ArticleInternational journal of ophthalmology2025
Assessing the proficiency of large language models on funduscopic disease knowledge.
Article in International journal of 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
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimTo assess the performance of five distinct large language models (LLMs; ChatGPT-3.5, ChatGPT-4, PaLM2, Claude 2, and SenseNova) in comparison to two human cohorts (a group of funduscopic disease experts and a group of ophthalmologists) on the specialized subject of funduscopic disease.
methodsFive distinct LLMs and two distinct human groups independently completed a 100-item funduscopic disease test. The performance of these entities was assessed by comparing their average scores, response stability, and answer confidence, thereby establishing a basis for evaluation.
resultsAmong all the LLMs, ChatGPT-4 and PaLM2 exhibited the most substantial average correlation. Additionally, ChatGPT-4 achieved the highest average score and demonstrated the utmost confidence during the exam. In comparison to human cohorts, ChatGPT-4 exhibited comparable performance to ophthalmologists, albeit falling short of the expertise demonstrated by funduscopic disease specialists.
conclusionThe study provides evidence of the exceptional performance of ChatGPT-4 in the domain of funduscopic disease. With continued enhancements, validated LLMs have the potential to yield unforeseen advantages in enhancing healthcare for both patients and physicians.
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.