Can Large Language Models Aid Caregivers of Pediatric Cancer Patients in Information Seeking? A Cross-Sectional Investigation

Abstract

AbstractBackground and ObjectivesIn pediatric oncology, caregivers seek detailed, accurate, and understandable information about their child’s condition, treatment, and side effects. The primary aim of this study was to assess the performance of four publicly accessible large language model (LLM)- supported knowledge generation and search tools in providing valuable and reliable information to caregivers of children with cancer.MethodsThis cross-sectional study evaluated the performance of the four LLM-supported tools — ChatGPT (GPT-4), Google Bard (Gemini Pro), Microsoft Bing Chat, and Google SGE- against a set of frequently asked questions (FAQs) derived from the Children’s Oncology Group Family Handbook and expert input. Five pediatric oncology experts assessed the generated LLM responses using measures including Accuracy (3-point ordinal scale), Clarity (3-point ordinal scale), Inclusivity (3-point ordinal scale), Completeness (Dichotomous nominal scale), Clinical Utility (5-point Likert-scale), and Overall Rating (4-point ordinal scale). Additional Content Quality Criteria such as Readability (ordinal scale; 5- 18th grade of educated reading), Presence of AI Disclosure (Dichotomous scale), Source Credibility (3- point interval scale), Resource Matching (3-point ordinal scale), and Content Originality (ratio scale) were also evaluated. We used descriptive analysis including the mean, standard deviation, median, and interquartile range. We conducted Shapiro-Wilk test for normality, Levene’s test for homogeneity of variances, and Kruskal-Wallis H-Tests and Dunn’s post-hoc tests for pairwise comparisons.ResultsThrough expert evaluation, ChatGPT showed high performance in accuracy (M=2.71, SD=0.235), clarity (M=2.73, SD=0.271), completeness (M=0.815, SD=0.203), Clinical Utility (M=3.81, SD=0.544), and Overall Rating (M=3.13, SD=0.419). Bard also performed well, especially in accuracy (M=2.56, SD=0.400) and clarity (M=2.54, SD=0.411), while Bing Chat (Accuracy M=2.33, SD=0.456; Clarity M=2.29, SD=0.424) and Google SGE (Accuracy M=2.08, SD=0.552; Clarity M=1.95, SD=0.541) had lower overall scores. The Presence of AI Disclosure was less frequent in ChatGPT (M=0.69, SD=0.46), which affected Clarity (M=2.73, SD=0.266), whereas Bard maintained a balance between AI Disclosure (M=0.92, SD=0.27) and Clarity (M=2.54, SD=0.403). Overall, we observed significant differences between LLM tools (p < .01).ConclusionsLLM-supported tools potentially contribute to caregivers’ knowledge of pediatric oncology on related topics. Each model has unique strengths and areas for improvement, suggesting the need for careful selection and evaluation based on specific clinical contexts. Further research is needed to explore the application of these tools in other medical specialties and patient demographics to assess their broader applicability and long-term impacts, including the usability and feasibility of using LLM- supported tools with caregivers.