Integration and Implementation Strategies for AI Algorithm Deployment
with Smart Routing Rules and Workflow Management
- URL: http://arxiv.org/abs/2311.10840v2
- Date: Tue, 21 Nov 2023 19:41:07 GMT
- Title: Integration and Implementation Strategies for AI Algorithm Deployment
with Smart Routing Rules and Workflow Management
- Authors: Barbaros Selnur Erdal, Vikash Gupta, Mutlu Demirer, Kim H. Fair,
Richard D. White, Jeff Blair, Barbara Deichert, Laurie Lafleur, Ming Melvin
Qin, David Bericat, Brad Genereaux
- Abstract summary: This paper reviews the challenges hindering the widespread adoption of artificial intelligence (AI) solutions in the healthcare industry.
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.
- Score: 0.37918614538294315
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: 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 Smart Routing Rules and Workflow
Management 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, are emphasized as essential for
advancing the adoption of healthcare AI solutions.
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