In a landmark advancement for breast cancer diagnostics, the Sylvester Comprehensive Cancer Center at the University of Miami Miller School of Medicine is spearheading a groundbreaking clinical trial designed to rigorously evaluate the role of artificial intelligence (AI) in mammography interpretation. This multi-institutional endeavor, known as the PRISM Trial (Pragmatic Randomized Trial of Artificial Intelligence for Screening Mammography), aims to address critical questions about the efficacy, safety, and real-world utility of AI-assisted screening for breast cancer, the second most lethal cancer among women in the United States.
The trial, which has secured $16 million in funding from the Patient-Centered Outcomes Research Institute (PCORI), represents the first large-scale randomized effort in the U.S. to systematically investigate how AI can support radiologists in interpreting mammograms. By leveraging advanced AI algorithms integrated through clinical workflows, this study aims to enhance early cancer detection, simultaneously reducing false positive rates and unnecessary patient recalls—problems that frequently plague conventional mammography screening programs, leading to patient anxiety and increased healthcare costs.
PRISM involves a collaborative network spanning seven leading academic institutions across six states—including UCLA, UC Davis, Boston Medical Center, UC San Diego, Sylvester Comprehensive Cancer Center, University of Washington-Fred Hutchinson Cancer Center, and University of Wisconsin-Madison. These centers will interpret hundreds of thousands of mammograms using a randomized approach where images are either assessed by radiologists unaided or with the decision support of an FDA-cleared AI platform known as Transpara by ScreenPoint Medical, seamlessly integrated into clinical workflows via the Aidoc aiOS platform.
The AI system in question operates by analyzing mammographic images using deep convolutional neural networks optimized for breast tissue characterization. This technology quantifies risk scores indicating the likelihood of malignancy, thereby providing radiologists with an AI-derived second opinion. Importantly, despite the assistance offered by AI, participating radiologists retain full control over final diagnostic decisions, ensuring clinical expertise remains paramount.
Jose Net, M.D., Director of Breast Imaging Services at Sylvester and co-principal investigator of the trial, emphasizes the critical balance sought in this research. “Our objective is not to replace the radiologist but to understand precisely how AI tools can augment diagnostic accuracy in a meaningful and patient-centered way,” Dr. Net remarks. The trial’s patient-first design reflects this ethos by maintaining existing screening protocols at each center, without any alteration in the patient experience or additional procedural burden.
The scientific premise for this trial arises from the challenges associated with mammographic screening. While mammography remains the cornerstone for early breast cancer detection and has demonstrably reduced mortality rates, limitations such as false positives—which generate unnecessary follow-up tests and psychological distress—and false negatives, where cancers go undetected, necessitate improvements in interpretation methodologies. AI holds transformative potential, but its actual performance in clinical environments has remained under-explored until now.
With regard to study design, mammograms will be randomized upon acquisition, ensuring that some images are read with AI assistance and others are interpreted solely by radiologists in the standard of care arm. This randomization enables robust comparative effectiveness evaluation, allowing researchers to quantify the impact of AI integration on cancer detection rates, recall rates, and diagnostic workflow efficiency. The pragmatic, real-world nature of the trial further ensures that findings will be directly translatable into clinical practice.
Complementing the quantitative analyses, the PRISM Trial incorporates qualitative components such as focus groups and surveys targeting both patients and radiologists. These instruments aim to capture perceptions, trust levels, and acceptance of AI in diagnostic decision-making, addressing an often-overlooked dimension in AI deployment: user and patient engagement and confidence in technology-augmented care pathways.
This trial stands as possibly the most ambitious effort yet to generate high-quality evidence on AI’s role in breast cancer screening. It is expected to inform not only clinical protocols but also insurance reimbursement policies and technology adoption strategies, as the healthcare industry grapples with integrating AI into routine care while safeguarding patient safety and optimizing outcomes.
Another pivotal aspect of PRISM is its extensive geographic and institutional reach, encompassing diverse populations and healthcare settings. This inclusivity ensures that the study evaluates AI performance across varied demographic cohorts and facility types, thereby increasing the generalizability of its conclusions and helping to identify subgroups who may derive particular benefit—or conversely, where AI assistance may not add value.
The underlying technical infrastructure supporting the trial is robust, featuring state-of-the-art AI models trained on large-scale imaging datasets and continuously updated through machine learning techniques to refine predictive accuracy. These systems operate within secure computational environments that comply with healthcare data privacy regulations, ensuring patient information confidentiality throughout the study.
The outcome of this pioneering research initiative will ultimately shed light on whether AI can reliably augment radiologists’ interpretative accuracy, reduce unnecessary recalls, and alleviate patient anxiety without compromising diagnostic safety. As Dr. Net notes, “We are poised to generate the evidence necessary to integrate AI thoughtfully, preserving the indispensable role of human expertise while harnessing the power of computational advances.”
Readouts from the PRISM Trial will offer vital guidance to clinicians, hospital administrators, policymakers, and payers as the healthcare system navigates the complex challenges and opportunities presented by AI. This trial not only pushes the boundaries of medical imaging technology but also embodies a patient-centered approach to innovation, prioritizing trust, transparency, and real-world applicability.
For ongoing updates on this and other related advancements, the Sylvester Comprehensive Cancer Center maintains a dedicated presence on their InventUM blog and social media channels, fostering open dialogue and dissemination of new knowledge to the broader scientific community and the public.
Subject of Research: Artificial intelligence in breast cancer screening and mammography interpretation
Article Title: (Not specified in the provided content)
News Publication Date: September 23, 2025
Web References:
- Sylvester Comprehensive Cancer Center: https://umiamihealth.org/en/sylvester-comprehensive-cancer-center
- InventUM blog on AI and breast cancer screening: https://news.med.miami.edu/studying-artificial-intelligence-in-breast-cancer-screening/
- Sylvester Cancer on X (formerly Twitter): https://x.com/SylvesterCancer
Image Credits: Photo by Sylvester Comprehensive Cancer Center
Keywords: Mammography, Diagnostic imaging, Breast cancer, Medical technology, Radiology
