AI in Breast Cancer Trials: Smarter Site Strategy, Faster Recruitment, Better Outcomes
AI is transforming breast cancer trials—from smarter patient selection and site strategy to faster, more targeted drug development.
Artificial intelligence is reshaping how breast cancer is diagnosed, treated and studied—transforming everything from image interpretation to patient selection and clinical trial design. As data complexity increases, AI is proving to be a critical ally in precision oncology, helping sponsors, investigators and care teams make smarter decisions faster. For pharmaceutical companies investing in breast cancer innovation, AI is no longer an optional tool—it’s a strategic differentiator.
Learn more about how we use AI and other technology to guide our oncology clinical trials.
“AI is not just improving breast cancer diagnostics—it’s fundamentally changing how we approach treatment and drug development,” says Daniel Mazzolenis, MD, SVP in Medical Management at Syneos Health.
While enthusiasm around AI continues to grow, the applications gaining real traction today are grounded in robust data, clinical oversight and measurable value—particularly in diagnostics, site strategy and patient stratification. For sponsors investing in breast cancer clinical trials, these capabilities translate into faster startup timelines, smarter site selection and a greater likelihood of enrolling the right patients at the right time.
AI and the Acceleration of Drug Discovery
Traditionally, drug discovery in breast cancer can take more than a decade and cost upwards of $2 billion to bring a single therapy from target identification to clinical use, according to research published in Drug Discovery Today. AI is beginning to shorten that timeline by:
- Identifying novel drug targets more efficiently
- Predicting protein structures with high accuracy (as seen with AlphaFold, the 2024 Chemistry Nobel Prize breakthrough)
- Supporting the design of more targeted, less toxic therapies
“These advancements are vital,” says Gisela Peterson, MD, Senior Medical Director at Syneos Health. “They’re helping breast cancer therapies enter clinical trials with stronger foundational data and more strategic design—ultimately improving the odds of meaningful clinical outcomes.”
AI in Site Strategy and Trial Readiness
Clinical trial teams are increasingly relying on AI to select the right sites, identify eligible patients and reduce delays—particularly in oncology, where biomarker-defined subgroups continue to narrow enrollment windows.
In today’s increasingly complex breast cancer trials, where biomarker precision is paramount, AI is helping sponsors make smarter feasibility decisions earlier—improving site strategy and reducing risk. This includes estimating real-world recruitment potential based on inclusion/exclusion criteria, stratifying patient pools and avoiding overinflated enrollment projections that can derail timelines.
Dr. Peterson cites real-world examples from sites like the Sarah Cannon Cancer Network, where AI-based systems are already matching patients to trials based on inclusion/exclusion criteria—shortening recruitment timelines while offering sponsors more realistic projections of enrollment potential. “These tools help filter large patient pools, but clinical judgment remains essential,” she notes. “AI enhances efficiency—it doesn’t replace expertise.”
AI-Powered Breakthroughs in Breast Cancer Detection
The past few years have seen a surge in FDA-approved AI-driven devices designed to support breast cancer diagnostics. These innovations focus primarily on:
- Radiographic imaging: AI tools assist radiologists in interpreting mammograms, detecting subtle abnormalities with greater accuracy and reducing false positives.
- Pathology analysis: AI systems quantify features in pathology slides, identifying patterns beyond human perception and improving prognostic insights.
With AI-enhanced mammography interpretation, screening programs are becoming more efficient—reducing the burden on radiologists while improving speed and diagnostic accuracy. These diagnostic advances are also accelerating the identification and onboarding of trial-eligible patients—bridging care and clinical research.
Multi-Modal AI for Survival Prediction & Personalized Treatment
One of the most promising areas in oncology AI is the rise of multi-modal foundation models. These tools integrate diverse data sources—imaging, pathology, genomic profiles and clinical records—to improve predictive accuracy and guide treatment decisions.
These models can analyze large datasets to:
- Identify genomic alterations from standard tissue staining
- Predict mutation status linked to breast cancer subtypes
- Reveal immune infiltration patterns, which are critical for assessing estrogen and progesterone receptor (ER/PR) status
“We’re working closely with sites and sponsors to use these tools to impact patient stratification and speed trial readiness,” adds Dr. Peterson. "With broader adoption across top-performing sites, sponsors gain early visibility into biomarker-aligned patient populations—enhancing both trial design and operational readiness."
What This Means for Sponsors
For sponsors designing the next generation of breast cancer trials, AI is unlocking new levels of precision and predictability. From feasibility modeling to site activation and patient matching, these tools are helping reduce delays, improve accuracy and optimize enrollment—especially in biomarker-driven studies where speed and specificity are critical.
As multi-modal models evolve, they’re not just guiding treatment decisions—they’re shaping smarter trials. Sponsors are beginning to apply these insights to define more meaningful endpoints, anticipate patient response variability and reduce dropout risk.
Yet technology alone isn’t the differentiator. The greatest value comes from partners who can operationalize AI within real-world clinical research settings—bridging innovation with execution to drive meaningful outcomes.
See how Syneos Health can help you run smarter, faster breast cancer trials.
Contributors
Gisela Peterson, MD | Senior Medical Director, Medical Management
Daniel Mazzolenis, MD | SVP, Medical Management
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