From the November 2018 issue of HealthCare Business News magazine
By Dr. Ulrik Kristensen
It is already clear that AI will play a role in almost every aspect of medical imaging.
Operational analytics, data acquisition and dose monitoring, scheduling and workload optimization, automatic tissue segmentation and measurement tools, automatic image analysis and diagnostic reporting are all active targets for new AI tools. Machine learning image analysis algorithms are being developed for a diverse range of clinical applications, such as neurological disorder detection, lung nodule detection and breast cancer diagnosis. Until recently, these algorithms were developed mainly at university hospitals for internal research use only, as well as a handful of specialist image analysis companies. But with the AI startup community flourishing and the number of commercially available algorithms increasing, how these tools will be integrated in the radiologist workflow remains unclear. In this article, we review and discuss the different integration strategies for AI and propose how the algorithm deployment maturation will most likely materialize.
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AI adoption and integration will depend heavily on the broader imaging IT structure and healthcare IT architecture. Today, radiology IT is still largely departmental. Hospital consolidation in the U.S. and some Western European countries is starting to drive enterprise imaging, and is creating shared resources between radiology departments within the same health system, or access to imaging data for departments outside radiology. On-premise data storage is, however, still the most commonly used architecture, though some hospitals in the U.S. and Western Europe are starting to adopt public cloud solutions as security concerns decrease and providers get more comfortable with public cloud storage, after some years of hybrid cloud for backup and disaster recovery.
The highly departmental structure of radiology and slow adoption of cloud has also had consequences for the development of AI, and initial development of radiology AI was limited to academic research projects within single hospitals. More recently, as the AI industry has proliferated outside of these early developers, it has remained limited by the confines of existing healthcare IT frameworks. This has led to a mixture of integration strategies today:
Although enterprise imaging is in demand, particularly in the U.S., PACS is still the most widely used viewing platform in medical imaging. Many AI vendors have attempted to make their algorithms easily accessible and integrated into the radiologist workflow by partnering with PACS vendors for integration into their software and clinical applications. The AI software would typically launch through a button in the PACS user interface; although integration could be even tighter, this is still a step forward compared to individually installed algorithms requiring separate login. A strong motivation from the AI vendors’ perspective is to tap into the PACS vendors’ distribution network while keeping the costs of doing business at a minimum.