Introduction Optic disc drusen (ODD) are acellular deposits in the optic nerve head, which are most often benign and asymptomatic. Patients may develop visual field defects and be at increased risk of ischemic co-morbidities. As ODD can be difficult to distinguish from papilledema, patients are at risk of unnecessary clinical workups. Patient information is a key aspect of ODD management. In this study, we explored the accuracy of ChatGPT responses for typical patient questions on ODD. Methods Two content experts reached consensus on 20 typical patient questions. We retrieved five separate responses for each question from ChatGPT, totaling 100 responses. Each response was evaluated on a 5-point Likert-scale on accuracy by each content expert in an individual fashion. Results The two experts were in fair/substantial agreement in the evaluation of responses (Cronbach’s alpha: 0.64). Of the 100 responses, 17 were relevant and without any inaccuracies, 78 were relevant and with inaccuracies not being harmful, and five were relevant and with inaccuracies potentially harmful. The lowest accuracy scores were obtained for questions dealing with treatment and prognosis. Conclusions ChatGPT often provides relevant answers for patient questions on ODD, but inaccuracies become potentially harmful when questions deal with treatment and prognosis.

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.

Purpose In this study, we investigate the potential of a novel artificial intelligence-based system for autonomous follow-up of patients treated for neovascular age-related macular degeneration (AMD). Methods A temporal deep learning model was trained on a data set of 84 489 optical coherence tomography scans from AMD patients to recognize disease activity, and its performance was compared with a published non-temporal model trained on the same data (Acta Ophthalmol, 2021). An autonomous follow-up system was created by augmenting the AI model with deterministic logic to suggest treatment according to the observe-and-plan regimen. To validate the AI-based system, a data set comprising clinical decisions and imaging data from 200 follow-up consultations was collected prospectively. In each case, both the autonomous AI decision and original clinical decision were compared with an expert panel consensus. Results The temporal AI model proved superior at detecting disease activity compared with the model without temporal input (area under the curve 0.900 (95% CI 0.894-0.906) and 0.857 (95% CI 0.846-0.867) respectively). The AI-based follow-up system could make an autonomous decision in 73% of the cases, 91.8% of which were in agreement with expert consensus. This was on par with the 87.7% agreement rate between decisions made in the clinic and expert consensus (p = 0.33). Conclusions The proposed autonomous follow-up system was shown to be safe and compliant with expert consensus on par with clinical practice. The system could in the future ease the pressure on public ophthalmology services from an increasing number of AMD patients.