AI medical health

o1 in Medicine is an artificial intelligence model focused on the medical field, aiming to improve medical data processing capabilities and diagnostic accuracy through advanced language model technology. The model, developed by researchers at UC Santa Cruz, the University of Edinburgh, and the National Institutes of Health, demonstrated its potential for application in the medical field by testing on multiple medical data sets. The main advantages of the o1 model include high accuracy, multi-language support, and in-depth understanding of complex medical problems. The development background of this model is based on the current needs in the medical field for efficient and accurate data processing and analysis, especially in diagnosis and treatment recommendations. At present, the research and application of this model are still in the preliminary stage, but its application in medical education and clinical practice has broad prospects.

The UltraMedical project aims to develop specialized general models in the biomedical field that are designed to answer questions relevant to examinations, clinical scenarios, and research questions, while maintaining a broad general knowledge base to effectively address cross-domain problems. By using advanced alignment techniques, including supervised fine-tuning (SFT), direct preference optimization (DPO), and odds ratio preference optimization (ORPO), large language models are trained on the UltraMedical dataset to create powerful and versatile models that effectively serve the needs of the biomedical community.

Prov-GigaPath is a full-slice base model for digital pathology research that is trained with real-world data and is designed to support AI researchers in their research on pathology base models and digital pathology slide data encoding. The model was developed by multiple authors and published in the journal Nature. It is not intended for clinical care or any clinical decision-making purposes and is for research use only.

Meditron is an open source medical large language model (LLM) suite. It adapts Llama-2 to the medical domain through continuous pre-training on a comprehensively screened medical corpus, including selected PubMed articles and abstracts, a new internationally recognized medical guideline dataset, and a general domain corpus. Meditron-70B was fine-tuned on relevant data and outperformed Llama-2-70B, GPT-3.5 and Flan-PaLM.