Digitalization and Artificial Intelligence in Medicine: Seizing Opportunities and Taking Responsibility

The healthcare system is at a turning point. While generative artificial intelligence (AI) has long been a part of many people's everyday lives, its structured and professional use in medical care in Germany has fallen short of its potential. The discrepancy between everyday practice and system reality is becoming an increasingly problematic issue for patients, service providers, and Germany as a business location.

At MLL and MLL MVZ, we have relied on digital and automated diagnostic procedures for years. Our goal is to provide patients with more precise, faster, and personalized care. While we recognize the potential, we also acknowledge the obstacles. We believe that clear decisions and a new mindset are necessary.

Several areas are essential for the responsible and effective advancement of digital medicine in Germany:

Germany is falling behind in implementing fundamental digital healthcare solutions and AI-supported systems. This is not primarily due to a lack of technology, but rather to overly regulated structures, slow processes, and a general mistrust of anything new. For instance, the tension between the EU AI Act and the Medical Device Regulation is currently causing significant uncertainty among businesses and research institutions. Duplicate approval requirements, contradictory assessment criteria, and a lack of prioritization are threatening innovations before they are even tested.

Therefore, we appeal to the regulatory authorities to make a trial-and-error approach possible and support it. This approach does not require additional commissions, but rather clear decisions and pragmatic pilot projects, especially in the highly relevant area of medical AI applications, such as cancer research.

Two recent studies published in the journal Nature demonstrate the impressive capabilities of AI systems. The AI system AMIE (Articulate Medical Intelligence Explorer) engaged in more structured and empathetic dialogues during simulated patient conversations and made more accurate diagnoses than experienced doctors.

The study results prove that modern AI systems can work at high analytical and communicative levels. In complex specialty areas such as hematology, where large amounts of data and rare clinical cases converge, AI-supported systems can provide targeted support for medical decision-making.

Without responsible access to networked, pseudonymized healthcare data, many innovations in Germany will continue to be overlooked. While other countries have long established patient-centered, data-supported care systems, Germany's progress is being slowed by data protection fears, federal structures, and a lack of interoperability.

Data protection is not incompatible with medical research; rather, it must be viewed as an enabler. A clear, legally secure, and ethically sound framework for the use of medical data is needed — in the interests of patients and scientific excellence.

Whether it's electronic patient records, e-prescriptions, or secure data exchange, the digital infrastructure of the German healthcare system remains largely incomplete. Although Gematik has ambitious roadmaps, conflicts of responsibility, system fragmentation, and a cumbersome administrative apparatus are slowing down real progress. There also is a lack of positive communication with the public. The result is isolated solutions, high costs, and minimal benefits.

Meanwhile, international tech companies are advancing the development of universal AI platforms suitable for everyday use — systems that surpass chatbots and increasingly provide health guidance. However, if these applications become more accessible to patients than the next update to the telematics infrastructure, there is a risk that the system will lose touch with reality.

Integrating AI and digitalization into everyday medical practice is a technical and cultural task. Many people still perceive digital systems as a threat rather than a help. However, it's clear that only those who understand AI can use it responsibly. AI is neither a cure-all nor an adversary, but rather a tool that must be sensibly integrated into everyday healthcare.

The recommended approach is to integrate AI skills into education and training, develop a practical understanding, and engage in open discussions about ethics and responsibility. Although people remain at the center of medicine, digital systems can help make care more humane, accessible, and effective.

At MLL and MLL MVZ, we are strongly committed to developing research, diagnostics, and care together — for the benefit of our patients and Germany's sustainable healthcare sector.