Integration and Implementation Strategies for AI Algorithm Deployment with Smart Routing Rules and Workflow Management.
CoRR(2023)
摘要
This paper reviews the challenges hindering the widespread adoption of
artificial intelligence (AI) solutions in the healthcare industry, focusing on
computer vision applications for medical imaging, and how interoperability and
enterprise-grade scalability can be used to address these challenges. The
complex nature of healthcare workflows, intricacies in managing large and
secure medical imaging data, and the absence of standardized frameworks for AI
development pose significant barriers and require a new paradigm to address
them. The role of interoperability is examined in this paper as a crucial
factor in connecting disparate applications within healthcare workflows.
Standards such as DICOM, Health Level 7 HL7, and Integrating the Healthcare
Enterprise (IHE) are highlighted as foundational for common imaging workflows.
A specific focus is placed on the role of DICOM gateways, with Laurel Bridge
leading transformational efforts in this area. To drive enterprise scalability,
new tools are needed. Project MONAI, established in 2019, is introduced as an
initiative aiming to redefine the development of medical AI applications. The
MONAI Deploy App SDK, a component of Project MONAI, is identified as a key tool
in simplifying the packaging and deployment process, enabling repeatable,
scalable, and standardized deployment patterns for AI applications. The
abstract underscores the potential impact of successful AI adoption in
healthcare, offering physicians both life-saving and time-saving insights and
driving efficiencies in radiology department workflows. The collaborative
efforts between academia and industry, exemplified by collaborations with
organizations like NVIDIA and Laurel Bridge, are emphasized as essential for
advancing the adoption of healthcare AI solutions.
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