ArticleBMJ open ophthalmology2025
Evaluating the diagnostic reasoning of large language models in complex neuro-ophthalmological cases: a comparative analysis of GPT-o1 Pro, GPT-4o, Gemini, Grok 2 and DeepSeek.
Article in BMJ open ophthalmology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThis study aims to evaluate and compare the diagnostic reasoning of five large language models (LLMs) in complex neuro-ophthalmological cases. We assessed the performance of GPT-o1 Pro, GPT-4o, Google Gemini, Grok 2 and DeepSeek in handling clinical scenarios related to neuro-ophthalmology.
method18 clinical scenarios, derived from six complex neuro-ophthalmological cases, were presented to five LLMs: GPT-o1 Pro, GPT-4o, Google Gemini, Grok 2 and DeepSeek. The responses generated by these models were evaluated using the Revised-IDEA (R-IDEA) assessment tool. R-IDEA scores for high-quality responses ranged from 6 to 10, with 'Excellent' responses defined as those scoring between 8 and 10. In addition, the simplicity of each response was evaluated based on word count using a readability tool.
resultGPT-o1 Pro (8.80) significantly outperformed GPT-4o (6.80) and Grok 2 (6.94) in the R-IDEA scores (p=0.001). It achieved 100% high-quality responses, compared with 72.2% for GPT-4o, 77.8% for Grok 2 and 83.3% for both Gemini and DeepSeek (p=0.175). Regarding 'Excellent' responses, GPT-o1 Pro achieved 88.9% of its responses rated as Excellent, significantly outperforming the other models: 27.8% for GPT-4o, 38.9% for Grok 2 and 55.6% for both Gemini and DeepSeek (p=0.003). GPT-o1 Pro used the fewest words, showing significant differences compared with GPT-4o (p<0.001) and Gemini (p=0.032).
conclusionThe study underscores the superior clinical reasoning capabilities of ChatGPT-o1 Pro in neuro-ophthalmology compared with other LLMs, highlighting its potential for enhancing diagnostic processes in this complex field.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.