Medical Natural Language Processing
Research focusing on NLP techniques for processing medical text, clinical narratives, and biomedical literature
Medical Natural Language Processing
Research on Large Language Models in Medical Natural Language Processing
Specialized Transformers for Medical Numbers
How CamemBERT-bio outperforms large LLMs in clinical numerical processing
LLM-Powered Diarization Correction
Improving speaker identification in transcribed conversations
Detecting ChatGPT in Academia
Uncovering LLM use in scientific writing through vocabulary analysis
Zero-Shot Clinical Entity Recognition
Leveraging Open LLMs for Medical Text Analysis
Improving Biomedical Information Extraction
A Structure-Aware Approach to Medical Text Analysis Using LLMs
SciLitLLM: Enhancing AI for Scientific Research
Specialized LLMs for Advanced Scientific Literature Understanding
Improving Medical Coding with Interpretable AI
Making complex medical predictions both accurate and understandable
Boosting Phenotype Normalization Accuracy
A Simplified Retrieval Approach for Enhanced LLM Performance
Enhancing LLM Instruction Tuning
A novel Mixup approach that improves performance without expensive data
Advancing Multimodal AI Evaluation
A new benchmark for vision-language model capabilities
Multilingual Medical AI for All
Democratizing Medical LLMs Across 50 Languages
BioMistral-NLU: Advancing Medical AI
Specialized language models for better healthcare understanding
Transparent AI for Medical Coding
Enhancing Interpretability in Automated Medical Code Assignment
Optimizing HPV Vaccine Stance Detection with LLMs
Comparing In-Context Learning vs. Fine-Tuning for Medical Content Moderation
Open-Source LLMs vs. Specialized Models for Medical Translation
Evaluating performance across language resource availability
Boosting NLP in Low-Resource Medical Settings
Using Pseudo-Annotations to Enhance Named Entity Detection with LLMs
AI-Powered Medical Documentation
LLMs Automating CT Simulation Summaries in Radiation Oncology
Optimizing Biomedical Translation
Data Filtering Techniques for English-Polish LLM Translation in Healthcare
Specialized Medical LLMs
Two-Stage Approach for Accurate Medical Question Answering
AI That Understands Patient Histories
Zero-shot LLMs for Clinical Text Summarization with Temporal Context
The Human Core of Large Language Models
Why larger LMs actually mimic human cognition better than we thought
Enhancing Radiology AI with Uncertainty Awareness
Improving LLM accuracy in complex medical text extraction using agent-based approaches
Bridging the Language Divide in NLP
Advancing multilingual capabilities for under-resourced languages
Bridging the Gap in African-Accented Healthcare Conversations
New benchmark dataset reveals performance gaps in speech recognition technologies
Transforming Clinical Data with LLMs
Fine-tuning language models for structured medical information extraction
LLMs and the Erosion of Linguistic Diversity
How AI writing tools are homogenizing language patterns
AI-Powered Medical Data Extraction
Using Large Language Models to Unlock Clinical Insights
Solving the Meeting Data Dilemma
Using AI agents to create synthetic meeting transcripts
Enhancing Healthcare Text Classification with LLMs
Using ensemble learning to improve multi-label classification of medical narratives
Advancing ICD Coding for Chinese Medical Records
Multi-axial knowledge with evidence verification approach
Smarter Medical NLP with Multi-Mode Retrieval
Enhancing LLM performance in biomedical contexts through strategic example selection
Extracting Medical Insights from Clinical Notes
A new transformer model enhances clinical text analysis for better patient outcomes
Controlling Clinical Text Generation
Enhancing LLM accuracy while reducing clinician oversight
LLMs as Brain Interpreters
Using AI to Decode How Our Brains Process Visual Information
AI-Powered Scientific Knowledge Mining
Transforming Neurosurgical Literature into Practical Applications
Revolutionizing Machine Translation with Reasoning
How R1-T1 framework enhances LLM translation through human-like reasoning
Brain-Inspired AI Transitions
Discovering How LLMs Learn Like Human Brains
Hacking Search Engines with AI
Training LLMs to optimize search queries through reinforcement learning
NeuroLit Navigator: Enhancing Medical Systematic Reviews
A neurosymbolic approach that boosts literature search accuracy by combining LLMs with medical ontologies
AI-Powered Clinical Trial Recruitment
Leveraging NLP to transform patient eligibility matching
AI-Powered Social Media Theme Discovery
Using Generative AI to Extract Meaningful Themes from Social Media
EchoQA: Transforming Cardiology with AI
A massive dataset powering medical question-answering systems
Zero-Shot Document Intelligence for Scientific Research
Enabling non-ML experts to extract insights from complex research papers
The Gap Between LLMs and Expert Biomedical Annotation
Why frontier language models struggle with specialized biomedical text mining
HILGEN: Enhancing Biomedical NER with Knowledge-Driven Data
Combining medical knowledge bases with LLMs for improved entity recognition
Improving LLM Translation for Specialist Domains
Comparing Retrieval vs. Generation for Domain Knowledge Adaptation
GEMA-Score: Revolutionizing Radiology Report Evaluation
A granular, explainable approach to assessing AI-generated medical reports
How Well Can AI 'See' Through Words?
Evaluating multimodal perception capabilities in advanced LLMs
LLMs Meet Medieval Texts
Bridging AI and Historical Language Processing
Taming the Long Tail in Multi-Label Classification
A Co-Occurrence Reranking Approach for Rare Label Detection
The Emotional Influence of Prompts
How prompt sentiment shapes LLM outputs across domains
AI-Powered Clinical Trial Matching
Empowering Patients to Find Relevant Trials Using Natural Language Processing
Breaking Language Barriers in Medical AI
Enhancing LLMs with Knowledge Graphs for Multilingual Medical Q&A
Enhancing Relation Extraction in Medical Data
A Novel Dual-Encoder Approach with Instance-Adapted Descriptions
Explainable AI for Medical Coding
Reframing ICD coding as an entity linking challenge for transparency
Unlocking Multilingual Medical Data
Building Low-Resource Information Extraction for Clinical Cases
Maximizing Medical AI with Limited Data
Fine-tuning small LLMs achieves strong results on specialized medical tasks
Unveiling Hidden Meanings in Digital Content
Multimodal euphemism identification for enhanced content moderation
Extracting Patient History with Clinical LLMs
Comparing large language models for structured medical information extraction
Enhancing Biomedical NLP with Synthetic Data
Using AI Debate to Overcome Data Scarcity in Medical Research
Evaluating LLMs vs Encoders for Biomedical Recognition
Comparing state-of-the-art approaches for identifying medical entities in text
GLiNER-biomed: Revolutionizing Biomedical Entity Recognition
Efficient, Adaptable Models for Open Biomedical NER
IHC-LLMiner: Mining Cancer Data with AI
Automating immunohistochemical profile extraction using large language models
Boosting Clinical NLP Without Domain Expertise
Synthesized guidelines make LLMs better at medical information extraction
Revolutionizing Medical Symptom Coding with LLMs
Using task context to enhance symptom identification in clinical text
Breaking Language Barriers in Healthcare
First large-scale multilingual medical speech translation dataset
Adapting LLMs for Specialized Domains
Beyond General-Purpose AI: Making Language Models Domain-Smart
Clinical ModernBERT: Advancing Medical AI
A powerful biomedical language model with 8K token context
AI That Catches Radiology Report Errors
Using GPT-4 to improve medical documentation safety
Benchmarking LLM Summarization Power
A multi-dimensional evaluation across 17 large language models
Efficient Personality Detection with LLMs
Parameter-Efficient Fine-Tuning for Enhanced Security Applications
Cognitive Signals Meet AI Language Models
Enhancing AI with Human Cognitive Processing Patterns
Enhancing Clinical Document Classification with Reasoning LLMs
Comparing reasoning vs. non-reasoning language models for medical diagnoses
Phi-3-Mini: Small but Mighty for Medical Text
Evaluating a resource-efficient SLM for healthcare content identification
CliniChat: Revolutionizing Clinical Interviews with AI
A framework using multi-source knowledge to create and evaluate realistic clinical dialogues
