Researchers from the University of British Columbia Okanagan and BC Cancer–Kelowna have developed a groundbreaking artificial intelligence model designed to identify breast cancer patients at high risk for cardiovascular disease. This innovation addresses a critical health challenge, as breast cancer patients face significantly higher rates of heart complications than the general population. Cardiovascular disease remains the leading cause of death worldwide, claiming an estimated 17.9 million lives annually. The study, published in the journal Radiotherapy and Oncology, leverages the routine chest CT scans taken by all breast cancer patients to plan radiation therapy. Traditionally, these images are used solely for treatment planning. However, the new AI system repurposes this data to assess heart health without imposing additional burdens on patients or the healthcare system. The model, developed by a team including Dr. Mohammad Shehata and Dr. Rasika Rajapakshe, utilizes a multimodal approach that integrates imaging data with electronic health records. By analyzing structural changes in the heart visible on CT scans alongside clinical factors such as age, hypertension, diabetes, family history, and general health, the system generates a more precise and personalized risk assessment than traditional methods. Dr. Shehata, a professor of computer science, noted that breast cancer patients often endure cardiovascular threats that are frequently overlooked. He explained that this tool provides clinicians with a proactive means to identify at-risk individuals, allowing for earlier intervention that could save lives. Dr. Rajapakshe, a senior medical physicist and co-lead on the study, emphasized that combining routinely collected imaging with clinical data enables detection that is both more accurate and earlier than previously possible. The team reported that the model achieved exceptionally high predictive performance, significantly outperforming existing risk models that rely primarily on clinical history. This breakthrough represents a significant advancement in personalized medicine. By identifying subtle patterns that conventional tools miss, the AI system facilitates early prediction of cardiovascular-related mortality. The researchers highlight that this non-invasive approach could lead to tailored interventions, ultimately improving survival outcomes for a vulnerable patient population. The project underscores the potential of local innovation to solve global health challenges by effectively merging advanced imaging analysis with clinical data. As healthcare systems seek ways to manage the dual threats of cancer and heart disease, this technology offers a scalable solution that enhances clinical decision-making without requiring new equipment or additional patient visits.