ReviewBundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz2025
[Artificial intelligence in hospital logistics and operational processes].
Review in Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The implementation of artificial intelligence (AI) solutions in hospital logistics is becoming increasingly important for optimizing clinical processes. This paper examines the use of AI in hospital logistics and other operational areas of healthcare. Following an introduction to the fundamental role of AI in hospitals, the technological foundations and specific logistics processes are presented. A developed matrix systematically classifies the various AI solutions according to automation levels and functional areas.The potential of AI systems is illustrated through concrete application examples. AI-supported transport disposition for optimizing transport logistics, automatic material requisition for modular cabinets in inventory management, and automated nursing documentation using sensors and machine learning demonstrate the potential for increasing efficiency in operational processes.The analysis identifies key challenges in implementing AI systems in hospitals, including technical integration, data availability, system flexibility, and transparency requirements. Despite these limitations, it is becoming clear that AI-based solutions are gaining increasing importance, particularly for logistics and hospital operations. The results show that the successful deployment of AI systems depends significantly on the balance between technological innovation and practical applicability in everyday hospital routines. The paper thus contributes to understanding the potential and limitations of AI applications in hospital logistics and provides a systematic framework for their strategic implementation.
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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.