Opportunities and Challenges of Chatbots in Ophthalmology: A Narrative Review

Mehmet Cem Sabaner, Rodrigo Anguita, Fares Antaki, Michael Balas, Lars Christian Boberg-Ans, Lorenzo Ferro Desideri, Jakob Grauslund, Michael Stormly Hansen, Oliver Niels Klefter, Ivan Potapenko, Marie Louise Roed Rasmussen, Yousif Subhi*

*Corresponding author for this work

Research output: Contribution to journalReviewResearchpeer-review

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Abstract

Artificial intelligence (AI) is becoming increasingly influential in ophthalmology, particularly through advancements in machine learning, deep learning, robotics, neural networks, and natural language processing (NLP). Among these, NLP-based chatbots are the most readily accessible and are driven by AI-based large language models (LLMs). These chatbots have facilitated new research avenues and have gained traction in both clinical and surgical applications in ophthalmology. They are also increasingly being utilized in studies on ophthalmology-related exams, particularly those containing multiple-choice questions (MCQs). This narrative review evaluates both the opportunities and the challenges of integrating chatbots into ophthalmology research, with separate assessments of studies involving open- and close-ended questions. While chatbots have demonstrated sufficient accuracy in handling MCQ-based studies, supporting their use in education, additional exam security measures are necessary. The research on open-ended question responses suggests that AI-based LLM chatbots could be applied across nearly all areas of ophthalmology. They have shown promise for addressing patient inquiries, offering medical advice, patient education, supporting triage, facilitating diagnosis and differential diagnosis, and aiding in surgical planning. However, the ethical implications, confidentiality concerns, physician liability, and issues surrounding patient privacy remain pressing challenges. Although AI has demonstrated significant promise in clinical patient care, it is currently most effective as a supportive tool rather than as a replacement for human physicians.

Original languageEnglish
Article number1165
JournalJournal of Personalized Medicine
Volume14
Issue number12
Number of pages25
ISSN2075-4426
DOIs
Publication statusPublished - 2024

Bibliographical note

Publisher Copyright:
© 2024 by the authors.

Keywords

  • artificial intelligence
  • Bard
  • Bing
  • ChatGPT
  • Claude
  • e-learning
  • Gemini
  • large language model
  • ophthalmology

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