ArticleFrontiers in oncology2023
Is ChatGPT accurate and reliable in answering questions regarding head and neck cancer?
Article in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.
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Who cites it
53 citing papers in PubMed.
- Generative AI for Personalized Learning Platforms in Otolaryngology-Head and Neck Surgery Education: Scoping Review.JMIR medical education · 2026Review
- Beyond one-size-fits-all: mapping information-seeking and decision-making pathways in cancer care.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026Article
- Commercial large language models for oral cavity cancer staging using descriptive pre-treatment MRI reports: ready for standalone use in clinical practice?Insights into imaging · 2026Article
- ChatGPT vs. multidisciplinary team decisions in auditory implantation: a concordance analysis of implant type and side selection.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Article
- The Reliability of Human Evaluation of Large Language Models in Health Care Settings: Scoping Review.Journal of medical Internet research · 2026Article
- Comparative evaluation of four large language models (ChatGPT-5, Gemini 2.5 Pro, Claude Sonnet-4, and DeepSeek-R1) for diagnostic and therapeutic decision-making in real-world otolaryngology scenarios.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Article
- 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
- Can a large language model inform patients about auditory brainstem implants?European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Article
- Scoping Review of Chatbot-Based Approaches to Cancer Patient Education.Nursing research and practice · 2026Review
- How AI responds to common HIV/AIDS questions: ChatGPT versus DeepSeek.Frontiers in public health · 2026Article
- ChatGPT as an information source for cochlear implants: A study on accuracy and reproducibility.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2025Article
- Quality assessment of patient-facing urologic telesurgery content using validated tools.Journal of robotic surgery · 2025Article
- Assessment of the validity of ChatGPT-3.5 responses to patient-generated queries following BPH surgery.Scientific reports · 2025Article
- Are ChatGPT's Answers to Questions About Salivary Gland Diseases Accurate and Reliable?Turkish archives of otorhinolaryngology · 2025Article
- Reducing Hallucinations and Trade-Offs in Responses in Generative AI Chatbots for Cancer Information: Development and Evaluation Study.JMIR cancer · 2025Article
- Applications of Natural Language Processing in Otolaryngology: A Scoping Review.The Laryngoscope · 2025Article
- Review
- Expert evaluation of ChatGPT accuracy and reliability for basic celiac disease frequently asked questions.Scientific reports · 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
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and objective: Chat Generative Pre-trained Transformer (ChatGPT) is an artificial intelligence (AI)-based language processing model using deep learning to create human-like text dialogue. It has been a popular source of information covering vast number of topics including medicine. Patient education in head and neck cancer (HNC) is crucial to enhance the understanding of patients about their medical condition, diagnosis, and treatment options. Therefore, this study aims to examine the accuracy and reliability of ChatGPT in answering questions regarding HNC. Methods: 154 head and neck cancer-related questions were compiled from sources including professional societies, institutions, patient support groups, and social media. These questions were categorized into topics like basic knowledge, diagnosis, treatment, recovery, operative risks, complications, follow-up, and cancer prevention. ChatGPT was queried with each question, and two experienced head and neck surgeons assessed each response independently for accuracy and reproducibility. Responses were rated on a scale: (1) comprehensive/correct, (2) incomplete/partially correct, (3) a mix of accurate and inaccurate/misleading, and (4) completely inaccurate/irrelevant. Discrepancies in grading were resolved by a third reviewer. Reproducibility was evaluated by repeating questions and analyzing grading consistency. Results: ChatGPT yielded "comprehensive/correct" responses to 133/154 (86.4%) of the questions whereas, rates of "incomplete/partially correct" and "mixed with accurate and inaccurate data/misleading" responses were 11% and 2.6%, respectively. There were no "completely inaccurate/irrelevant" responses. According to category, the model provided "comprehensive/correct" answers to 80.6% of questions regarding "basic knowledge", 92.6% related to "diagnosis", 88.9% related to "treatment", 80% related to "recovery - operative risks - complications - follow-up", 100% related to "cancer prevention" and 92.9% related to "other". There was not any significant difference between the categories regarding the grades of ChatGPT responses (p=0.88). The rate of reproducibility was 94.1% (145 of 154 questions). Conclusion: ChatGPT generated substantially accurate and reproducible information to diverse medical queries related to HNC. Despite its limitations, it can be a useful source of information for both patients and medical professionals. With further developments in the model, ChatGPT can also play a crucial role in clinical decision support to provide the clinicians with up-to-date information.
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