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Found 9 papers, 3 papers with code
Building an Ethical and Trustworthy Biomedical AI Ecosystem for the Translational and Clinical Integration of Foundational Models
We explore strategies that can be implemented throughout the biomedical AI pipeline to effectively tackle these challenges, ensuring that these FMs are translated responsibly into clinical and translational settings.
Explainable Biomedical Hypothesis Generation via Retrieval Augmented Generation enabled Large Language Models
The vast amount of biomedical information available today presents a significant challenge for investigators seeking to digest, process, and understand these findings effectively.
CliBench: A Multifaceted and Multigranular Evaluation of Large Language Models for Clinical Decision Making
The integration of Artificial Intelligence (AI), especially Large Language Models (LLMs), into the clinical diagnosis process offers significant potential to improve the efficiency and accessibility of medical care.
Know2BIO: A Comprehensive Dual-View Benchmark for Evolving Biomedical Knowledge Graphs
Knowledge graphs (KGs) have emerged as a powerful framework for representing and integrating complex biomedical information.
Clinical Named Entity Recognition using Contextualized Token Representations
To facilitate various downstream applications using clinical case reports (CCRs), we pre-train two deep contextualized language models, Clinical Embeddings from Language Model (C-ELMo) and Clinical Contextual String Embeddings (C-Flair) using the clinical-related corpus from the PubMed Central.
CREATe: Clinical Report Extraction and Annotation Technology
Clinical case reports are written descriptions of the unique aspects of a particular clinical case, playing an essential role in sharing clinical experiences about atypical disease phenotypes and new therapies.
Clinical Temporal Relation Extraction with Probabilistic Soft Logic Regularization and Global Inference
There has been a steady need in the medical community to precisely extract the temporal relations between clinical events.
EVIDENCEMINER: Textual Evidence Discovery for Life Sciences
EVIDENCEMINER is constructed in a completely automated way without any human effort for training data annotation.
