Medical foundation large language models for comprehensive text analysis and beyond

Recent advancements in large language models (LLMs) show significant potential in medical applications but are hindered by limited specialized medical knowledge. We present Me-LLaMA, a family of open-source medical LLMs integrating extensive domain-specific knowledge with robust instruction-following capabilities. Me-LLaMA is developed through continual pretraining and instruction tuning of LLaMA2 models using diverse biomedical and clinical data sources (e.g., biomedical literature and clinical notes). We evaluated Me-LLaMA on six text analysis tasks using 12 benchmarks (e.g., PubMedQA and MIMIC-CXR) and assessed its clinical utility in complex case diagnosis through automatic and human evaluations. Me-LLaMA…

• PO
  Patient-Centered Outcomes Research Institute

  Award: PCORI RI-FLORIDA-01-PS1, PCORI ME-2018C3-14754
• NI
  National Institutes of Health

  Award: 1RF1AG072799, 1R01AG078154, R01AG073435, R01LM013519, RF1AG084178, R01AG083039, R01CA284646, R01AI172875, R01AG080991, R01AG080624, R01AG080429, 1K99LM01402, 1K99LM014614-01,NIH/NCATS UL1 TR001427, CDC U18 DP006512