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

We propose DISC-MedLLM, a comprehensive solution that leverages LargeLanguage Models (LLMs) to provide accurate and truthful medical response inend-to-end conversational healthcare services. To construct high-qualitySupervised Fine-Tuning (SFT) datasets, we employ three strategies: utilizingmedical knowledge-graphs, reconstructing real-world dialogues, andincorporating human-guided preference rephrasing. These datasets areinstrumental in training DISC-MedLLM, surpassing existing medical LLMs in bothsingle-turn and multi-turn consultation scenarios. Extensive experimentalresults demonstrate the effectiveness of the proposed model in bridging the gapbetween general language models and real-world medical consultation.Additionally, we release the constructed dataset and model weights to furthercontribute to research and development. Further details and resources can befound at https://github.com/FudanDISC/DISC-MedLLM

DISC-MedLLM: Bridging General Large Language Models and Real-World Medical Consultation