ArticleBriefings in bioinformatics2026
Reflections on the use of LLMs for cell annotation.
Article in Briefings in bioinformatics, 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
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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
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Who cites it
1 citing paper in PubMed.
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Authors and funding
1 author.
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No grant is acknowledged in the PubMed record.
Abstract
This letter comments on the recently published AICellType platform for large language model (LLM)-based cell type annotation in single-cell and spatial transcriptomics. While appreciating the authors' systematic benchmarking and practical contribution, concerns are raised regarding the continued dependence on proprietary commercial LLMs such as Claude 3.5 Sonnet for biomedical annotation. Greater emphasis is suggested on open-source biomedical LLMs, multimodal vision-language models, local deployment, reproducibility, privacy preservation, and regulatory compliance to ensure more transparent, reliable, and sustainable medical annotation systems for translational bioinformatics.
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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.