ReviewFrontiers in public health2026
Artificial intelligence in biomedical visualization and education: a narrative review.
Review in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Biomedical visualization increasingly spans traditional medical illustration, synthetic medical imaging, vision-language systems, and patient-specific 3D reconstruction. This narrative review examines applications of artificial intelligence (AI) across this field, with particular attention to medical education and scientific and clinical communication. We searched PubMed/MEDLINE and IEEE Xplore for English-language literature published from January 2017 through September 2026, supplemented by targeted searches of publisher databases and reference-list screening. Included publications addressed AI-enabled medical or anatomical illustration, text-to-image generation, synthetic medical imaging, vision-language models, 3D reconstruction, or immersive biomedical visualization. Current evidence indicates that diffusion models can rapidly generate visually persuasive and customizable drafts, whereas segmentation-guided synthesis and anatomy-aware 3D methods offer stronger structural control in defined tasks. However, direct evaluations of general-purpose text-to-image systems consistently identify fabricated anatomy, incorrect labels, prompt- and model-dependent variability, demographic bias, and limited reliability for complex structures or procedures. Vision-language models can support prompt refinement, annotation, and consistency checking, but they cannot ensure scientific accuracy and may evaluate flawed AI outputs more favorably than human experts. Evidence supporting AI-generated educational illustrations remains less mature than that supporting conventional 3D and virtual-reality learning environments. We therefore distinguish established applications from emerging or prospective uses and propose an expert-supervised workflow incorporating documented prompts and model versions, structure-specific validation, manual correction, and transparent disclosure. AI should currently be treated as a drafting and visualization aid, with domain-expert verification required before educational, clinical, or scientific use.
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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.