Evidence map›Paper›PMID 40172646›Full record

ReviewDie Ophthalmologie2025

[Artificial intelligence in medicine-Opportunities and risks from an ethical perspective].

Saskia Metan, Florian Bruns

Abstract readEnglish AbstractReview
PubMed Publisher
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Saskia MetanLehrstuhl für Ethik und Geschichte der Medizin und Zahnmedizin, Medizinische Fakultät Carl Gustav Carus, Technische Universität Dresden, Fetscherstr. 74, 01307, Dresden, Deutschland.
Florian BrunsLehrstuhl für Ethik und Geschichte der Medizin und Zahnmedizin, Medizinische Fakultät Carl Gustav Carus, Technische Universität Dresden, Fetscherstr. 74, 01307, Dresden, Deutschland. florian.bruns@tu-dresden.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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

Artificial IntelligenceDiagnostic ImagingEthics, MedicalOphthalmologyAlgorithmsComputer SecurityHumansAlgorithmsClinical decision support systemsMedical ethicsOphthalmologyPrinciple-based ethics

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.