Evidence map›Paper›PMID 41293332›Full record

ArticleCureus2025

Artificial Intelligence and Medical Translation: An Editorial on the Ethical Considerations for Emerging Technologies in Dermatology.

Ryan J Scheinkman, Mariana Ramirez-Posada, Sheila Sharifi, Lea Tordjman, Keyvan Nouri

Abstract readEditorial
In one paragraph

Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Ryan J ScheinkmanDr. Phillip Frost Department of Dermatology and Cutaneous Surgery, University of Miami Miller School of Medicine, Miami, USA.
Mariana Ramirez-PosadaDepartment of Dermatology, Stanford University, Palo Alto, USA.
Sheila SharifiDr. Phillip Frost Department of Dermatology and Cutaneous Surgery, University of Miami Miller School of Medicine, Miami, USA.
Lea TordjmanDr. Phillip Frost Department of Dermatology and Cutaneous Surgery, University of Miami Miller School of Medicine, Miami, USA.
Keyvan NouriDr. Phillip Frost Department of Dermatology and Cutaneous Surgery, University of Miami Miller School of Medicine, Miami, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The growing demand for medical translation services in the U.S. highlights the potential of artificial intelligence (AI), large language models (LLMs) like ChatGPT (OpenAI, San Francisco, CA), to bridge language gaps. However, their use in dermatology raises ethical concerns, including information accuracy, patient privacy, dialectical variations, legal accountability, and algorithm bias for a variety of skin colors. AI models may default to informal language, leading to misunderstandings, and their limited ability to handle less common dialects poses communication challenges. The risk of "hallucination," where incorrect information is generated, and inadequate data oversight further complicate their use. In dermatology, precise translation is crucial due to the field's visual nature and the sensitivity of cosmetic concerns. Linguistic diversity can lead to misinterpretations, affecting patient care. Dermatologists must consider these ethical implications to ensure AI tools address the nuances of dermatological terminology and regional language differences, ultimately improving patient outcomes.

Indexed as

aidermatologyethicslarge language modelsmedical translation

Identifiers

PMID41293332
PMCPMC12641312

What OpenQuestion holds

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LicenceCC BY
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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.