# Clinical Decision Support

MedGemma-4B-it-Q8_0-GGUF is a GGUF format model converted from google/medgemma-4b-it, specifically designed for image-to-text tasks in the medical field.

A medical reasoning model fine-tuned based on Qwen3-0.6B, specifically designed for clinical decision support and medical education

Citrus is a medical language model that bridges the gap between clinical expertise and AI reasoning by simulating the cognitive processes of medical experts.

GEM is a multimodal large language model that integrates ECG time series, 12-lead ECG images, and text to enable clinical evidence-based ECG interpretation.

An 8B-parameter medical QA model distilled and optimized based on the Llama architecture, enhanced for clinical decision support through specialized medical data fine-tuning

A compact multimodal language model for radiology report generation, capable of producing detailed 'Findings' sections with temporal comparisons

This is a medical domain Q&A model fine-tuned on DeepSeek-R1, specializing in medical-related text generation tasks.

A medical reasoning model fine-tuned based on the DeepSeek-R1-Distill-Llama-8B model, optimized for chain-of-thought reasoning and clinical decision-making capabilities

An enhanced fine-tuned version based on Bio-Medical-Llama-3-8B and Deepseek's distilled Llama 8B, achieving advanced reasoning capabilities through the introduction of Chain-of-Thought (COT) instruction sets, specializing in healthcare and life sciences.

A biomedical-specific model fine-tuned based on Llama-3.2-1B-Instruct, trained on 625,000 samples with 25,000 chain-of-thought (CoT) guidance samples to enhance reasoning capabilities

Llama3.1-Aloe-Beta-8B is an open-source large medical language model that achieves state-of-the-art performance in multiple medical tasks.

A multimodal large language model (MLLM) specifically designed for interpreting electrocardiogram (ECG) images, capable of handling various ECG-related tasks from diverse data sources.

A multimodal biomedical model fine-tuned based on Llama-3-8B-Instruct, supporting text and image processing, suitable for biomedical research and clinical applications.

Bio-Medical-Llama-3-8B is a high-performance biomedical language model fine-tuned from Llama-3-8B-Instruct, specifically designed for biomedical applications with broad coverage of biomedical topics.

Merlin is a 3D vision-language model for computed tomography, pretrained using both structured electronic health records and unstructured radiology reports.

A large language model specialized in clinical medicine, with particular focus on resource-limited regions and humanitarian settings

Palmyra-Med-70B-32k is a large language model specifically designed by Writer for the healthcare domain, featuring a 32k context window and excelling in biomedical benchmark tests.

Palmyra-Med is a large language model specifically designed by Writer for the healthcare and biomedical domain, excelling in biomedical benchmarks and outperforming multiple mainstream models.

A large language model for the medical domain developed by John Snow Labs, optimized based on Microsoft Phi-2 architecture

CheXagent is a foundational model focused on chest X-ray interpretation, designed to assist in medical imaging analysis.

This is a text generation model based on chest X-ray images, capable of generating radiology impression reports from medical imaging.

OpenBioLLM-70B is an advanced open-source language model specifically designed for the biomedical field, fine-tuned based on Meta-Llama-3-70B-Instruct, demonstrating outstanding performance in biomedical tasks.

A large-scale medical domain language model trained by physicians, the first 7b model to surpass the 60% passing score on the MedQA (USMLE) exam

A transformers-based image-to-text model specialized in generating diagnostic impressions from chest X-rays

This model can convert medical images (such as X-rays) into descriptive text to assist in medical diagnosis.

CheXagent is a foundational model focused on chest X-ray interpretation, aiming to improve the accuracy and efficiency of medical imaging analysis.

CheXagent is a foundation model for chest X-ray interpretation, capable of automatically analyzing and interpreting chest X-rays.

CheXagent is a foundational model focused on chest X-ray interpretation, aiming to enhance the accuracy and efficiency of medical imaging analysis through advanced AI technology.

CheXagent is a foundation model focused on chest X-ray interpretation, aiming to enhance the accuracy and efficiency of medical imaging analysis through advanced vision-language processing technology.

CheXagent is a foundation model specifically designed for chest X-ray interpretation, capable of automatically analyzing and interpreting chest X-ray images.

CheXagent is a foundation model specifically designed for chest X-ray interpretation, aiming to assist medical imaging analysis through advanced artificial intelligence technology.

Meditron-7B is an open-source medical large language model with 7 billion parameters, adapted from Llama-2-7B through a medical corpus, and performs excellently in multiple medical reasoning tasks.

Med42 70B is a medical assistant model created by UAE's M42 Health, specializing in text generation tasks in the healthcare domain.

Med42 is a 70-billion-parameter clinical large language model developed by M42, based on the LLaMA-2 architecture, focusing on medical knowledge Q&A and clinical decision support.

This is a fine-tuned version of the Clinical-T5 model for PET report findings summarization, capable of automatically generating personalized clinical impressions based on PET examination findings.

Clinical Camel is an open-source large language model based on the LLaMA-2 70B architecture, fine-tuned with QLoRA, specifically tailored for the medical and clinical research fields.

A sequence-to-sequence model based on BioBart for summarizing radiological findings into impressions, trained on 70,000 radiology reports.

A large language model specifically designed and optimized for clinical scenarios, fine-tuned on a wide variety of medical datasets

MedLLaMA_13B is a large language model for the medical domain, fine-tuned on partial medical corpora based on the LLaMA-13b architecture.

MedAlpaca 13b is a large language model specifically fine-tuned for medical domain tasks, based on the LLaMA architecture with 13 billion parameters, designed to enhance performance in medical Q&A and dialogue tasks.