AI-Powered Precision Learning: Optimizing Frontline Treatment Decision-Making in Chronic Lymphocytic Leukemia: Integrating Biomarkers, Clinical Evidence, and Patient-Centered Care

The frontline management of chronic lymphocytic leukemia (CLL) has never offered more promise or demanded greater clinical precision. Biomarker-guided treatment selection and an expanding arsenal of targeted therapies are redefining standards of care. However, clinicians continue to face challenges integrating biomarker data, clinical evidence, and patient-specific factors when selecting frontline therapy, particularly for older adults and medically complex patients. Treatment-related toxicities associated with Bruton tyrosine kinase (BTK) inhibitors and venetoclax-based regimens further require proactive monitoring and multidisciplinary management to preserve treatment continuity. To deliver individualized, evidence-based care with confidence, clinicians need practice grounded in realistic clinical scenarios. In this AI-powered precision learning activity, learners will work through real-world scenarios that require integration of biomarker testing results, current clinical evidence, and patient-specific characteristics to guide personalized frontline treatment decisions including the choice between fixed-duration and continuous treat-to-progression approaches. Throughout the activity, learners will further be challenged to tailor treatment for older and medically complex patients and apply evidence-based multidisciplinary strategies to monitor and manage treatment-emergent toxicities in support of treatment continuity and optimal patient outcomes.