ReviewBundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz2025
[Applications, challenges and a trustworthy use of artificial intelligence in public health].
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
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rapid advancements in artificial intelligence (AI) over recent years have resulted in its integration into people's everyday lives. The wide availability of diverse data within the public health sector opens up a number of fields of application for AI, ranging from infection research and the analysis of epidemiological data to the extraction of information from communication data such as social media, the development of new resilience strategies against climate change and the systematic evaluation of specialist literature.The quality of the underlying data is paramount to the successful implementation of AI applications. In public health research, on the one hand, there is a wide variability of data types including, but not limited to, image data, numerical data and survey data. On the other hand, availability can be limited, for example when a rare pathology is being investigated and/or stringent data protection requirements apply. Concurrently, it is imperative to maintain high ethical standards and to mitigate biases, imbalances and lack of transparency as early as possible.We delineate an approach towards the responsible and trustworthy utilisation of AI applications in public health, which leads from the initial question to the data and the model development to evaluation and emphasises the importance of careful and complete documentation.
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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.