LLM for Clinical Evidence Extraction and Summarisation

In medicine, our understanding of diseases, treatments, and preventive measures is perpetually advancing. Despite this, the existing framework of evidence-based medicine (EBM) faces formidable challenges from information overload, as no clinician can catch up with the explosive publication pace. In this talk, I will discuss our research on the fine-tuning of LLMs for effective medical evidence extraction and summarization.

In medicine, our understanding of diseases, treatments, and preventive measures is perpetually advancing. Despite this, the existing framework of evidence-based medicine (EBM) faces formidable challenges from information overload, as no clinician can catch up with the explosive publication pace. In this talk, I will discuss our research on the fine-tuning of LLMs for effective medical evidence extraction and summarization.

In medicine, our understanding of diseases, treatments, and preventive measures is perpetually advancing. Despite this, the existing framework of evidence-based medicine (EBM) faces formidable challenges from information overload, as no clinician can catch up with the explosive publication pace. In this talk, I will discuss our research on the fine-tuning of LLMs for effective medical evidence extraction and summarization.

In medicine, our understanding of diseases, treatments, and preventive measures is perpetually advancing. Despite this, the existing framework of evidence-based medicine (EBM) faces formidable challenges from information overload, as no clinician can catch up with the explosive publication pace. In this talk, I will discuss our research on the fine-tuning of LLMs for effective medical evidence extraction and summarization.