At the recent DACH Healthcare Innovation Summit in Berlin, a panel of leading healthcare executives and practitioners tackled a pressing reality: the biggest barrier to AI adoption in medicine isn’t the technology—it’s us. While more than 1,000 FDA-cleared AI algorithms exist today, only a fraction are in clinical use.
Moderated by Dr. Bertram Weiss, VP Health at Merantix Momentum, the discussion featured key voices from across the industry who painted a clear picture of AI’s role in modern healthcare—not as a distant promise, but as a present force shaping the future.
“The openness to engage with digital solutions is much higher in Spain than in Germany, both from healthcare providers and patients,” observed Prof. Dr. Ralf Kuhlen, Chief Medical Officer at Fresenius. “Our biggest challenges aren’t in regulations, but in habits, traditions and mindset.”
This isn’t just another technology implementation challenge. We’re watching a collision between two worlds: the methodical, traditionally conservative medical field and the breakneck pace of AI advancement. While medical knowledge once doubled every five to seven years, making a six-year medical education sensible, we’re now seeing transformative AI developments in mere months.
The transformation is happening, ready or not. Alexander Boehmcker, VP Europe at Aidoc, shared a striking example: “We’ve seen our first foundation model reduce AI development time from one year to two weeks.” This isn’t incremental change – it’s a paradigm shift.
Prof. Dr. Beatrice Beck Schimmer, Vice President Medicine at the University of Zurich, highlighted a successful case study where AI analysis of multi-platform tumor profiling has achieved remarkable results: “About 40% of patients with no remaining therapy options responded to AI-suggested treatments.” This isn’t theoretical potential – it’s real-world impact.
The panel repeatedly emphasized that successful AI implementation isn’t just about having accurate algorithms, but also about integration. Dr. Maja Ullrich, Chief Data Officer at University Hospital Essen, described how they’re revolutionising patient experience through AI-driven voice control systems: “Patients can now manage their room environment and access their appointment schedules through voice commands, making AI tangible and beneficial in their daily hospital experience.”
Here’s the truth: While we debate AI implementation, patient needs grow more complex, and healthcare systems strain under increasing pressure. The question isn’t whether to adopt AI, but how quickly we can overcome our cultural barriers to do so effectively.
Consider this: From Boehmcker’s experience of helping clients implement AI solutions, he discovered that hospitals often struggle not with the technology itself, but with scarce hospital IT capacity and competing project priorities. The solution? Cloud versions, which have seen increasing acceptance even in traditionally conservative markets like Germany.
As Prof. Kuhlen aptly pointed out, “Medicine will always remain human.” The goal isn’t to replace human judgment but to augment it. Think of it like modern aviation: while autopilot handles 98% of flight operations, pilots remain essential for critical decision-making and overall system oversight.
Let’s talk numbers. Every CEO knows that healthcare costs are spiraling while margins shrink. AI isn’t just a nice-to-have technological upgrade – it’s becoming an economic necessity. The panel highlighted how AI is already delivering tangible benefits:
The ROI isn’t theoretical. As demonstrated at institutions using Aidoc’s platform like University Hospital Essen and Unfallkrankenhaus Berlin, AI integration is showing measurable improvements in workflow efficiency and patient outcomes. For example, the platform’s ability to help prioritise critical cases has demonstrably reduced time-to-treatment in acute conditions like pulmonary embolism and intracranial hemorrhage.
While data privacy often dominates discussions about AI implementation, the panel revealed a surprising truth. “We have about 75% of patients consenting to data donation for scientific purposes,” shared Prof. Kuhlen. In the randomised controlled trial MASAI study, assessing AI effectiveness in mammography reporting, only 0.16% of 100,000 women decided not to participate in the study. “The challenge isn’t patient willingness – it’s institutional silos and system fragmentation.”
This insight challenges the traditional narrative about data barriers. The real opportunity lies in breaking down these institutional walls while maintaining appropriate security and compliance frameworks.
Looking ahead to 2025-2026, we’re entering the era of foundation models in healthcare. These models promise to transform how we develop and deploy AI solutions, potentially reducing development cycles from years to weeks. This isn’t just about speed – it’s about democratising access to advanced AI capabilities across healthcare systems of all sizes.
The healthcare organisations that will thrive in the next decade aren’t necessarily those with the most advanced AI systems, but those that successfully bridge the cultural gap between traditional medical practice and technological innovation. The technology is ready, the economics make sense and patients are willing participants. The question isn’t whether to embrace this transformation, but how quickly we can overcome our organisational inertia to do so.
As Prof. Beck Schimmer aptly concluded, “AI will break through – not as a vision, but as reality. In a few years, AI will be a companion to healthcare workers, enhancing efficiency and allowing more time for patient care.” The future of healthcare is being written now, and AI is holding the pen. The only question is: which organisations will be the authors of this transformation, and which will be left trying to catch up?
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