“When we talk about AI, everybody thinks of the mainstream discussion that centers around topics like generative AI and large language models, and how AI can hallucinate and do all of these terrible things,” said Shen. “A lot of our time in Washington is spent helping our lawmakers understand the differences between the different types of AI.”

For Shen's employer, Siemens Healthineers, as well as other medical equipment companies, the focus is on a subset of AI known as Algorithm-Based Healthcare Services (ABHSs). These are clinical algorithms that provide some sort of qualitative or quantitative information that helps the clinician make a more informed diagnostic decision.


According to Shen, ABHS algorithms are very different from unregulated, less formal uses of generative AI that may answer clinical questions or streamline operational tasks. For example, an ABHS algorithm may help with things such as patient positioning and also trace segmentations of a cancerous tumor so a physician knows where to target the radiation during radiation therapy.

“I think there's a strong belief that if we can get Medicare and CMS to start to provide reimbursement for this technology, then it will drive private payers and others to also recognize the impact that AI has and hopefully drive further adoption in that respect as well,” he added.

Demonstrating benefits in the data
In June, the Medicare Payment Advisory Committee (MedPAC) released a report outlining its successes and challenges when it comes to AI reimbursement. Stakeholders want to move away from the traditional fee-for-service payment model to a value-based one, but software reimbursement strategies remain undeveloped.

One of the main hurdles standing in the way is a lack of data on the efficacy and economic impact of these technologies. There needs to be data that prove that it can improve patient outcomes.

“The real problem is that we have not accrued and collected enough data so that there's actually evidence,” said Dr. Liron Pantanowitz, professor and chair of the department of pathology at UPMC and the University of Pittsburgh. “Not only evidence for us in clinical medicine to come up with evidence-based guidelines on how best to use this technology and monitor it, but also data to give to CMS.”

A 'catch-22' for efficiency
However, he does caution that there may be a bit of a paradox. As a pathologist, if he purchases a commercial AI tool to help him screen biopsies and audit tests quicker, then asks CMS for reimbursement, there may be an unintended consequence.

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