According to the CEO, there is “no endpoint” in the passage of innovation in artificial intelligence (AI) applications in medical imaging.
Talk to Medical Device Network Marissa Fayer, CEO of Deeplook Medical, said at the Life Sciences Baltics 2025 held in Vilnius, Lithuania on September 16-19, how AI is set to continue to have a major impact on the medical imaging space.
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Feyer outlined that although AI in medical imaging primarily focuses on visualization and decision support at this point, its use is likely to develop and expand areas such as full-scale decision-making, interpretation, and longitudinal prediction.
“I don't think there's an end to AI in the imaging space, especially since we're dealing with real images, so we can interpret it and read it,” Feyer said.
According to an analysis by GlobalData, AI use has progressed rapidly across healthcare, with the market projected to reach a $19 billion valuation by 2027.
While Feyer is bullish about the ongoing fate of AI applications in imaging, she said there are key challenges in how technology can be implemented effectively at scale in real time.
“Everyone loves research, pilots, papers, but how do you implement it in real clinical practice? That's the challenge. But in my view, if someone from any industry does it, it's definitely going to be in the imaging space first,” Feyer said.
There is a clear investor desire for AI applications in medical imaging. Cleerly and Radai, the companies developing AI solutions for the radiology department, have raised $106 million and $60 million respectively over the past year.
More in detail, Feyer explained that the current “reluctance” of some practitioners is the desire to give up on AI decisions, in some cases, a factor that draws wider adoption boundaries, but legacy systems are the dominant obstacles.
Feyer explained: “I don't think that the adoption or lack of AI is fundamentally related to physicians. Rather, it is difficult to implement different technologies, especially in the US and Europe and the UK, as they are hospital systems and legacy systems. It means that technology can build infrastructure more easily, as it is burdened with large legacy systems. [AI] First.
Fayer's remarks reflect those of fellow experts who are already seeking an upgrade to digital infrastructure for the adoption of AI in the healthcare framework.
“I think it's important as an industry to find ways to overcome the hurdles of legacy systems, but we also need to focus on developing countries as AI adoptions are a very widespread takeoff,” concluded Fayer.
