2 citations · 4 across the 5 of their papers we have counts for
6 papers · 1 filter
Building Trust in Clinical LLMs: Bias Analysis and Dataset Transparency
Svetlana Maslenkova, Clement Christophe, Marco AF Pimentel +7
Large language models offer transformative potential for healthcare, yet their responsible and equitable development depends critically on a deeper understanding of how training da…
Named Clinical Entity Recognition Benchmark
Wadood M Abdul, Marco AF Pimentel, Muhammad Umar Salman +6
This technical report introduces a Named Clinical Entity Recognition Benchmark for evaluating language models in healthcare, addressing the crucial natural language processing (NLP…
Beyond Fine-tuning: Unleashing the Potential of Continuous Pretraining for Clinical LLMs
Clément Christophe, Tathagata Raha, Svetlana Maslenkova +4
Large Language Models (LLMs) have demonstrated significant potential in transforming clinical applications. In this study, we investigate the efficacy of four techniques in adaptin…
MEDIC: Comprehensive Evaluation of Leading Indicators for LLM Safety and Utility in Clinical Applications
Praveenkumar Kanithi, Clément Christophe, Marco AF Pimentel +8
While Large Language Models (LLMs) achieve superhuman performance on standardized medical licensing exams, these static benchmarks have become saturated and increasingly disconnect…
Med42-v2: A Suite of Clinical LLMs
Clément Christophe, Praveen K Kanithi, Tathagata Raha +2
Med42-v2 introduces a suite of clinical large language models (LLMs) designed to address the limitations of generic models in healthcare settings. These models are built on Llama3…
Med42 -- Evaluating Fine-Tuning Strategies for Medical LLMs: Full-Parameter vs. Parameter-Efficient Approaches
Clément Christophe, Praveen K Kanithi, Prateek Munjal +13
This study presents a comprehensive analysis and comparison of two predominant fine-tuning methodologies - full-parameter fine-tuning and parameter-efficient tuning - within the co…