ArticleScientific reports2024
Evaluating AI in medicine: a comparative analysis of expert and ChatGPT responses to colorectal cancer questions.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.
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Who cites it
25 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Large Language Models in Colorectal Cancer Care and Clinical Decision Support: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Emerging applications of NLP and large language models in gastroenterology and hepatology: a systematic review.Frontiers in medicine · 2024Pooled it
- Review
- Walkable Intelligent Parks: Can a Large Language Model Turn Urban Park Audit Findings into Actionable Recommendations?International journal of environmental research and public health · 2026Article
- ChatGPT in urogynecology: Comparing large language model responses to human experts.Acta obstetricia et gynecologica Scandinavica · 2026Article
- ChatGPT in the diagnosis and management of complex polyneuropathies: comparative analysis with neurologists using real-world cases.NPJ digital medicine · 2026Article
- A multicenter multifunctional assessment of large language models in pure-tone audiogram interpretation for patients.NPJ digital medicine · 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
- Evaluating large language models using the Type 2 Diabetes Health Education guideline: a comparative analysis of ChatGPT-4.1, Claude-4.0, DeepSeek-V3, and ERNIE Bot 4.5 Turbo.Frontiers in public health · 2026Article
- Foundation models: Insights and implications for gastrointestinal cancer.World journal of gastroenterology · 2025Review
- Using large language models for clinical staging of colorectal cancer from imaging reports: a pilot study.Annals of surgical treatment and research · 2025Article
- Evidence-Based Medicine: Past, Present, Future.Journal of clinical medicine · 2025Review
- Large language model integrations in cancer decision-making: a systematic review and meta-analysis.NPJ digital medicine · 2025Article
- Healthcare professionals and the public sentiment analysis of ChatGPT in clinical practice.Scientific reports · 2025Article
- The clinical application potential assessment of the Deepseek-R1 large language model in lung cancer.Frontiers in oncology · 2025Article
- Addressing comments by Daungsupawong et al on ChatGPT's responses regarding radiofrequency ablation for varicose veins.Journal of vascular surgery. Venous and lymphatic disorders · 2025Article
- Testing the knowledge of artificial intelligence chatbots in pharmacology: examples of two groups of drugs.PeerJ. Computer science · 2025Article
- Cancer prognosis information-seeking among survivors and caregivers: findings from the National Cancer Institute's Cancer Information Service.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2024Article
- Investigation and analysis of the status of cancer health popularization in China, 2023.World journal of clinical oncology · 2024Article
- A framework for human evaluation of large language models in healthcare derived from literature review.NPJ digital medicine · 2024Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Colorectal cancer (CRC) is a global health challenge, and patient education plays a crucial role in its early detection and treatment. Despite progress in AI technology, as exemplified by transformer-like models such as ChatGPT, there remains a lack of in-depth understanding of their efficacy for medical purposes. We aimed to assess the proficiency of ChatGPT in the field of popular science, specifically in answering questions related to CRC diagnosis and treatment, using the book "Colorectal Cancer: Your Questions Answered" as a reference. In general, 131 valid questions from the book were manually input into ChatGPT. Responses were evaluated by clinical physicians in the relevant fields based on comprehensiveness and accuracy of information, and scores were standardized for comparison. Not surprisingly, ChatGPT showed high reproducibility in its responses, with high uniformity in comprehensiveness, accuracy, and final scores. However, the mean scores of ChatGPT's responses were significantly lower than the benchmarks, indicating it has not reached an expert level of competence in CRC. While it could provide accurate information, it lacked in comprehensiveness. Notably, ChatGPT performed well in domains of radiation therapy, interventional therapy, stoma care, venous care, and pain control, almost rivaling the benchmarks, but fell short in basic information, surgery, and internal medicine domains. While ChatGPT demonstrated promise in specific domains, its general efficiency in providing CRC information falls short of expert standards, indicating the need for further advancements and improvements in AI technology for patient education in healthcare.
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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.