Sylvester Cancer Center Launches AI Platform to Deliver Personalized Support for Cancer Survivors

AI Framework Addresses Post-Treatment Challenges for Cancer Survivors

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, part of the University of Miami Miller School of Medicine, have introduced a framework for developing and implementing AI tools in cancer survivorship and supportive care. The proof-of-concept study, published in Translational Behavioral Medicine, presents Precision AI for Survivorship and Supportive Care—a system designed to personalize evidence-based support as cancer survivors navigate persistent side effects and other challenges after treatment while maintaining clinical oversight and safety guardrails.

Frank Penedo, Ph.D., study lead and director of Sylvester's Survivorship and Supportive Care Institute, emphasized that for many cancer survivors, the hardest part isn't always treatment itself but what happens afterward. Patients often manage fatigue, anxiety, uncertainty and other challenges between clinic visits. The framework explores whether AI for survivorship can help extend evidence-based support beyond the walls of the cancer center so survivors feel more connected, informed and supported throughout their journey, while not replacing in-person care.

Building on Longitudinal Patient Data and Clinical Infrastructure

The framework builds on years of survivorship research and clinical infrastructure developed at Sylvester through the My Wellness Check program, an electronic health record-integrated screening and triage program. Through this system, oncology patients routinely report symptoms, quality-of-life concerns, practical needs and other survivorship challenges before appointments. Since expanding across the cancer center, the program has collected longitudinal patient data including patient-reported outcomes, supportive care needs and nutritional data from more than 37,000 ambulatory oncology patients.

Researchers evaluated data from a subsample that included 25,592 of these ambulatory cancer survivors followed over 36 months to develop machine-learning models that identify patterns associated with symptom burden and unplanned healthcare utilization. Implementing these advanced analytic and AI-driven techniques improved predictive precision for unfavorable outcomes by over 25%. These resources provide the foundation for developing more personalized support for cancer survivors.

My Wellness Support Platform Delivers Tailored Interventions

The team's current work builds on the data and infrastructure behind My Wellness Check with an AI-enabled platform called My Wellness Support. This platform combines patient-reported outcomes, clinical records and behavioral information to identify risk patterns and unmet needs. That information tailors evidence-based educational resources, symptom-management strategies, resources to address practical needs and supportive care recommendations to each patient. Cancer survivors will interact with an AI companion while the study team and clinicians monitor trends, risk scores and alerts through a dedicated dashboard.

Akina Natori, M.D., MSPH, a Sylvester oncologist and assistant professor in the Miller School's Division of Medical Oncology, explained that a patient may be doing well medically but still be struggling with symptoms, stress or questions that arise between appointments. The goal is not to replace those interactions with clinicians but to create another layer of AI-driven supportive care that helps identify concerns earlier and gives patients access to trusted, evidence-based information when they need it.

Safety Guardrails and Continuous Evaluation Prioritized

Researchers emphasize the platform is designed to operate within established clinical guidelines and safety guardrails, and maintain human oversight. The project remains in early testing. Sara Fleszar-Pavlović, Ph.D., research assistant professor in the Miller School's Division of Medical Oncology and director of research operations for Sylvester's Survivorship and Supportive Care Institute, noted that new technologies often move faster than the systems designed to evaluate them. The framework emphasizes scientific validation, transparency and continuous evaluation to ensure AI becomes part of adjunctive survivorship and supportive care safely, effectively and with a range of patient populations in mind.

Implications for Future Cancer Care Models

By pairing new technology with rigorous science, researchers hope to help cancer survivors receive the support they need between appointments while establishing a model for how AI can be responsibly integrated into survivorship and supportive care. The framework addresses a critical gap in cancer care—the period between clinic visits when survivors may experience symptoms or concerns but lack immediate access to clinical guidance. Watch for how this model influences broader adoption of AI tools in oncology settings and whether similar frameworks emerge for other chronic conditions requiring ongoing symptom management. The emphasis on clinical oversight and validation may set standards for how healthcare systems evaluate and deploy AI-driven patient support tools going forward.