Assessing the Role of Large Language Learning Models and Artificial Intelligence in Improving Oncology Survivorship Care: A Comparative Study of ChatGPT and Gemini (Preprint)

Abstract

<sec> <title>BACKGROUND</title> As the population of cancer survivors continues to grow, the demand for accurate, accessible, and guideline-consistent survivorship care information has increased. Survivorship care encompasses a range of complex domains, including physical activity, nutrition, mental health, fertility, and the management of long-term treatment effects. However, gaps in patient education and limited healthcare resources create challenges in delivering comprehensive care. Artificial intelligence (AI)-powered large language models (LLMs) have emerged as potential tools to bridge these gaps, yet their performance in survivorship-specific contexts has not been systematically evaluated. </sec> <sec> <title>OBJECTIVE</title> This study aimed to evaluate and compare the performance of three commonly used LLMs: ChatGPT-4, ChatGPT Strawberry, and Gemini in providing evidence-based responses across multiple survivorship care domains, with a focus on alignment with established clinical guidelines. </sec> <sec> <title>METHODS</title> A set of predefined questions on survivorship care was submitted to each LLM. Two independent evaluators assessed responses using five quality metrics: clarity, coherence, completeness, factual accuracy, and relevance. Inter-rater reliability was calculated using Cohen’s kappa. Differences in AI performance were examined using descriptive statistics, paired t-tests, and mixed-effects models to assess variations by model, topic domain, and question complexity. </sec> <sec> <title>RESULTS</title> Inter-rater agreement was high (κ = 0.83), with the highest agreement in coherence (κ = 0.98) and the lowest in factual accuracy (κ = 0.70). ChatGPT Strawberry consistently outperformed both ChatGPT-4 and Gemini across most domains, especially in exercise, nutrition, mental health, and hormone-related symptoms (p &lt; 0.001). ChatGPT-4 performed comparably in fertility and sexual health but lagged in exercise and nutrition. Gemini demonstrated the lowest scores across all metrics. Notably, higher-complexity questions yielded stronger factual accuracy and completeness scores compared to medium-complexity items (p &lt; 0.001). </sec> <sec> <title>CONCLUSIONS</title> LLMs show potential as tools for survivorship care education, though accuracy and completeness remain limitations. ChatGPT Strawberry demonstrated the most consistent and high-quality performance. Future work should focus on refining AI models to better handle complex medical queries, integrating real-world patient interactions, and ensuring equity in AI-driven healthcare solutions. AI models may supplement survivorship care by offering timely, guideline-consistent information. However, given current limitations, these tools should complement clinician guidance, with ongoing validation to ensure their safe integration into cancer care. </sec> <sec> <title>CLINICALTRIAL</title> None </sec>

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