A new generation of cancer researchers will need to master both traditional oncology and artificial intelligence to keep pace with how the field is evolving. Weill Cornell Medicine investigators are launching a groundbreaking dual-track training program designed to create what they call "bilingual" scientists, fluent in cancer biology and AI large language models (LLMs), which are AI systems trained on vast amounts of text data to understand and generate human language. The initiative addresses a critical workforce gap as pharmaceutical companies increasingly deploy AI tools to design clinical trials, monitor drug safety, and meet regulatory requirements .

Why Is AI Expertise Suddenly Essential for Cancer Researchers?

Cancer research is drowning in data. Hospitals and research centers now generate massive datasets spanning genomics, imaging, and clinical outcomes from patient tumors. The problem is that no single researcher can manually sift through all this information to identify the best treatment options for an individual patient. That's where AI comes in. Large language models can process this knowledge at scale and help oncologists match patients with personalized therapies .

"AI is transforming our world, how we work, live and conduct research. Cancer is uniquely positioned to benefit, because we now have massive datasets spanning genomics, imaging and clinical outcomes that AI can finally put to use," said Dr. Olivier Elemento, director of the Englander Institute for Precision Medicine and a professor of systems and computational biomedicine.

Dr. Olivier Elemento, Director of the Englander Institute for Precision Medicine, Weill Cornell Medicine

The vision is straightforward but ambitious. When a patient receives a cancer diagnosis, researchers would molecularly characterize the tumor, feed that data into AI systems, and use the AI's analysis to identify available therapies and customize treatment plans. This approach could dramatically accelerate the move toward precision medicine, where treatments are tailored to each patient's unique tumor profile .

How to Build a Workforce Ready for AI-Powered Cancer Research?

Weill Cornell's solution involves creating a structured training program with specific components designed to bridge the knowledge gap between computational science and clinical oncology:

• Dual Mentorship Model: Each trainee is assigned two mentors, one computational and one clinical oncologist or cancer biologist, ensuring they gain expertise in both domains simultaneously.
• Cross-Training Clinical and Computational Scientists: The program trains clinical oncology fellows and cancer research scientists in AI tools, while also teaching computational scientists the fundamentals of cancer biology.
• Ethical AI Supervision: Trainees learn how to properly oversee AI tools to avoid ethical breaches, protect patient privacy, and detect inaccurate or fabricated results, including identifying fake papers with synthetically generated data.

The program is already being built, and the team is pursuing grants to fund it. Weill Cornell is well-positioned to lead this effort because it already operates "AI clinics" where AI-fluent investigators train colleagues in person and via video conference. An upcoming workshop will focus on securely using AI to analyze medical records .

"The moment a patient gets a cancer diagnosis, we would like to molecularly characterize the tumor, use all the knowledge that is out there, which is what AI large language models do, and see what therapies might be available and how we can customize treatment," explained Dr. Paraskevi Giannakakou, a professor of pharmacology in medicine and member of the Sandra and Edward Meyer Cancer Center.

Dr. Paraskevi Giannakakou, Professor of Pharmacology in Medicine, Weill Cornell Medicine

What Are the Risks of Deploying Powerful AI Tools Without Proper Training?

The stakes are high. Dr. Elemento, who describes himself as a "super user" of AI for research, emphasized that powerful tools demand careful hands. There have already been examples of fake scientific papers published with synthetically generated data, a cautionary tale for researchers who don't understand how to validate AI outputs. Training the next generation to ethically use these tools is not optional; it's essential to maintaining the integrity of cancer research .

The urgency is real. Pharmaceutical companies are already moving fast, deploying AI agents to design clinical trials and monitor drug safety. Researchers who lack AI fluency risk being left behind professionally. Dr. Giannakakou stressed that federal, private, and institutional foundation investments in training programs are critical to prevent an entire generation of scientists from falling behind .

"We need federal, private and institutional foundation investments in training programs to create AI-cancer biology bilingual scientists like we are creating at Weill Cornell Medicine. We do not want to leave a generation of scientists behind," emphasized Dr. Giannakakou.

Dr. Paraskevi Giannakakou, Professor of Pharmacology in Medicine, Weill Cornell Medicine

Weill Cornell's broader institutional support strengthens this initiative. The institution's "AI to Advance Medicine" initiative aims to provide the infrastructure and services needed to support safe, effective AI adoption across faculty, staff, and students. Combined with the institution's robust expertise in cancer biology and clinical medicine, this positions Weill Cornell to ensure that AI is answering the most important questions for patients with cancer, not just optimizing metrics .

The dual-training model represents a fundamental shift in how cancer researchers will need to be educated. It's no longer enough to understand oncology or AI separately; the future belongs to researchers who can navigate both worlds fluently and responsibly.