Researchers from Japan presented similar findings from a study evaluating the safety of a fully robot-assisted needle placement system from CT-guided biopsy. In that study, physicians used a targeting device in 11 patients; the robot moved the needle to the designated spot and inserted the needle before an IR took over the procedure.


Machine learning


Machine learning, along with “deep learning,” are subsets of AI that utilize algorithms to essentially mimic human reaction and function. Deep learning utilizes neural networks to train algorithms based on existing data. According to many presenters at SIR 2024, radiomics—in which an algorithm pulls data from medical imaging to make suggestions—has the most current and far-reaching applications in IR.


At the University of Wisconsin, researchers developed an AI algorithm that suggests puncture pathways utilizing data from volumetric lung CTs by segmenting the thoracic anatomy and detecting areas of emphysema. The pathways were then evaluated by physicians to determine their safety.


According to Meridith A. Kinsting, MA, who presented the findings, all the ideal pathways found by the algorithm were determined to be safe by the physicians. In effect, the algorithm successfully generated high-quality pathways and accurately judged their safety, based on literature-derived rules.


IRs at Temple University Hospital have begun using AI software that utilizes machine learning to interpret imaging and alert members of a pulmonary embolism response team (PERT) if there are critical findings. According to members of the PERT, this software has enhanced accuracy and speed of diagnosis, while improving communication and risk stratification.


“The integration of [AI software] exemplifies the potential of AI to facilitate and optimize patient care, particularly in emergent clinical settings including PE management,” said Temple University Hospital resident Daniel Kushner, MD.


Learn more about this application on page 30.


Using AI intelligently There are hundreds, if not thousands, of potential applications for AI in IR—but just because the solution exists, doesn’t mean it will work for your practice.


“AI can’t solve every problem, so it’s important to define your actual clinical problem and understand if it’s worth solving,” said Julius Chapiro, MD, PhD. “Don’t create solutions in the absence of problems. Start with simple issues and frequently encountered barriers.”


Dr. Chapiro, who published a paper on the topic with Olivia Gaddum, shared some key questions to ask when implementing AI into your practice. First, think critically about the practical value of the solution. Are you implementing AI to improve accuracy or reduce risks? Is it for streamlined patient education or experience? Identify the value, and ensure you have the metrics to track potential success.


Once the problem and end goal are identified, make sure the algorithm you’re using is tailored to your data type and needs, Dr. Chapiro said. Large language models (LLMs) and deep learning algorithms require large data sets, so you need to ensure that you have enough high-quality data to train


the algorithm. And finally, determine whether the software or technology you’re using is realistic in your practice, or if your solution can be achieved through another software.


The future of AI According to Dr. Daye, the sky is the limit for AI possibilities—but the real challenge is application. While many algorithms are already on the market in terms of patient selection, triage and pre-procedural planning, she said solutions for intraprocedural planning are still in the research phase.


Dr. Tromburg agreed, and said he expects to see a spike in devices and algorithms that will improve information content of imaging—such as limiting the noise in ultrasounds or utilizing imaging to extract vascular features and classify tumors.


In the coming years, Dr. Tromburg expects more devices and algorithms to be submitted for FDA approval. AI is quickly going to be unavoidable, Dr. Daye said, and IRs will have to choose whether to adapt or fall behind.


“It’s clear that AI absolutely has a place in medicine, both in management and clinical practice,” said Dr. Dhanaliwala. “And it’s already here.”


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