Evidence map›Paper›PMID 42059089›Full record

ReviewJournal of Korean Neurosurgical Society2026

Generative AI-Assisted Medical Illustrations in Pediatric Neurosurgery : Workflows, Ethical Safeguards, and Journal Policy Considerations.

Jun Kyu Hwang, Dong-Su Jang

Abstract readReview
In one paragraph

Review in Journal of Korean Neurosurgical Society, 2026. 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

2 authors.

Jun Kyu HwangDepartment of Neurosurgery, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Korea.
Dong-Su JangResearch Affairs, Yonsei University College of Medicine, Seoul, Korea. artanato@yuhs.ac.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Generative artificial intelligence (AI) has emerged as a transformative tool for creating high-quality visual materials in medical research and education. In pediatric neurosurgery, where ethical and legal constraints limit the use of real patient photographs, AI-assisted illustrations offer significant potential. However, concerns regarding clinical accuracy, intellectual property, and the protection of vulnerable pediatric patients necessitate rigorous oversight. We present a human-in-the-loop workflow that integrates generative AI with vector-based digital editing to produce scientifically accurate and ethically grounded medical illustrations. We reviewed current AI usage policies from major medical journals, including the International Committee of Medical Journal Editors and the Journal of Korean Neurosurgical Society (JKNS). To demonstrate practical application, we developed illustrative examples for conditions such as sacral dimple, Crouzon syndrome, and Down syndrome using clinician-led sketches and AI-assisted refinement. Vector-based workflows facilitate the transformation of AI-generated raster drafts into editable, high-resolution graphics, allowing clinicians to correct "hallucinations" and ensure anatomical precision. While most journals prohibit listing AI as an author, they permit its use for conceptual figures provided there is transparent disclosure of the tools and prompts used. Our proposed workflow emphasizes that AI should function as a "constrained assistant" rather than an autonomous creator, ensuring that the final output remains non-identifiable and respectful of pediatric patients' dignity. Generative AI tools can significantly enhance visualization in pediatric neurosurgery when governed by strict ethical and technical safeguards. Adherence to journal policies and the maintenance of human-directed validation are essential to uphold scientific integrity and patient privacy in the era of AI-assisted publishing.

Indexed as

Artificial intelligenceEthics, publishingMedical illustrationNeurosurgery, pediatric

Identifiers

PMID42059089
PMCPMC13341202

What OpenQuestion holds

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LicenceCC BY-NC
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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.