Implementierung von künstlicher Intelligenz (KI) im Gesundheitswesen: Historische Entwicklung, aktuelle Technologien und Herausforderungen

Abstract

The historical development of artificial intelligence (AI) in healthcare since the 1960s shows a transformation from simple rule-based systems to complex, data-driven approaches. Early applications focused on decision support, while innovative systems use neural networks and machine learning to recognise patterns in large datasets. The integration of AI technologies in medicine has given rise to diverse areas of application, which can be categorized into preventive AI, diagnostic AI, AI-assisted therapeutic, and administrative AI. Preventive AI analyses risk factors to enable early interventions, while diagnostic AI contributes to faster and more accurate diagnoses. AI-assisted therapy supports individualized treatments, such as personalized medication. Administrative AI optimizes processes such as appointment scheduling, resource management and billing.Despite their potential, AI systems face challenges. These include the fragmentation of health data, a lack of standardisation, data protection concerns and algorithmic biases. The establishment of interoperable data infrastructures and the development of ethical guidelines are crucial to overcoming these hurdles. Future trends include the further development of foundation models (large AI models that are based on broad datasets and can be used in a variety of ways), the integration of structured and unstructured data and greater personalisation in medicine. In the long term, AI can improve the quality and efficiency of healthcare. However, this requires close co-operation between research, industry and politics in order to ensure safe and sustainable implementation.

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