ReviewDie Ophthalmologie2025
[Artificial intelligence in medicine-Opportunities and risks from an ethical perspective].
Review in Die Ophthalmologie, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- [Digitalization and artificial intelligence in ophthalmology-Is the matrix not far off?]Die Ophthalmologie · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Imaging disciplines, such as ophthalmology, offer a wide range of opportunities for the beneficial use of artificial intelligence (AI). The analysis of images and data by trained algorithms has the potential to facilitate making the diagnosis and patient care and not just in ophthalmology. If AI brings about advances in clinical practice that benefit patients, this is ethically to be welcomed; however, respect for the self-determination of patients and data security must be guaranteed. Traceability and explainability of the algorithms would strengthen trust in automated decision-making and enable ultimate medical responsibility. It should be noted that algorithms are only as good and unbiased as the data used to train them. If the use of AI is likely to lead to a loss of skills on the part of doctors (deskilling), this must be counteracted, for example through improved training. Accompanying medical ethics research is necessary to identify those aspects of the use of AI that require regulation. In principle, care must be taken to ensure that AI serves people and adapts to their needs, not the other way round.
Indexed as
Identifiers
40172646What 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.