ArticleScientific reports2025
Industrial applications of large language models.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 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
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Emerging Roles of AI in Self-Directed Stress Management: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Genolator enables protein function interpretation using a multimodal large language model fusing genomic and structural interpretation with natural language interaction.Genome biology · 2026Article
- The use of artificial intelligence (AI) in neuropsychiatric drug discovery: current challenges and future directions.Translational psychiatry · 2026Review
- Value of the DeepSeek-R1 large language model in extracting structured data from magnetic resonance imaging reports of rectal cancer and assisting in tumor staging.Quantitative imaging in medicine and surgery · 2026Article
- Development and psychometric analysis of 5-item Artificial Intelligence Attitude (AIAT-5) for students.BMC psychology · 2026Article
- A Large Language Model-Powered Multiagent Framework Emulating Standardized Patients in Clinical Communication Skills Training: Development and Evaluation Study.Journal of medical Internet research · 2026Article
- Screening Routine Clinical Notes for Epilepsy Surgery Candidates Using Large Language Models.Annals of clinical and translational neurology · 2026Article
- A multi-agent large language model framework for intelligent vendor evaluation and risk-aware procurement decisions.Scientific reports · 2026Article
- Article
- ChatGPT in liver transplantation: Current applications, limitations, and future directions.World journal of transplantation · 2026Review
- When Artificial Intelligence Fails the Future: How Unchecked Artificial Intelligence Could Amplify Inequality in Medical Education.Plastic and reconstructive surgery. Global open · 2026Article
- Dynamic adaptation of non standard service tasks through reinforcement learning driven task technology fit and service interaction.Scientific reports · 2026Article
- A Review of Federated Large Language Models for Industry 4.0.Sensors (Basel, Switzerland) · 2026Review
- Small language models applied in text summarization task of health-related news to improve public health audit: an experimental case study.Frontiers in artificial intelligence · 2026Article
- Sino-US-DrugQA: A benchmark for evaluating large language models in cross-jurisdictional pharmaceutical regulation.PloS one · 2026Article
- A unified framework for interpretable elevator fault diagnosis and predictive maintenance via style-aware CoT fine-tuning.PloS one · 2026Article
- Quantifying surprise in clinical care: Detecting highly informative events in electronic health records with foundation models.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2026Article
- Linear accelerator (linac) downtime analysis assisted with a Large Language Model (LLM).Journal of applied clinical medical physics · 2025Article
- Feedback-integrated prompt optimiser for problem formulation.Scientific reports · 2025Article
- Human evaluation of large language models in healthcare: gaps, challenges, and the need for standardization.npj health systems · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Large language models (LLMs) are artificial intelligence (AI) based computational models designed to understand and generate human like text. With billions of training parameters, LLMs excel in identifying intricate language patterns, enabling remarkable performance across a variety of natural language processing (NLP) tasks. After the introduction of transformer architectures, they are impacting the industry with their text generation capabilities. LLMs play an innovative role across various industries by automating NLP tasks. In healthcare, they assist in diagnosing diseases, personalizing treatment plans, and managing patient data. LLMs provide predictive maintenance in automotive industry. LLMs provide recommendation systems, and consumer behavior analyzers. LLMs facilitates researchers and offer personalized learning experiences in education. In finance and banking, LLMs are used for fraud detection, customer service automation, and risk management. LLMs are driving significant advancements across the industries by automating tasks, improving accuracy, and providing deeper insights. Despite these advancements, LLMs face challenges such as ethical concerns, biases in training data, and significant computational resource requirements, which must be addressed to ensure impartial and sustainable deployment. This study provides a comprehensive analysis of LLMs, their evolution, and their diverse applications across industries, offering researchers valuable insights into their transformative potential and the accompanying limitations.
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.