate. Itâs a matter of life, harm, or hope. So the question is: are we going to let chatbots distract us from Cochraneâs lessonâor use AI to fulfill it? Anna Forsythe is the Founder and President of Oncoscope-AI, the first platform to bring together real-time oncology treatment data, clinical guidelines, research publications, and regulatory approvals â all in one place, just like Expedia for cancer care. Available free to oncology professionals worldwide, Oncoscope-AI is redefining how cancer care information is accessed and applied. A clinically trained Doctor of Pharmacy (PharmD), Anna also holds a Masterâs in Health Economics and Policy from the University of Birmingham (UK) and an MBA from Columbia University. She previously co-founded Purple Squirrel Economics (acquired by Cytel in 2020) and led Global Value and Access at Eisai Pharmaceuticals, following earlier roles at Novartis and Bayer in clinical research and health economics.