Artificial intelligence versus ophthalmology experts: Comparative analysis of responses to blepharitis patient queries.

Journal: European journal of ophthalmology
Published Date:

Abstract

ObjectiveTo assess the accuracy and clinical education value of responses from AI models (GPT-3.5, GPT-4o, Gemini, Gemini Advanced) compared to expert ophthalmologists' answers to common patient questions about blepharitis, and evaluate their potential for patient education and clinical use.MethodsThirteen frequently asked questions about blepharitis were selected. Responses were generated by AI models and compared to expert answers. A panel of ophthalmologists rated each response for correctness and clinical education value using a 7-point Likert scale. The Friedman test with post hoc comparisons was used to identify performance differences.ResultsExpert responses had the highest correctness (6.3) and clinical education value (6.4) scores, especially in complex, context-driven questions. Significant differences were found between expert and AI responses ( < 0.05). Among AI models, GPT-3.5 performed best in simple definitions (correctness: 6.4) but dropped to 5.5 in nuanced cases. GPT-4o followed (5.4), while Gemini and Gemini Advanced scored lower (5.0 and 4.9), especially in diagnostic and treatment contexts.ConclusionsAI models can support patient education by effectively answering basic factual questions about blepharitis. However, their limitations in complex clinical scenarios highlight the continued need for expert input. While promising as educational tools, AI should complement-not replace-clinician guidance in patient care.

Authors

  • Daniel Bahir
    Department of Ophthalmology, Tzafon Medical Center, Poriya, Israel. bahirdaniel@gmail.com.
  • Audrey Rostov
    Bellevue Precision Vision, Bellevue, WA, USA.
  • Yumna Busool Abu Eta
    Ophthalmology Department, Tzafon Medical Center, Azrieli Faculty of Medicine, Bar Ilan University, Tiberias, Israel.
  • Shirin Hamed Azzam
    Ophthalmology Department, Tzafon Medical Center, Azrieli Faculty of Medicine, Bar Ilan University, Tiberias, Israel.
  • David Lockington
    Gartnavel General Hospital, Tennent Institute of Ophthalmology, Glasgow, UK.
  • Joshua C Teichman
    Department of Ophthalmology and Vision Sciences, University of Toronto, Toronto, Canada.
  • Artemis Matsou
    Corneoplastic Unit, Queen Victoria Hospital, East Grinstead, UK.
  • Clara C Chan
    Department of Ophthalmology and Vision Sciences, University of Toronto, Toronto, Canada.
  • Elad Shvartz
    Ophthalmology Department, Tzafon Medical Center, Azrieli Faculty of Medicine, Bar Ilan University, Tiberias, Israel.
  • Michael Mimouni

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