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Found 8 papers, 1 papers with code
Deciphering genomic codes using advanced NLP techniques: a scoping review
The goal of this review is to assess data and model accessibility in the most recent literature, gaining a better understanding of the existing capabilities and constraints of these tools in processing genomic sequencing data.
Enhancing disease detection in radiology reports through fine-tuning lightweight LLM on weak labels
These findings demonstrate the potential of fine-tuning LLMs with synthetic labels, offering a promising direction for future research on LLM specialization in the medical domain.
Closing the gap between open-source and commercial large language models for medical evidence summarization
Large language models (LLMs) hold great promise in summarizing medical evidence.
Uncovering Misattributed Suicide Causes through Annotation Inconsistency Detection in Death Investigation Notes
Data accuracy is essential for scientific research and policy development.
Evaluating GPT-4 with Vision on Detection of Radiological Findings on Chest Radiographs
The study examines the application of GPT-4V, a multi-modal large language model equipped with visual recognition, in detecting radiological findings from a set of 100 chest radiographs and suggests that GPT-4V is currently not ready for real-world diagnostic usage in interpreting chest radiographs.
Hidden flaws behind expert-level accuracy of multimodal GPT-4 vision in medicine
GPT-4V also performs well in cases where physicians incorrectly answer, with over 78% accuracy.
A Span-based Model for Extracting Overlapping PICO Entities from RCT Publications
On the PICO-Corpus, PICOX obtained higher recall and F1 scores than the baseline and improved the micro recall score from 56. 66 to 67. 33.
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" $\unicode{x2013}$ there are a few common findings followed by many more relatively rare conditions.
