AI-Assisted Chemotherapy Side Effect Management

Chemotherapy-induced side effects affect patient quality of life and treatment outcomes. Studies indicate 86% of patients receiving chemotherapy experience at least one side effect, with 30% reporting moderate to severe symptoms. Common adverse effects including nausea, vomiting, fatigue, anorexia, pain, diarrhea, constipation, mucositis, and peripheral neuropathy lead to dose reductions, treatment delays, or discontinuation.

Care gaps persist despite advances in supportive care due to clinician time constraints and variable attention to specific side effects. Digital health interventions have demonstrated improved quality of life, reduced emergency department visits, and enhanced survival in cancer patients. A 2025 umbrella review reported positive effects of digital health interventions on multiple mental health indicators in cancer patients.

LLMs offer natural language processing and medical knowledge bases. Clinical application remains limited, with only 5% of LLM healthcare studies involving actual patient care according to recent systematic reviews. Reliability concerns and hallucination require physician oversight. While healthcare providers manage life-threatening chemotherapy toxicities effectively, less attention may be given to symptoms that impact quality of life without immediate life-threatening consequences.

This adaptive randomized controlled trial employs a two-stage design. Stage 1 enrolls 60 patients (30 per arm) with breast or colorectal cancer without malignancy-related symptoms at baseline. Stratification occurs by cancer type. The intervention involves healthcare providers collecting patient symptoms during clinic visits, excluding information that could identify patients, anonymizing information according to standardized prompts, and inputting data into the LLM system via temporary chat sessions where no records are retained. The LLM generates symptom analysis and management recommendations. Physicians review recommendations and adjust treatment plans accordingly, with all final decisions remaining under physician responsibility.

Interim analysis after 60 patients are enrolled evaluates safety, feasibility, and efficacy. Safety assessment includes stopping for serious LLM-related issues. Interim analysis after 60 patients are enrolled evaluates safety and feasibility. Safety assessment includes stopping for serious LLM-related adverse events. Feasibility assessment examines LLM system usage rate and physician satisfaction. No formal efficacy stopping rule is applied; therefore, no alpha adjustment is required for the final analysis. Based on interim results, up to 114 additional patients may be enrolled for a maximum total of 174 participants.

The primary analysis uses linear mixed-effects models adjusting for baseline scores and stratification factors. A clinically meaningful difference of 8 points on the EORTC QLQ-C30 scale with standard deviation of 20 points provides >85% power at the final sample size. Secondary analyses include logistic regression for clinically meaningful improvement rates, appropriate statistical tests for adherence and toxicity outcomes, and Poisson/negative binomial regression for healthcare utilization.

This study aims to evaluate whether LLM-assisted intervention improves patient outcomes and healthcare resource utilization in oncology practice, providing evidence for the clinical application of LLMs as decision support tools in patient care.