Technological advances in artificial intelligence (AI) have enabled the development of large vision language models (LVLMs) that are trained on millions of paired image and text samples. Subsequent research efforts have demonstrated great potential of LVLMs to achieve high performance in medical imaging use cases (e.g., radiology report generation), but there remain barriers that hinder the ability to deploy these solutions broadly. These include the cost of extensive computational requirements for developing large scale models, expertise in the development of sophisticated AI models, and the difficulty in accessing substantially large, high-quality datasets that adequately represent the population in which the LVLM solution is to be deployed. The HOPPR Medical-Grade Platform addresses these barriers by providing powerful computational infrastructure, a suite of foundation models on top of which developers can fine-tune for their specific use cases, and a robust quality management system that sets a standard for evaluating fine-tuned models for deployment in clinical settings. The HOPPR Platform has access to millions of imaging studies and text reports sourced from hundreds of imaging centers from diverse populations to pretrain foundation models and enable use case-specific cohorts for fine-tuning. All data are deidentified and securely stored for HIPAA compliance. Additionally, developers can securely host models on the HOPPR platform and access them via an API to make inferences using these models within established clinical workflows. With the Medical-Grade Platform, HOPPR's mission is to expedite the deployment of LVLM solutions for medical imaging and ultimately optimize radiologist's workflows and meet the growing demands of the field.

翻译：人工智能（AI）的技术进步促进了大规模视觉语言模型（LVLMs）的发展，这些模型基于数百万对图像-文本样本进行训练。后续研究已证明LVLMs在医学影像应用场景（如放射报告生成）中实现高性能的巨大潜力，但仍存在阻碍这些解决方案广泛部署的壁垒。这些壁垒包括开发大规模模型所需的大量计算资源成本、开发复杂AI模型的专门知识，以及难以获取充分代表LVLM解决方案目标人群的、足够大规模的高质量数据集。HOPPR医疗级平台通过提供强大的计算基础设施、一套可供开发者针对特定用例进行微调的基础模型，以及一个为评估临床环境部署微调模型设定标准的稳健质量管理系统，来应对这些壁垒。HOPPR平台可访问来自数百个不同人群影像中心的数百万影像研究和文本报告，用于预训练基础模型，并为微调提供特定用例队列支持。所有数据均经去标识化处理并安全存储，符合HIPAA规范。此外，开发者可在HOPPR平台上安全托管模型，并通过API访问这些模型，在既定临床工作流程中进行推理。借助该医疗级平台，HOPPR的使命是加速医学影像LVLM解决方案的部署，最终优化放射科医师工作流程，满足该领域日益增长的需求。