Tag:
Biomedicine
AI
When Good Intentions Go Wrong: The Dangers of LLMs Spreading Inaccurate Medical Information through Sycophantic Responses
To evaluate language models' familiarity with drugs, we utilized the RABBITS30 dataset, comprising 550 drugs with brand-generic mappings. Using various pre-training corpora and the...
AI
Leveraging a Fine-Tuned Large Language Model for Symptom-Based Depression Assessment
This study investigates a BERT-based language model, MADRS-BERT, fine-tuned on German MADRS interview data for predicting depression severity. The model significantly improved prediction accuracy,...
AI
Leveraging Large Language Models to Predict Patient Health Trajectories for Enhanced Digital Twin Applications
DT-GPT is a novel framework that utilizes fine-tuned pre-trained large language models (LLMs) on clinical data to forecast patients' laboratory values. This method is...
AI
Enhanced Drug Analysis through Collaborative Large Language Models
This content discusses the development of DrugGPT, an advanced ensemble model integrating three general-purpose large language models (LLMs) tailored for the medical domain. It...
AI
AI-Enhanced Epitope Prediction: A Comprehensive Review, Comparative Analysis, and Practical Guide for Vaccine Development
The evolution of vaccine development has shifted dramatically, influenced by advances in immunology and technology. A pivotal article by Benn et al. (2020) in...
AI Hacker News
Transforming Healthcare: The Rise of Physician-Algorithm Specialists in the Age of Clinical AI
Unlocking Clinical Value with Algorithmic Consultants
As healthcare evolves, the integration of AI in clinical settings presents unique challenges and opportunities. Enter the...
AI
CARE-AD: An Advanced Multi-Agent Language Model Framework for Predicting Alzheimer’s Disease Through Longitudinal Clinical Notes
This study utilized the VHA Corporate Data Warehouse's extensive EHR database, covering 2000-2022, to investigate Alzheimer’s disease (AD) using a case-control design. Approved by...
AI
Enhancing Depression Screening Through AI-Driven Multi-Modal Information: A Systematic Review and Meta-Analysis
The comprehensive review highlights recent advances in depression detection, employing diverse methodologies, including EEG, facial expressions, and speech analysis. Key studies demonstrate the efficacy...