Abstract 1296: Evaluating the accuracy and reproducibility of ChatGPT models in answering lung cancer patient queries

Abstract

Abstract Large language models (LLMs) such as ChatGPT can imitate human conversation and produce rapid, coherent responses, which may mask their potential for inaccuracies. With patients increasingly turning to the internet for medical information, the use of LLM chatbots for cancer-related queries risks spreading misinformation. Our study assessed ChatGPT’s accuracy and reproducibility in offering valid information and treatment advice for lung cancer in line with established guidelines. In the evolving landscape of AI-driven healthcare support, the ability of language models to provide accurate and reliable information is crucial. Our study delves into the effectiveness of OpenAI's ChatGPT models (versions 3.5 and 4.0) in responding to patient inquiries about lung cancer across various domains including general information, clinical presentation, risk factors, screening, diagnosis, staging, treatment options, prognosis, post-treatment follow-up, lifestyle recommendations, and psychosocial/educational aspects. We conducted a structured assessment, posing identical sets of questions to both ChatGPT 3.5 and 4.0. A total of 47 questions were posed with each query being repeated twice per model to evaluate both the accuracy and reproducibility of the responses. The scoring system focused on the accuracy and comprehensiveness of each response. Our findings revealed a notable disparity in the performance of the two models. GPT 4.0 demonstrated higher consistency and accuracy, with 41 out of 47 (87.2%) responses deemed accurate and comprehensive, compared to 36 out of 47 (76.6%) for GPT 3.5. In terms of reproducibility, both models exhibited strong performance: 42 out of 47 (89.3%) for GPT 3.5 and 45 out of 47 for GPT 4.0 (95.7%). When comparing responses between the models, we observed good reproducibility in 38 out of 47 questions (80.8%). A key observation was that GPT 4.0 significantly outperformed its predecessor GPT 3.5 in terms of both accuracy as well as reproducibility within its own responses, indicating a more reliable and consistent performance. The area most lacking in accuracy for both models was lung cancer staging, indicating a need for further refinement in this domain. Another key observation was the models' tendency to incorporate empathetic language, often beginning responses with expressions of sympathy and consistently advising confirmation with a medical professional. Our study underscores the potential and limitations of current AI models in patient education and support, highlighting areas for improvement and the importance of empathetic communication in AI interactions with patients. As the model continues to be trained on a larger and more comprehensive set of data, it is reasonable to anticipate further improvements in its ability to provide precise, detailed, and contextually appropriate responses. Citation Format: Asiyah Allibhai, Ahmed Allibhai, Anthony Brade, Zishan Allibhai. Evaluating the accuracy and reproducibility of ChatGPT models in answering lung cancer patient queries [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1296.