ArticleMedicine2026
Comparative evaluation of large language models for text-based diagnostic reasoning in fetal central nervous system MRI: A retrospective single-center diagnostic accuracy study.
Article in Medicine, 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
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Fetal magnetic resonance imaging (MRI) plays an important role in evaluating prenatal central nervous system (CNS) abnormalities, but expert interpretation and counseling remain challenging. Large language models (LLMs) may support text-based diagnostic reasoning, yet their performance in fetal CNS MRI has not been well characterized. The study aimed to compare ChatGPT, Gemini, and DeepSeek in text-based diagnostic reasoning for fetal CNS MRI cases. This retrospective single-center study included 85 fetal MRI cases with postnatal diagnostic confirmation. Each case was converted into a standardized text summary containing MRI findings and essential clinical information, without image input. The 3 LLMs generated ranked differential diagnoses, diagnostic reasoning, management recommendations, prognostic counseling points, and clinical cautions. Anonymized outputs were independently evaluated under blinded conditions for principal diagnosis matching, diagnostic accuracy, clinical reasoning quality, clinical suggestion utility, and communication style and safety. DeepSeek achieved the highest principal diagnosis matching rate (66/85, 77.6%), followed by Gemini (59/85, 69.4%) and ChatGPT (52/85, 61.2%). The overall difference was significant (Cochran's Q = 6.2553, P = .0438), although no pairwise comparison remained significant after Holm correction. DeepSeek showed the highest diagnostic accuracy and reasoning quality, whereas Gemini performed best in clinical suggestion utility and communication style and safety. Exploratory correlation analyses suggested model-specific associations among evaluation metrics. LLM performance in fetal CNS MRI text-based reasoning was multidimensional and model-dependent. These findings support further supervised evaluation of LLMs as text-based decision-support tools but do not support autonomous clinical diagnosis or counseling.
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.