ArticleInternational journal of radiation oncology, biology, physics2024
Current Strengths and Weaknesses of ChatGPT as a Resource for Radiation Oncology Patients and Providers.
Article in International journal of radiation oncology, biology, physics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Evaluation of a Retrieval-Augmented Generation-Powered Chatbot for Pre-CT Informed Consent: a Prospective Comparative Study.Journal of imaging informatics in medicine · 2025Trial
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- Generative AI for patient education in cancer care: A scoping review of evaluation practices and emerging trends.Technical innovations & patient support in radiation oncology · 2026Review
- The reliability of answers from four different AI chatbots on periodontology theoretical exam questions: an evaluation in dental education.BMC oral health · 2025Article
- Artificial Intelligence in the Diagnosis, Treatment, and Prognosis of Hypopharyngeal Carcinoma: A Scoping Review.Clinical medicine & research · 2025Article
- Large Language Models in Population Oncology: A Contemporary Review on the Use of Large Language Models to Support Data Collection, Aggregation, and Analysis in Cancer Care and Research.JCO clinical cancer informatics · 2025Review
- RTPhy-ChatBot: A RAG-Based intelligent assistant for radiotherapy physics using LLaMA3 and AAPM reports.Journal of applied clinical medical physics · 2025Article
- Development and evaluation of large-language models (LLMs) for oncology: A scoping review.PLOS digital health · 2025Article
- Large language model integrations in cancer decision-making: a systematic review and meta-analysis.NPJ digital medicine · 2025Article
- Feasibility study of automatic radiotherapy treatment planning for cervical cancer using a large language model.Radiation oncology (London, England) · 2025Article
- Current applications and challenges in large language models for patient care: a systematic review.Communications medicine · 2025Article
- Generative AI/LLMs for Plain Language Medical Information for Patients, Caregivers and General Public: Opportunities, Risks and Ethics.Patient preference and adherence · 2025Review
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- A Narrative Review on the Application of Large Language Models to Support Cancer Care and Research.Yearbook of medical informatics · 2024Review
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- Artificial Intelligence and the future of radiotherapy planning: The Australian radiation therapists prepare to be ready.Journal of medical radiation sciences · 2024Article
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11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeChat Generative Pre-Trained Transformer (ChatGPT), an artificial intelligence program that uses natural language processing to generate conversational-style responses to questions or inputs, is increasingly being used by both patients and health care professionals. This study aims to evaluate the accuracy and comprehensiveness of ChatGPT in radiation oncology-related domains, including answering common patient questions, summarizing landmark clinical research studies, and providing literature reviews with specific references supporting current standard-of-care clinical practice in radiation oncology. METHODS AND MATERIALS: We assessed the performance of ChatGPT version 3.5 (ChatGPT3.5) in 3 areas. We evaluated ChatGPT3.5's ability to answer 28 templated patient-centered questions applied across 9 cancer types. We then tested ChatGPT3.5's ability to summarize specific portions of 10 landmark studies in radiation oncology. Next, we used ChatGPT3.5 to identify scientific studies supporting current standard-of-care practice in clinical radiation oncology for 5 different cancer types. Each response was graded independently by 2 reviewers, with discordant grades resolved by a third reviewer.
resultsChatGPT3.5 frequently generated inaccurate or incomplete responses. Only 39.7% of responses to patient-centered questions were considered correct and comprehensive. When summarizing landmark studies in radiation oncology, 35.0% of ChatGPT3.5's responses were accurate and comprehensive, improving to 43.3% when provided the full text of the study. ChatGPT3.5's ability to present a list of studies related to standard-of-care clinical practices was also unsatisfactory, with 50.6% of the provided studies fabricated.
conclusionsChatGPT should not be considered a reliable radiation oncology resource for patients or providers at this time, as it frequently generates inaccurate or incomplete responses. However, natural language programming-based artificial intelligence programs are rapidly evolving, and future versions of ChatGPT or similar programs may demonstrate improved performance in this domain.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.