RadAdapt: Radiology report summarization via lightweight domain adaptation of large language models
We systematically investigate lightweight strategies to adapt large language models (LLMs)
for the task of radiology report summarization (RRS). Specifically, we focus on domain …
for the task of radiology report summarization (RRS). Specifically, we focus on domain …
Evaluating large language models for radiology natural language processing
The rise of large language models (LLMs) has marked a pivotal shift in the field of natural
language processing (NLP). LLMs have revolutionized a multitude of domains, and they …
language processing (NLP). LLMs have revolutionized a multitude of domains, and they …
A survey of pre-trained language models for processing scientific text
The number of Language Models (LMs) dedicated to processing scientific text is on the rise.
Keeping pace with the rapid growth of scientific LMs (SciLMs) has become a daunting task …
Keeping pace with the rapid growth of scientific LMs (SciLMs) has become a daunting task …
Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge
Many real-world image recognition problems, such as diagnostic medical imaging exams,
are “long-tailed”–there are a few common findings followed by many more relatively rare …
are “long-tailed”–there are a few common findings followed by many more relatively rare …
Parameter-efficient fine-tuning of llama for the clinical domain
Adapting pretrained language models to novel domains, such as clinical applications,
traditionally involves retraining their entire set of parameters. Parameter-Efficient Fine …
traditionally involves retraining their entire set of parameters. Parameter-Efficient Fine …
Data-centric foundation models in computational healthcare: A survey
The advent of foundation models (FMs) as an emerging suite of AI techniques has struck a
wave of opportunities in computational healthcare. The interactive nature of these models …
wave of opportunities in computational healthcare. The interactive nature of these models …
Leveraging a medical knowledge graph into large language models for diagnosis prediction
Electronic Health Records (EHRs) and routine documentation practices play a vital role in
patients' daily care, providing a holistic record of health, diagnoses, and treatment. However …
patients' daily care, providing a holistic record of health, diagnoses, and treatment. However …
[HTML][HTML] Hierarchical pretraining on multimodal electronic health records
Pretraining has proven to be a powerful technique in natural language processing (NLP),
exhibiting remarkable success in various NLP downstream tasks. However, in the medical …
exhibiting remarkable success in various NLP downstream tasks. However, in the medical …
[HTML][HTML] The Future of Intelligent Healthcare: A Systematic Analysis and Discussion on the Integration and Impact of Robots Using Large Language Models for …
S Pashangpour, G Nejat - Robotics, 2024 - mdpi.com
The potential use of large language models (LLMs) in healthcare robotics can help address
the significant demand put on healthcare systems around the world with respect to an aging …
the significant demand put on healthcare systems around the world with respect to an aging …
Large language models and control mechanisms improve text readability of biomedical abstracts
Biomedical literature often uses complex language and inaccessible professional
terminologies. That is why simplification plays an important role in improving public health …
terminologies. That is why simplification plays an important role in improving public health …