The introduction of AI in medical imaging could not have come at a better time with the COVID-19 pandemic, as AI applications for detection, diagnosis and acquisition support. especially when using Telemedicine. have shown to be invaluable managing these patients both at healthcare institutions as well as at home. There are a couple of caveats however, using this new technology, first the regulatory constraints limiting new AI algorithms because the FDA needs to catch up with approvals, second, as with any Deep Learning algorithm, AI for healthcare needs lots of data to train the algorithm, which is a limiting factor for COVID cases even although several hospitals are making their COVID patient data files publicly available. But, despite these limitations, institutions are ready to deploy AI for this particular use case together with other applications that have been identified and are addressed by literally hundreds of companies developing these novel applications.
However, early implementations of AI have come across a major obstacle: how to adopt it to the workflow as it has caused a true “traffic jam” of data to be routed to several algorithms, and the results from these AI applications, in the form of annotations, reports, markers, screen saves and other indications, to be routed to their destinations such as the EMR, PACS, reporting systems or viewers. This orchestration has to occur synchronized with other information flows for example, an AI result has to be available either before or at the time of the reporting of the imaging studies, and has to be available together with lab or other results, which might need delaying or queuing these other non-AI information flows to be effective.
What is needed to manage this is an AI “conductor” that orchestrates the flow of images, results, reports between all the different parties such as modalities, reporting systems, EMR, and obviously the AI applications, the latter of which could be on-premise or in the cloud. Note that the number of AI apps eventually reach hundreds if you take into account that an algorithm might be modality specific (CT, MR, US etc.), and be specialized for different body parts and/or diseases. Scalability is a key requirement of this critical device but also many other features.
A simple “DICOM router” will not be able to orchestrate this rather complex workflow. To assist users with identifying the required features, I created three levels of routers as shown in the figure.
Level 1 can do simple forwarding and multiplexing, queue management and has a simple rules engine to determine what to send where.
The second level has additional features as it can perform “fuzzy routing” i.e. based on fuzzy logic, prefetch information using proxies (i.e. querying multiple sources while giving a single return), do conversions of data and file formats, anonymize the data and is scalable.
The third level has all of the level 1 and 2 functionality and extends it to AI specific routing, can modify images header and split studies, perform worklist proxies (i.e. query multiple worklists while appearing as a single thread), and has secure connectivity to meet “zero-trust” requirements. It supports not only “traditional” DICOM, HL7 but also webservices such as WADO and FHIR and supports IHE. It can also perform static and dynamic routing, do data conversions, filter the data, split studies, normalize the data, anonymize it if so desired, and provide support for several different formats and support for Structured reports, annotations, to name a few. As a matter of fact, a fully featured AI conductor requires at least 25 distinctly different functions as described in detail in this white paper (link).
In conclusion, there is a serious workflow issue deploying AI, but the good news is that there are solutions available, some in the public domain with limited features and some as commercial products. Make sure you know what you need before shopping around, the link to the comprehensive white paper on this subject has a handy checklist you can use when you are shopping at your (virtual) HIMSS, SIIM or RSNA trade shows or when “Zooming” with your favorite vendor. You can download the white paper here.