![]() Creating markups to highlight areas of concern with arrows or heatmaps.Segmenting organs, lesions, and other structures.Classifying medical imaging studies for the presence of a disease or condition.With MONAI, developers, researchers, and data scientists are building applications for a wide range of medical AI applications, including: ![]() It allows developers to build AI applications, orchestrate clinical AI workflows, and interoperate with medical imaging systems like PACS (picture archiving and communication systems) over standards like DICOM, FHIR, and HL7. MONAI Deploy provides a set of open source tools for developing, packaging, testing, deploying, and running medical AI applications. MONAI, the Medical Open Network for AI, is bridging this gap from development to clinical deployment with MONAI Deploy. These applications can help clinicians streamline imaging workflows, uncover hidden insights, improve productivity, and connect multi-modal patient information for deeper patient understanding. ![]() In most data science teams, model developers lack a fast, consistent, easy-to-use, and scalable way to develop and package trained AI models into market-ready medical AI applications. Many of these models are nothing short of groundbreaking, yet 87% of data science projects never make it into production. ![]() With a wide breadth of open source, accelerated AI frameworks at their fingertips, medical AI developers and data scientists are introducing new algorithms for clinical applications at an extraordinary rate. ![]()
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