ArticleFrontiers in digital health2026
Performance and limitations of four large language models in genetic counseling for thalassemia.
Article in Frontiers in digital health, 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
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In regions with high thalassemia prevalence, such as southern China and Southeast Asia, chronic shortages of professional genetic counseling resources have driven interest in large language models (LLMs) as auxiliary tools, yet their performance and safety boundaries in this setting remain uncharacterized. This single-center retrospective study evaluated four LLMs (ChatGPT-5.2 Thinking, DeepSeek-V3.2 chat, Gemini 3 Flash, and Grok 4.1 Fast) using 1,080 standardized knowledge questions administered across five independent sessions and 150 real-world clinical cases scored by six senior experts across eight dimensions. All models exceeded 90% accuracy on single-choice and true-false questions. Between-model differences were most pronounced in multiple-choice questions, where ChatGPT-5.2 Thinking achieved the highest accuracy (87.28% ± 1.69%), significantly outperforming Grok 4.1 Fast (72.06% ± 1.69%,
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.