ArticleDigestive diseases and sciences2024
Applicability of Online Chat-Based Artificial Intelligence Models to Colorectal Cancer Screening.
Article in Digestive diseases and sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 3 of them syntheses that pooled it.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
19 citing papers in PubMed, 3 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
- Large Language Models in Gastroenterology: Systematic Review.Journal of medical Internet research · 2024Pooled it
- Emerging applications of NLP and large language models in gastroenterology and hepatology: a systematic review.Frontiers in medicine · 2024Pooled it
- A Guideline-Concordant Chatbot Framework for Structured Colorectal Cancer Screening: Multistage Feasibility Study.Journal of medical Internet research · 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
- Implementation of AI in oncology: a systematic review of educational and clinical integration in global contexts.Frontiers in digital health · 2026Review
- Foundation models: Insights and implications for gastrointestinal cancer.World journal of gastroenterology · 2025Review
- Temporal evolution of large language models (LLMs) in oncology.Journal of translational medicine · 2025Article
- Large language model integrations in cancer decision-making: a systematic review and meta-analysis.NPJ digital medicine · 2025Article
- Assessing online chat-based artificial intelligence models for weight loss recommendation appropriateness and bias in the presence of guideline incongruence.International journal of obesity (2005) · 2025Article
- Medical accuracy of artificial intelligence chatbots in oncology: a scoping review.The oncologist · 2025Article
- Large language models in oncology: a review.BMJ oncology · 2025Review
- The Potential Clinical Utility of the Customized Large Language Model in Gastroenterology: A Pilot Study.Bioengineering (Basel, Switzerland) · 2024Article
- Large language model use in clinical oncology.NPJ precision oncology · 2024Article
- Accuracy and consistency of publicly available Large Language Models as clinical decision support tools for the management of colon cancer.Journal of surgical oncology · 2024Article
- The Application of Large Language Models in Gastroenterology: A Review of the Literature.Cancers · 2024Review
- Evaluating Large Language Models' Ability Using a Psychiatric Screening Tool Based on Metaphor and Sarcasm Scenarios.Journal of Intelligence · 2024Article
- Large language models in patient education: a scoping review of applications in medicine.Frontiers in medicine · 2024Article
- Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOver the past year, studies have shown potential in the applicability of ChatGPT in various medical specialties including cardiology and oncology. However, the application of ChatGPT and other online chat-based AI models to patient education and patient-physician communication on colorectal cancer screening has not been critically evaluated which is what we aimed to do in this study.
methodsWe posed 15 questions on important colorectal cancer screening concepts and 5 common questions asked by patients to the 3 most commonly used freely available artificial intelligence (AI) models. The responses provided by the AI models were graded for appropriateness and reliability using American College of Gastroenterology guidelines. The responses to each question provided by an AI model were graded as reliably appropriate (RA), reliably inappropriate (RI) and unreliable. Grader assessments were validated by the joint probability of agreement for two raters.
resultsChatGPT and YouChat™ provided RA responses to the questions posed more often than BingChat. There were two questions that > 1 AI model provided unreliable responses to. ChatGPT did not provide references. BingChat misinterpreted some of the information it referenced. The age of CRC screening provided by YouChat™ was not consistently up-to-date. Inter-rater reliability for 2 raters was 89.2%.
conclusionMost responses provided by AI models on CRC screening were appropriate. Some limitations exist in their ability to correctly interpret medical literature and provide updated information in answering queries. Patients should consult their physicians for context on the recommendations made by these AI models.
Indexed as
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38267726What OpenQuestion holds
Registered trials
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.