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

Large language models have exhibited exceptional performance on variousNatural Language Processing (NLP) tasks, leveraging techniques such as thepre-training, and instruction fine-tuning. Despite these advances, theireffectiveness in medical applications is limited, due to challenges such asfactual inaccuracies, reasoning abilities, and lack grounding in real-worldexperience. In this study, we present ClinicalGPT, a language model explicitlydesigned and optimized for clinical scenarios. By incorporating extensive anddiverse real-world data, such as medical records, domain-specific knowledge,and multi-round dialogue consultations in the training process, ClinicalGPT isbetter prepared to handle multiple clinical task. Furthermore, we introduce acomprehensive evaluation framework that includes medical knowledgequestion-answering, medical exams, patient consultations, and diagnosticanalysis of medical records. Our results demonstrate that ClinicalGPTsignificantly outperforms other models in these tasks, highlighting theeffectiveness of our approach in adapting large language models to the criticaldomain of healthcare.

ClinicalGPT: Large Language Models Finetuned with Diverse Medical Data and Comprehensive Evaluation