ArticleiScience2024
Language model and its interpretability in biomedicine: A scoping review.
Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of ethical considerations of large language models in healthcare and medicine.Frontiers in digital health · 2025Pooled it
- Application of Sparse Autoencoders to Enhance Mechanistic Interpretability of Large Language Models in Medicine.JMIR AI · 2026Article
- From Sensing to Sense-Making: A Framework for On-Person Intelligence with Wearable Biosensors and Edge LLMs.Sensors (Basel, Switzerland) · 2026Article
- Human versus artificial intelligence in oral pathology diagnosis: a comparative study of ChatGPT, Grok, and MANUS.Scientific reports · 2026Article
- Interpretable depression assessment using a large language model.PLOS digital health · 2026Article
- Understanding mental health discourse on Reddit with transformers and explainability.Scientific reports · 2026Article
- Association of the platelet-to-albumin ratio with diabetic nephropathy lesions via a fine-tuning-free large language model framework.Frontiers in medicine · 2026Article
- Language Modelling Techniques for Analysing the Impact of Human Genetic Variation.Bioinformatics and biology insights · 2025Review
- BERT-based language model for accurate drug adverse event extraction from social media: implementation, evaluation, and contributions to pharmacovigilance practices.Frontiers in public health · 2024Article
- Decade-long insights into AI for orthopedic rehabilitation mapping research networks and future trajectories.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
With advancements in large language models, artificial intelligence (AI) is undergoing a paradigm shift where AI models can be repurposed with minimal effort across various downstream tasks. This provides great promise in learning generally useful representations from biomedical corpora, at scale, which would empower AI solutions in healthcare and biomedical research. Nonetheless, our understanding of how they work, when they fail, and what they are capable of remains underexplored due to their emergent properties. Consequently, there is a need to comprehensively examine the use of language models in biomedicine. This review aims to summarize existing studies of language models in biomedicine and identify topics ripe for future research, along with the technical and analytical challenges w.r.t. interpretability. We expect this review to help researchers and practitioners better understand the landscape of language models in biomedicine and what methods are available to enhance the interpretability of their models.
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.