The field of natural language processing is moving towards more accurate and reliable fact-checking and clinical decision support systems. Recent studies have shown that large language models (LLMs) can be fine-tuned to achieve state-of-the-art performance in various tasks, including fact-checking, question answering, and medical report generation. However, these models often struggle with factual consistency and context alignment, highlighting the need for more robust evaluation metrics and training methods. Notable papers in this area include the development of novel frameworks for fact-checking, such as Trification and SAFE, which leverage tree-based strategies and retrieval-augmented generation to improve accuracy and reliability. Additionally, the introduction of benchmarks like RxBench and TCM-BEST4SDT has enabled more comprehensive evaluations of LLMs in clinical decision support and traditional Chinese medicine. Furthermore, research on multi-LLM collaboration and safety-aware decoding has shown promising results in improving the reliability and trustworthiness of AI-powered clinical decision support systems. Some papers that are particularly noteworthy in this regard include 'Use of Retrieval-Augmented Large Language Model Agent for Long-Form COVID-19 Fact-Checking' and 'Trification: A Comprehensive Tree-based Strategy Planner and Structural Verification for Fact-Checking', which demonstrate innovative approaches to fact-checking and clinical decision support.
Advancements in AI-Powered Fact-Checking and Clinical Decision Support
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Use of Retrieval-Augmented Large Language Model Agent for Long-Form COVID-19 Fact-Checking
Trification: A Comprehensive Tree-based Strategy Planner and Structural Verification for Fact-Checking
Comparative Analysis of 47 Context-Based Question Answer Models Across 8 Diverse Datasets
Statistical NLP for Optimization of Clinical Trial Success Prediction in Pharmaceutical R&D
Text Mining Analysis of Symptom Patterns in Medical Chatbot Conversations
Generalist Large Language Models Outperform Clinical Tools on Medical Benchmarks
Conveying Imagistic Thinking in Traditional Chinese Medicine Translation: A Prompt Engineering and LLM-Based Evaluation Framework
First, do NOHARM: towards clinically safe large language models
In-context Inverse Optimality for Fair Digital Twins: A Preference-based approach
Human-Level and Beyond: Benchmarking Large Language Models Against Clinical Pharmacists in Prescription Review
HealthContradict: Evaluating Biomedical Knowledge Conflicts in Language Models
Memory-Augmented Knowledge Fusion with Safety-Aware Decoding for Domain-Adaptive Question Answering
Beyond N-grams: A Hierarchical Reward Learning Framework for Clinically-Aware Medical Report Generation
Towards Unification of Hallucination Detection and Fact Verification for Large Language Models
Radiologist Copilot: An Agentic Assistant with Orchestrated Tools for Radiology Reporting with Quality Control
A benchmark dataset for evaluating Syndrome Differentiation and Treatment in large language models
Thucy: An LLM-based Multi-Agent System for Claim Verification across Relational Databases
AlignCheck: a Semantic Open-Domain Metric for Factual Consistency Assessment
AR-Med: Automated Relevance Enhancement in Medical Search via LLM-Driven Information Augmentation
Balancing Safety and Helpfulness in Healthcare AI Assistants through Iterative Preference Alignment
UW-BioNLP at ChemoTimelines 2025: Thinking, Fine-Tuning, and Dictionary-Enhanced LLM Systems for Chemotherapy Timeline Extraction
Factuality and Transparency Are All RAG Needs! Self-Explaining Contrastive Evidence Re-ranking
Multi-LLM Collaboration for Medication Recommendation