ArticleGraefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie2026
Comparing the performance of four mainstream large language models on medical literature review generation: a human expert evaluation in SMILE surgery.
Article in Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Hallucination Rate of Peer-Reviewed Citations Generated by Large Language Models in Neurocritical Care.Critical care explorations · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors.
Funding
Abstract
purposeTo systematically evaluate and compare the performance of four leading large language models (LLMs) in generating medical literature reviews across topics of varying research maturity, thereby providing insights for their effective and responsible application in academic writing.
methodsIn this comparative study, using standardized prompts, we instructed four leading LLMs (GPT-4, Gemini 2.5 Pro, Grok-3, and DeepSeek R1) to generate literature reviews on nine topics related to small incision lenticule extraction (SMILE) surgery. These topics were categorized into three groups by research maturity: well-researched, controversial, and open. Seven ophthalmology experts evaluated the generated content across four dimensions: quality, accuracy, bias, and relevance, while all references were verified for authenticity. Performance differences among models were evaluated using group comparison tests followed by post-hoc analysis.
resultsSignificant performance variations were identified across all four models and dimensions (p < 0.001). Specifically, Gemini ranked highest in content quality, accuracy, and bias control. In contrast, DeepSeek, despite its high-quality score, received the lowest relevance score. Grok-3 demonstrated the highest reference authenticity (p < 0.001), whereas GPT-4's was the lowest (p < 0.001). All models showed diminished performance on open topics and exhibited severe reference fabrication ("hallucinations").
conclusionRather than excelling universally, LLMs exhibit distinct and task-specific strengths that mandate a task-driven, hybrid strategy in tool selection. Reference fabrication was found to be a pervasive issue across all models, regardless of the task topic, elevating human verification from a best practice to an essential safeguard for academic integrity.
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.