AI-Assisted Chemotherapy Side Effect Management

This two-stage adaptive randomized controlled trial evaluates the feasibility and preliminary efficacy of large language model (LLM)-assisted intervention for managing chemotherapy side effects in patients with solid tumors. Adults with histologically confirmed breast or colorectal cancer scheduled for at least 3 months of systemic chemotherapy will be randomly assigned (1:1) to receive either LLM-assisted care or standard care. The study employs an adaptive design with initial enrollment of 60 patients (30 per arm), followed by interim analysis. Unless enrollment is stopped for safety reasons identified at interim analysis, an additional 114 patients will be enrolled for a maximum total of 174 patients (87 per arm). In the intervention group, healthcare providers input anonymized patient symptom data into an LLM system using sessions where data is not retained, which generates evidence-based management recommendations. Physicians critically review these recommendations and use them as reference for clinical decision-making, with final treatment decisions remaining under physician discretion. The control group receives standard supportive care without LLM assistance. The primary outcome is change in health-related quality of life measured by EORTC QLQ-C30 global health status/QoL scale from baseline to end of treatment. Secondary outcomes include proportion achieving clinically meaningful improvement (≥8-point increase), treatment adherence, dose intensity, healthcare reso…

This description comes directly from the study's public registry record.

Young-Joon Kang  ·  +82322805179  ·  yjkang.md@gmail.com

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