Evaluating reasoning large language models with human-like thinking in ophthalmic question answering

Key Points

• Reasoning large language models achieved superior accuracy in ophthalmic question answering, indicating their effectiveness.
• DeepSeek-R1 stood out with the highest accuracy score, showcasing the potential of LLMs to simulate human-like thinking.
• The analysis compared reasoning LLMs with conventional non-reasoning models to highlight their advantages in simulated thinking processes.
• These findings suggest that integrating reasoning capabilities may enable more trustworthy systems in the field of ophthalmology.