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• Medical SAM 2: Segment medical images as video

  • This paper introduces Medical SAM 2 (MedSAM-2), an improved segmentation model based on the SAM2 framework and designed to improve the segmentation of both 2D and 3D medical images. This is achieved by treating medical images as video sequences.

• MedGraphRAG: Graph-enhanced medical RAG

  • In this paper, we introduce MedGraphRAG, a RAG framework specifically designed for the medical domain. It can handle long contexts, reduce hallucinations, and provide evidence-based responses, ensuring safe and reliable AI usage in healthcare.

• Multimodal LLM for medical time series

  • This paper introduces MedTsLLM, a general multimodal LLM framework that effectively integrates time series data and rich contextual information in the form of text.

• ECG-FM: Open Electrocardiogram Basic Model

  • This paper introduces ECG-FM, an open transformer-based baseline model for electrocardiogram (ECG) analysis. Using the newly collected UHN-ECG dataset with over 700k ECGs

• Private & Safe Healthcare RAG

  • In this work, researchers introduce the Retrieval-Augmented Thought Process (RATP). Given access to external knowledge, RATP formulates the thought generation of LLMs as a multi-step decision-making process. RATP addresses a critical challenge: leveraging LLMs in healthcare while protecting sensitive patient data.

• Comprehensive multimodal medical AI benchmark

  • This paper presents GMAI-MMBench, a comprehensive benchmark for general medical AI. It is built with 285 datasets covering 39 medical imaging modalities, 18 clinically related tasks, 18 divisions and 4 perceptual granularities in a Visual Question Answering (VQA) format.

See the full thread in detail: https://x.com/OpenlifesciAI/status/1824790439527887073