TY - JOUR
T1 - Opportunities and Challenges of Chatbots in Ophthalmology
T2 - A Narrative Review
AU - Sabaner, Mehmet Cem
AU - Anguita, Rodrigo
AU - Antaki, Fares
AU - Balas, Michael
AU - Boberg-Ans, Lars Christian
AU - Ferro Desideri, Lorenzo
AU - Grauslund, Jakob
AU - Hansen, Michael Stormly
AU - Klefter, Oliver Niels
AU - Potapenko, Ivan
AU - Rasmussen, Marie Louise Roed
AU - Subhi, Yousif
N1 - Publisher Copyright:
© 2024 by the authors.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - artificial intelligence
KW - Bard
KW - Bing
KW - ChatGPT
KW - Claude
KW - e-learning
KW - Gemini
KW - large language model
KW - ophthalmology
U2 - 10.3390/jpm14121165
DO - 10.3390/jpm14121165
M3 - Review
C2 - 39728077
AN - SCOPUS:85213487423
VL - 14
JO - Journal of Personalized Medicine
JF - Journal of Personalized Medicine
SN - 2075-4426
IS - 12
M1 - 1165
ER -