von Conta Jill, Engelke Merlin, Bahnsen Fin H, Dada Amin, Liebert Elisabeth, Nensa Felix, Kleesiek Jens, Diehl Anke
Institut für Künstliche Intelligenz (KI) in der Medizin (IKIM), Universitätsmedizin Essen, Essen, Deutschland.
Institut für Diagnostik und Interventionelle Radiologie und Neuroradiologie, Universitätsmedizin Essen, Essen, Deutschland.
Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz. 2025 Jun 25. doi: 10.1007/s00103-025-04086-6.
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.
自20世纪60年代以来，人工智能（AI）在医疗保健领域的历史发展呈现出从简单的基于规则的系统到复杂的数据驱动方法的转变。早期应用主要集中在决策支持方面，而创新系统则使用神经网络和机器学习来识别大型数据集中的模式。人工智能技术在医学中的整合催生了不同的应用领域，可分为预防性人工智能、诊断性人工智能、人工智能辅助治疗和管理性人工智能。预防性人工智能分析风险因素以实现早期干预，而诊断性人工智能有助于更快、更准确地进行诊断。人工智能辅助治疗支持个性化治疗，如个性化用药。管理性人工智能优化诸如预约安排、资源管理和计费等流程。
尽管人工智能系统具有潜力，但它们也面临挑战。这些挑战包括健康数据的碎片化、缺乏标准化、数据保护问题以及算法偏差。建立可互操作的数据基础设施和制定道德准则对于克服这些障碍至关重要。未来趋势包括基础模型（基于广泛数据集且可多种方式使用的大型人工智能模型）的进一步发展、结构化和非结构化数据的整合以及医学中更大程度的个性化。从长远来看，人工智能可以提高医疗保健的质量和效率。然而，这需要研究、行业和政治之间密切合作，以确保安全和可持续的实施。
