AI Platform Personalizes Support for Cancer Survivors Between Doctor Visits

Researchers at the Sylvester Comprehensive Cancer Center, part of the University of Miami Miller School of Medicine, have unveiled a novel artificial intelligence framework designed to deliver personalized supportive care to cancer survivors between clinical appointments. Published recently in Translational Behavioral Medicine, the Precision AI for Survivorship and Supportive Care initiative establishes a rigorous methodology for developing, evaluating, and deploying AI tools within oncology follow-up care. The project addresses a critical gap in cancer treatment: the prolonged period of physical and psychological management that survivors endure after active therapy concludes. To power this initiative, the research team leverages data collected through My Wellness Check, an electronic health record-integrated screening system that has gathered longitudinal patient-reported outcomes from over 37,000 ambulatory oncology patients. By analyzing a subset of 25,592 survivors tracked across 36 months, researchers trained machine learning models to identify patterns correlating with symptom burden and unplanned healthcare utilization. The integration of these predictive analytics has already increased forecasting accuracy for unfavorable outcomes by more than 25 percent. Building upon this analytical foundation, the team is testing My Wellness Support, an AI-enabled platform that synthesizes patient-reported outcomes, clinical records, and behavioral data to detect unmet needs. Survivors interact with an AI companion that delivers tailored educational materials, symptom-management protocols, and practical resources. Simultaneously, healthcare providers monitor aggregated patient data, risk scores, and clinical alerts through a dedicated dashboard. This dual-channel approach ensures that artificial intelligence functions strictly as an adjunct to traditional care, preserving direct patient-provider communication while enabling earlier intervention for emerging complications. Dr. Frank Penedo, director of Sylvester Survivorship and Supportive Care Institute, emphasized that the platform aims to bridge the distance between clinic visits without supplanting human oversight. Dr. Akina Natori, an oncologist on the research team, noted that the system is engineered to operate within established clinical guidelines, utilizing automated safety guardrails to flag cases requiring immediate medical attention. Dr. Sara Fleszar-Pavlović, director of research operations for the institute, underscored the framework’s commitment to scientific validation and transparency, acknowledging that regulatory and ethical standards must evolve in tandem with rapid AI advancement. Currently in early testing phases, the initiative represents a methodical approach to integrating predictive AI into post-treatment oncology. By prioritizing continuous evaluation, population-inclusive development, and strict clinical boundaries, the Sylvester team aims to establish a reproducible model for responsible AI deployment in survivorship care. If successful, the framework could standardize how healthcare systems leverage machine learning to extend evidence-based support, ultimately improving long-term quality of life for millions of cancer survivors navigating the post-treatment landscape.