Clinical Research Directory
Browse clinical research sites, groups, and studies.
AI-Assisted Chemotherapy Side Effect Management
Sponsor: Incheon St.Mary's Hospital
Summary
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 40 patients (20 per arm), followed by interim analysis. If predefined criteria are met, an additional 134 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 resource utilization, and physician acceptance of LLM recommendations.
Official title: A Randomized Controlled Trial of AI-Assisted Chemotherapy Side Effect Management in Solid Tumor Patients
Key Details
Gender
All
Age Range
18 Years - Any
Study Type
INTERVENTIONAL
Enrollment
174
Start Date
2025-11-12
Completion Date
2026-10-31
Last Updated
2025-12-15
Healthy Volunteers
No
Conditions
Interventions
LLM-assisted chemotherapy side effect management
Healthcare providers collect patient symptoms during clinic visits, anonymize the information, and input it into the LLM system via temporary chat sessions. The LLM generates management recommendations which physicians review and consider when adjusting treatment plans. Healthcare providers anonymize patient information and input into LLM using temporary chat sessions. LLM recommendations serve as reference only, with physicians making all final clinical decisions.
Locations (1)
Incheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea
Incheon, South Korea