Opportunities and Challenges of Chatbots in Ophthalmology: A Narrative Review
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".