Evidence map›Paper›PMID 40114646›Full record

ReviewCurrent opinion in urology2025

Ethical aspects of artificial intelligence: what urologists need to know.

Rounak Verma, Findlay Macaskill, Anna Kim, Nicholas Raison, Prokar Dasgupta

Abstract readReview
In one paragraph

Review in Current opinion in urology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Review
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  6. Review
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

5 authors.

Rounak VermaResponsible AI UK.
Findlay MacaskillResponsible AI UK.
Anna KimResponsible AI UK.
Nicholas RaisonResponsible AI UK.
Prokar DasguptaResponsible AI UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purpose of reviewThe integration of artificial intelligence in urology presents both transformative opportunities and ethical dilemmas. As artificial intelligence driven tools become more prevalent in diagnostics, robotic-assisted surgeries, and patient monitoring, it is crucial for urologists to understand the ethical implications of these technologies. This review examines key ethical concerns surrounding artificial intelligence in urology, including bias, transparency, accountability, and data privacy. RECENT

findingsRecent literature highlights algorithmic bias as a significant challenge, where artificial intelligence models trained on nondiverse datasets may produce inequitable outcomes. The "black-box" nature of artificial intelligence systems complicates transparency and interpretability, raising concerns about clinician and patient trust. Emerging reporting standards, such as STREAM-URO and IDEAL frameworks, and WHO Guidelines provide structured approaches for ethical artificial intelligence integration in urology. SUMMARY: The ethical deployment of artificial intelligence in urology requires a balanced approach that prioritizes fairness, accountability, and patient autonomy. Clinicians must advocate for explainable artificial intelligence, ensure equitable access, and integrate human oversight into artificial intelligence assisted decision-making. Future research should focus on improving dataset diversity, enhancing artificial intelligence interpretability, and establishing robust ethical guidelines to ensure that artificial intelligence advances align with medical ethics and patient-centered care.

Indexed as

Artificial IntelligenceUrologistsUrologyConfidentialityHumans

Identifiers

PMID40114646
PMCPMC11970583

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.