A survey by LSN and ARIC shows how this transition into everyday clinical practice can be successful—and which framework conditions are now crucial.
What is the current state of artificial intelligence (AI) in healthcare and the life sciences? A joint survey by Life Science Nord (LSN) and the Artificial Intelligence Center Hamburg (ARIC) shows that the industry is evolving—but the transition from pilot projects to routine operations remains a key challenge.
The analysis is based on a survey of sentiment among stakeholders in northern Germany from the medical technology, pharmaceutical, research, and healthcare sectors. The results make it clear that AI has long since become part of everyday work—though it is still predominantly in the early stages of implementation.
AI is being used—but rarely in routine operations
The majority of the organizations surveyed are already using AI, though primarily in pilot projects or partial implementations. “Only a small percentage has fully integrated AI into their processes,” explains Sabrina Pohlmann of ARIC. AI is used particularly frequently in data-intensive areas such as research, quality management, or administration. Applications directly involving patients—such as in diagnostics or therapy—remain the exception so far.
“We’re seeing rapid growth in the use of AI—but we’re also seeing that many projects are still in the experimental stage. The transition to widespread adoption is significantly more complex,” explains Caro Memah of ARIC.
Efficiency as a Driver—Supply Potential Grows
Expectations for AI are high: The focus is primarily on increasing efficiency, optimizing processes, and reducing the workload on specialized staff. A large proportion of respondents are already reporting positive effects, particularly in the form of time savings and improved work organization. At the same time, it is evident that while the potential for direct improvements in patient care is recognized, it has so far been tapped only gradually.
“AI is currently viewed primarily as an economic lever. The true added value for healthcare will only become apparent once applications are securely and scalably integrated into everyday life,” said Sassan Sangsari of ARIC.
The main obstacles lie in structure and regulation
The biggest challenges in AI implementation are not so much technological in nature. Rather, respondents cite structural barriers as the key factors: poor data quality, insufficient access to data, and legal uncertainties. In particular, data protection, liability issues, and unclear regulatory requirements are hindering implementation.
The results make it clear: The current discourse on AI is still heavily influenced by pilot projects. What is missing are reliable framework conditions for the transition to routine operation.
Responsible AI as a Prerequisite
A key finding of the survey is the great importance of responsible AI. Issues such as transparency, fairness, and human accountability for decisions are considered essential by the majority of stakeholders. This makes it clear that trust is a key prerequisite for the widespread adoption of AI in healthcare.
Regulatory Sandboxes as a Possible Solution
A promising approach to overcoming existing hurdles is the use of so-called regulatory sandboxes—protected testing environments for AI applications under real-world conditions. Although these tools are gaining political significance, they remain relatively unknown in practice. At the same time, there is a clear need for such formats to better align innovation with regulatory requirements.
“If we want to successfully integrate AI into healthcare, we need spaces where innovation can be tested under real-world conditions—in a legally compliant and practical manner,” emphasizes Annika Wallbott of Life Science Nord.
Conclusion: It all comes down to the transition
The results clearly show that the question is no longer whether AI will be used in healthcare, but rather how quickly and under what conditions it can be integrated into routine operations.
The next step requires not so much new pilot projects as clear regulatory guidelines, better access to data, and the targeted development of skills.
Only if we succeed in creating these conditions can AI realize its full potential—for more efficient health care, innovative research, and the sustainable strengthening of Northern Germany as a business hub.
AI IN HEALTH – Sentiment Survey of Stakeholders in Northern Germany | Overview of Results
