ReviewBundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz2025
[Implementation of artificial intelligence (AI) in healthcare: historical development, current technologies and challenges].
Review in Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- A randomized controlled trial on the application of a chronic disease management platform based on digital health technology combined with an innovative model of intelligent management for hypertension in patients with hypertension.Frontiers in digital health · 2025Trial
- Patients' perception towards large language models in otorhinolaryngology, head and neck surgery: a single-centre survey.Frontiers in digital health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.