How AI Is Influencing Oncology Clinical Trials and Cancer Care

Cancer continues to be one of the most significant health challenges globally, prompting an ongoing pursuit of innovation in detection, treatment and patient care.

Projections from the American Cancer Society indicate that in 2025 the US will see more than 2 million new cases of cancer, not counting nonmelanoma skin cancers, and over 618,000 people will die from the disease.

The need for more precise, efficient and personalized oncology solutions has never been greater.

AI in oncology clinical trials — Nate Lear
Head, Advanced Analytics and
Clinical Trial Efficiency
AstraZeneca

Recent advances in AI are ushering in a new era for cancer care. AI is transforming screening, drug discovery and clinical decision support. These innovations promise earlier cancer detection using improved imaging and liquid biopsy techniques. They can also simplify clinical trial design, optimize patient recruitment and customize treatments based on individual genetic profiles.

Exploring the intersection of AI and oncology allows us to better understand how emerging technologies are transforming cancer care.

In this Xtalks Clinical Edge™ interview, Nate Lear, Head of Advanced Analytics and Clinical Trial Efficiency at AstraZeneca, shared his vision on the evolving role of AI in oncology.

From early detection to optimizing clinical trial processes, Nate’s insights emphasize both the transformative potential of AI and the challenges associated with incorporating these technologies into healthcare workflows.

XTALKS CLINICAL EDGE™: Issue 4 — Interview with AstraZeneca’s Nate Lear

Xtalks Clinical Edge™ is a magazine for clinical research professionals and all who want to be informed about the latest trends and happenings in clinical trials. This magazine immerses you in a world where industry leaders, patient advocates and top researchers converge to provide the most insightful perspectives on clinical trials.

A Journey from Manufacturing to Oncology

Nate’s journey from manufacturing to oncology began with his early experiences as a biomedical engineer where he first encountered the limitations of routine processes. Reflecting on his beginnings in vaccine manufacturing, he remembered spending long hours on the inspection line and dealing with extensive documentation.

He explains that he “wanted something more creative,” which prompted him to investigate how data could be used to foster meaningful change.

As Nate entered the field of oncology, he found that data science provided the chance to reveal insights that could directly influence patient care.

He acknowledged that although healthcare providers always prioritize patients’ best interests, not every patient receives optimal treatment. This insight started his passion for using data to identify gaps and enhance outcomes.

“When we share insights derived from data,” he said, the process catalyzes collaboration and decision-making, ultimately leading to tangible improvements in patient care.

Reflecting on this career shift, Nate described his work in oncology as “the most rewarding experience I’ve had within my career.” His switch highlights the significant role of data — not only in refining operational processes but also in transforming lives by enabling more effective, personalized treatments.

Breaking New Ground in Cancer Care with AI

During the interview, Nate made a striking comparison between historical scientific breakthroughs and today’s AI innovations. He recalled watching a video featuring Neil deGrasse Tyson, in which Tyson praised Isaac Newton for his innovative contributions at a young age. Nate suggested that just as Newton redefined science, AI is now paving the way for advancements in cancer care.

Nate highlighted three key areas where AI is pushing the boundaries:

Drug Discovery: AI-driven analytics are speeding up the discovery of new molecules. By enhancing research and lowering associated costs, AI is facilitating the more efficient introduction of innovative treatments to the market.
Screening and Detection: Advanced imaging techniques and liquid biopsies are increasingly leveraging AI to detect cancer at earlier stages and with greater accuracy. The rapid identification of disease, which often surpasses human capabilities, is facilitating more accessible and community-based screening initiatives.
Clinical Decision Support: At the point of care, AI tools can analyze patient data — ranging from medical history to genetic profiles — to recommend personalized treatment options. This approach not only aids healthcare providers but also ensures that patients receive care tailored to their unique needs.

These innovations demonstrate that AI is not merely a new tool; it is actively redefining the parameters of scientific inquiry and patient care.

AI-Enhanced Strategies for Clinical Trials

At AstraZeneca, AI plays a key role in both the design and execution of clinical trials. Nate described several modern applications:

Protocol Generation and Optimization: By using generative AI, the team can draft trial protocols based on vast datasets, including internal records and external trial designs. This speeds up protocol development and allows for real-time adjustments based on evolving needs.
Refining Patient Selection: Advanced analytics help identify potential recruitment challenges by simulating the impact of inclusion and exclusion criteria. This helps ensure that clinical trials can enroll a representative sample of the disease population and may lead to more robust and generalizable results.
Predictive Modeling: Machine learning models are under development to forecast patient recruitment rates and enhance site selection. This proactive strategy facilitates real-time adjustments, ensuring that studies achieve their recruitment targets while prioritizing diversity and inclusion.

By integrating these AI-driven strategies, AstraZeneca aims to enhance trial efficiency and reduce the time from drug discovery to patient care — a crucial factor in oncology.

Overcoming the Challenges of AI: It’s About Data Quality and Trust

Despite the exciting potential of AI, Nate acknowledges significant challenges — most notably, data quality and user trust.

AI systems are only as good as the data they consume, and in a situation where health systems are increasingly protective of their data, ensuring high-quality, consistent inputs remains a constant challenge.

Furthermore, the successful adoption of AI depends on fostering trust among both internal teams and external healthcare providers.

Nate emphasized the importance of transparency and human oversight. “It’s about finding that middle ground, leveraging AI for its strengths while integrating the expertise of experienced clinicians.”

To build familiarity and confidence, AstraZeneca has introduced an internal ChatGPT tool designed to clarify AI capabilities and limitations for its staff.

Future AI Innovations and AstraZeneca’s Strategic Positioning

Looking ahead, Nate sees a future where AI not only supports but truly transforms cancer care. He predicts emerging early detection technologies — such as at-home 3D body scanners and sophisticated liquid biopsy methods — as game changers.

These innovations could turn cancer into a condition that’s managed long before it becomes life-threatening, by catching signs of disease at their earliest stages and enabling rapid, tailored interventions.

Nate also highlights the potential of predictive analytics and improved drug discovery methods. He believes these advancements could significantly accelerate the development of new treatments by predicting patient responses and optimizing the clinical trial process.

While AstraZeneca may not view itself as a definitive pioneer in AI technology, the company is dedicated to remaining at the forefront of these advancements through robust internal expertise and strategic partnerships.

As Nate puts it, “the core focus remains on patients and their care,” ensuring that every technological breakthrough translates into better outcomes for those who need it most.

This approach enables AstraZeneca to leverage the full potential of AI, not merely as an innovation tool but as a basis for a more responsive and effective model of cancer care.