Comparative Evaluation of Diagnostic and Management Capabilities of Infiniti AI and ChatGPT-4o in Corneal Diseases.

BACKGROUND

Artificial intelligence (AI), particularly large language models (LLMs), is rapidly transforming medical education and clinical decision support. Ophthalmology, a specialty heavily reliant on pattern recognition, presents a promising domain for LLM integration. While general-purpose models like ChatGPT-4o have demonstrated strong performance in ophthalmic tasks, domain-specific systems such as Infiniti AI, built with a retrieval-augmented generation (RAG) framework, claim advantages by grounding responses in peer-reviewed ophthalmic literature. This study compares ChatGPT-4o (OpenAI, San Francisco, CA, USA) and Infiniti AI (Sinjab Academy, UAE) in corneal disease case scenarios.

MATERIALS AND

METHODS

Twenty corneal cases were selected from the University of Iowa EyeRounds database, covering infectious, inflammatory, degenerative, developmental, and systemic associations. ChatGPT-4o, Infiniti AI, and a fellowship-trained cornea specialist independently evaluated each case. Diagnostic and management responses were scored against American Academy of Ophthalmology preferred practice pattern guidelines using a four-point scale (0-3). Statistical comparisons were performed using paired t-tests and Wilcoxon signed-rank tests.

RESULTS

ChatGPT-4o significantly outperformed Infiniti AI across all categories. Diagnostic accuracy was higher for ChatGPT-4o (2.37 ± 0.81) than Infiniti AI (1.13 ± 0.71, p < 0.001, Cohen's d = 1.35). Management scores were also superior (2.65 ± 0.65 vs 1.98 ± 0.65, p < 0.001, d = 1.37). Overall, ChatGPT-4o achieved a mean total score of 5.00 ± 1.22 compared with 3.10 ± 1.10 for Infiniti AI (p < 0.001, d = 1.75).

CONCLUSIONS

ChatGPT-4o demonstrated greater diagnostic and management accuracy than Infiniti AI in corneal disease scenarios, highlighting the current strength of general-purpose LLMs over specialized retrieval-based systems. Nonetheless, both models remain prone to hallucinations and should serve as adjuncts to, rather than replacements for, expert judgment. Further refinement of ophthalmology-specific models is warranted to improve safety and clinical utility.