ArticleAdvances in medical education and practice2026
Evaluating the Applicability of Advanced Large Language Models in Laboratory Medicine Test Questions: A Comparative Performance Study.
Article in Advances in medical education and practice, 2026. 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
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: While large language models (LLMs) show promise in medical education, their comprehensive performance in specialized domains like medical laboratory science remains inadequately assessed. Purpose: This study aimed to evaluate advanced LLMs on medical laboratory questions, assessing accuracy, natural language generation (NLG) quality, reasoning performance, and efficiency. Methods: We conducted a multi-faceted evaluation of three advanced LLMs (DeepSeek-R1, Gemini-2.5 Pro, GPT-5), benchmarking them against medical laboratory scientists and earlier ChatGPT versions. The evaluation utilized 493 questions sourced from the internal Medical Laboratory Test Bank of Wannan Medical College. These questions comprised both knowledge-based and reasoning-based single- and multiple-choice types (SCQs and MCQs). Performance was measured by accuracy, Macro-F1, response time, NLG scores (ROUGE-L, METEOR), and structured logical reasoning assessment. Appropriate statistical tests (including χ Results: DeepSeek-R1's accuracy on total questions was 78.3%, nearing the 79.3% of the higher-performing senior expert. Notably, it excelled at complex reasoning-based MCQ, demonstrating an advantage over senior experts with an accuracy of 64.4%, compared to 58.7% (SMLS-1) and 56.7% (SMLS-2). While ChatGPT-5 was the fastest model, DeepSeek-R1 exhibited intermediate efficiency, aligning with human experts on SCQ but requiring more time for MCQ. In terms of NLG, DeepSeek-R1 consistently achieved the highest scores, with ROUGE-L scores of 0.36 ± 0.14 (Total Q), 0.33 ± 0.15 (SCQ), and 0.38 ± 0.13 (MCQ), and METEOR scores of 0.53 ± 0.19 (Total Q), 0.40 ± 0.17 (SCQ), and 0.63 ± 0.14 (MCQ). Furthermore, it significantly outperformed all other LLMs in logical reasoning comprehensiveness. A critical strength was its consistent integration of key negative findings, vital for diagnosis. Conclusion: DeepSeek-R1 approaches or even surpasses senior expert performance in certain tasks, showing strong potential as an effective tool for education and assessment despite slower processing times.
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.