首页|New Findings on Artificial Intelligence from University of Toronto Summarized (E valuating Chatgpt On Orbital and Oculofacial Disorders: Accuracy and Readability Insights)
New Findings on Artificial Intelligence from University of Toronto Summarized (E valuating Chatgpt On Orbital and Oculofacial Disorders: Accuracy and Readability Insights)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in Artific ial Intelligence. According to news reporting originating from Toronto, Canada, by NewsRx correspondents, research stated, "To assess the accuracy and readabili ty of responses generated by the artificial intelligence model, ChatGPT (version 4.0), to questions related to 10 essential domains of orbital and oculofacial d isease. A set of 100 questions related to the diagnosis, treatment, and interpre tation of orbital and oculofacial diseases was posed to ChatGPT 4.0." Our news editors obtained a quote from the research from the University of Toron to, "Responses were evaluated by a panel of 7 experts based on appropriateness a nd accuracy, with performance scores measured on a 7-item Likert scale. Inter-ra ter reliability was determined via the intraclass correlation coefficient. The a rtificial intelligence model demonstrated accurate and consistent performance ac ross all 10 domains of orbital and oculofacial disease, with an average appropri ateness score of 5.3/6.0 (‘mostly appropriate' to ‘completely appropriate'). Dom ains of cavernous sinus fistula, retrobulbar hemorrhage, and blepharospasm had t he highest domain scores (average scores of 5.5 to 5.6), while the proptosis dom ain had the lowest (average score of 5.0/6.0). The intraclass correlation coeffi cient was 0.64 (95% CI: 0.52 to 0.74), reflecting moderate inter-r ater reliability. The responses exhibited a high reading-level complexity, repre senting the comprehension levels of a college or graduate education. This study demonstrates the potential of ChatGPT 4.0 to provide accurate information in the field of ophthalmology, specifically orbital and oculofacial disease. However, challenges remain in ensuring accurate and comprehensive responses across all di sease domains. Future improvements should focus on refining the model's correctn ess and eventually expanding the scope to visual data interpretation. Our result s highlight the vast potential for artificial intelligence in educational and cl inical ophthalmology contexts."
TorontoCanadaNorth and Central Ameri caArtificial IntelligenceEmerging TechnologiesMachine LearningUniversity of Toronto
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