ArticleJMIR cancer2025
Assessing the Quality and Reliability of ChatGPT's Responses to Radiotherapy-Related Patient Queries: Comparative Study With GPT-3.5 and GPT-4.
Article in JMIR cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Large Language Models for Breast and Cervical Cancers Communication: Mixed Methods Evaluation Study Assessing Linguistic Quality, Safety, and Accessibility.JMIR cancer · 2026Article
- Comparing the Accuracy of ChatGPT-4o, DeepSeek-V3, and Gemini 2.5 Flash in Answering Frequently Asked Questions About Systemic Lupus Erythematosus: Quantitative Study.JMIR formative research · 2026Article
- Performance of three large language models in answering parent-focused questions on rickets: a dual pediatric-orthopedic specialist evaluation.BMC pediatrics · 2026Article
- Developing a Service Quality Index System for AI Health Care Chatbots: Mixed Methods Study.Journal of medical Internet research · 2026Article
- Comparative assessment of large language models in diabetic foot infection management: alignment with IWGDF/IDSA guidelines.Frontiers in endocrinology · 2026Article
- Article
- Evaluation of the feasibility of artificial intelligence models as patient information advisors for human papillomavirus vaccines.Revista da Associacao Medica Brasileira (1992) · 2025Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Patients frequently resort to the internet to access information about cancer. However, these websites often lack content accuracy and readability. Recently, ChatGPT, an artificial intelligence-powered chatbot, has signified a potential paradigm shift in how patients with cancer can access vast amounts of medical information, including insights into radiotherapy. However, the quality of the information provided by ChatGPT remains unclear. This is particularly significant given the general public's limited knowledge of this treatment and concerns about its possible side effects. Furthermore, evaluating the quality of responses is crucial, as misinformation can foster a false sense of knowledge and security, lead to noncompliance, and result in delays in receiving appropriate treatment. Objective: This study aims to evaluate the quality and reliability of ChatGPT's responses to common patient queries about radiotherapy, comparing the performance of ChatGPT's two versions: GPT-3.5 and GPT-4. Methods: We selected 40 commonly asked radiotherapy questions and entered the queries in both versions of ChatGPT. Response quality and reliability were evaluated by 16 radiotherapy experts using the General Quality Score (GQS), a 5-point Likert scale, with the median GQS determined based on the experts' ratings. Consistency and similarity of responses were assessed using the cosine similarity score, which ranges from 0 (complete dissimilarity) to 1 (complete similarity). Readability was analyzed using the Flesch Reading Ease Score, ranging from 0 to 100, and the Flesch-Kincaid Grade Level, reflecting the average number of years of education required for comprehension. Statistical analyses were performed using the Mann-Whitney test and effect size, with results deemed significant at a 5% level (P=.05). To assess agreement between experts, Krippendorff α and Fleiss κ were used. Results: GPT-4 demonstrated superior performance, with a higher GQS and a lower number of scores of 1 and 2, compared to GPT-3.5. The Mann-Whitney test revealed statistically significant differences in some questions, with GPT-4 generally receiving higher ratings. The median (IQR) cosine similarity score indicated substantial similarity (0.81, IQR 0.05) and consistency in the responses of both versions (GPT-3.5: 0.85, IQR 0.04; GPT-4: 0.83, IQR 0.04). Readability scores for both versions were considered college level, with GPT-4 scoring slightly better in the Flesch Reading Ease Score (34.61) and Flesch-Kincaid Grade Level (12.32) compared to GPT-3.5 (32.98 and 13.32, respectively). Responses by both versions were deemed challenging for the general public. Conclusions: Both GPT-3.5 and GPT-4 demonstrated having the capability to address radiotherapy concepts, with GPT-4 showing superior performance. However, both models present readability challenges for the general population. Although ChatGPT demonstrates potential as a valuable resource for addressing common patient queries related to radiotherapy, it is imperative to acknowledge its limitations, including the risks of misinformation and readability issues. In addition, its implementation should be supported by strategies to enhance accessibility and readability.
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