English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Recent advances in large language models (LLMs) have shown impressive abilityin biomedical question-answering, but have not been adequately investigated formore specific biomedical applications. This study investigates the performanceof LLMs such as the ChatGPT family of models (GPT-3.5s, GPT-4) in biomedicaltasks beyond question-answering. Because no patient data can be passed to theOpenAI API public interface, we evaluated model performance with over 10000samples as proxies for two fundamental tasks in the clinical domain -classification and reasoning. The first task is classifying whether statementsof clinical and policy recommendations in scientific literature constitutehealth advice. The second task is causal relation detection from the biomedicalliterature. We compared LLMs with simpler models, such as bag-of-words (BoW)with logistic regression, and fine-tuned BioBERT models. Despite the excitementaround viral ChatGPT, we found that fine-tuning for two fundamental NLP tasksremained the best strategy. The simple BoW model performed on par with the mostcomplex LLM prompting. Prompt engineering required significant investment.

Evaluation of ChatGPT Family of Models for Biomedical Reasoning and Classification