Medicine is currently experiencing an unprecedented technological revolution. Artificial intelligence (AI) already transforms basic processes in health care and promises a future with precise diagnoses, more individual treatments and more efficient patient care.
The health sector faces growing demand due to an aging population, rising costs and a lack of specialists – AI could make a decisive contribution to solving these challenges.
The digital transformation of the healthcare system
The digitization of medicine creates the basis for AI applications. Electronic patient files, wearables and medical imaging generate millions of data daily that provide valuable insights into health states. The development of personalized medicine is particularly interesting for insured persons who have one private health insurance have opened new treatment options, since faster access to innovative AI-based therapies is often possible here.
Surprisingly, 44 percent of the health specialists surveyed had never participated in the development or implementation of a AI solution in their organization. This shows that despite the enormous potential, the practical implementation is still in its infancy.
Vacuums in diagnostics
Medical imaging is a prime example of successful AI integration. Google Deepminds Alphafold revolutionized the protein structure forecast in 2020 and developers Demis Hassabis and John Jumper were awarded the Nobel Prize in Chemistry in 2024. In radiology, AI systems already show impressive results: A groundbreaking study by Google Deepmind in Nature demonstrated that a AI system in the mammography evaluation achieved an absolute reduction of 5.7% for false-positive and 9.4% in false negatives in the USA.
In an independent study with six radiologists, the AI system exceeded all human readers by an absolute difference of 11.5% in the AUC Roc rating.
Personalized treatment approaches by AI
The true revolution of AI in medicine lies in the personalization of therapies. AI can lead to better treatment results and improve the productivity of care. It enables health practitioners to spend more time with patient care and thereby increase employee satisfaction.
Pharmaceutical companies use machine learning more to develop tailor -made treatment plans. The application in pharmacogenetics is particularly promising: algorithms can predict how patients will react to certain medication. The Alphafold database now provides over 200 million protein structure forecasts free of charge and enables researchers worldwide to develop new medication.
Challenges and ethical aspects
Despite all the successes, the AI revolution brings significant challenges. Quality problems in the selection of applications, AI design and the robustness of Algorithms were repeatedly identified in studies. A central problem is the explanability of AI decisions: Doctors must be able to understand why a system recommends a certain diagnosis.
On August 1, 2024, the European AI Act came into force, which subjects high-risk ACI systems to strict requirements, including risk reduction systems and human supervision. Data security and protection of privacy remain further critical challenges when implementing AI systems in healthcare.
Outlook into the future
The future of AI in medicine promises further groundbreaking developments. In August 2025 Oracle announced a new generation of AI-based electronic patient files that helps doctors concentrate on patients and reduce administrative stress. Experts expect three phases of AI scaling: First of all, routine, administrative tasks are automated.
In the second phase, solutions for home -based care follow. In the third phase, researchers expect more evidence-based AI solutions in clinical practice. It will be crucial that these progress is developed with an appropriate regulatory framework to ensure the trust of the patients.
03.09.2025
