At the RSNA (Chicago, Illinois) in 2022, several PACS vendors announced new, or promoted existing, cloud-hosted PACS solutions. One term that was so heavily used that it likely lost some differentiation among buyers is “cloud-native”. Almost every vendor uses the term, with little explanation as to the benefit of this (other than the implication that it is better somehow) or how their solution specifically provides this. Vendors should back up these claims with some clear examples of how their solution is cloud-native and how that affects the customer.
Most cloud-hosted PACS solutions are offered as a private cloud, meaning that each cloud-based instance is built and hosted for each specific customer. Some solutions are multi-tenant, with the software developed to run a single logical instance, but use access control and data management features to segregate each customer's access to their data.
There are several opportunities within the medical imaging domain that could be realized in a multi-tenant solution deployment, including secure and managed research data and teaching file sharing across enterprises, among others.
Artificial intelligence (AI) AI, in its many flavors, continues to capture the attention and imagination of both vendors and buyers. It remains in peak hype mode, as evidenced by the number of AI-focused vendors and the booth signage at RSNA 2022.
Despite of a number of published governance and scoring models in journals, many healthcare organizations are still in the process of defining and applying formal or ad hoc policies and models for assessing and prioritizing where to invest in AI-based applications. Governance and surveillance tools and methods are absent for many enterprises, while Radiologists in different subspecialties continue to advocate for the purchase of an AI-based application for use in their section. Business-case models to justify investment in new AI-based solutions are often immature or incomplete.
At RSNA 2022, some solutions to help develop and/or validate AI-based applications have started to appear. Some enterprises will look to these types of tools to test and monitor the performance of AI-based solutions before and during clinical use.
The types of suppliers of AI-based applications are also varied. From startups, aggregators, incumbent imaging IT vendors like PACS and reporting solution providers, even to imaging center chain and reading group organizations — all are offering AI-based applications (or pay-per-use services). Buyers seeking to minimize the number of partnerships they need to manage are overwhelmed by the amount of choice available.
