ArticleMedical science educator2024
Artificial Intelligence-Generated Facial Images for Medical Education.
Article in Medical science educator, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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
13 citing papers in PubMed.
- Visual representation bias in artificial intelligence-generated depictions of anesthesia, pain, and intensive care professionals.The Journal of international medical research · 2026Article
- AI-Generated Clinical Images of Head-and-Neck Lesions in Plastic Surgery Education: An Expert Evaluation.Aesthetic plastic surgery · 2026Article
- Bias, representation, and clinical fidelity in AI-generated images for medical education: a systematic literature review.NPJ digital medicine · 2026Article
- Generative Artificial Intelligence for Medical Image Creation in Health Professions Education: a Scoping Review.Journal of medical systems · 2026Article
- Ethical Use of Artificial Intelligence for Processing Medical Images.Journal of Korean medical science · 2025Article
- Applications, Challenges, and Prospects of Generative Artificial Intelligence Empowering Medical Education: Scoping Review.JMIR medical education · 2025Article
- Technologies, opportunities, challenges, and future directions for integrating generative artificial intelligence into medical education: a narrative review.Ewha medical journal · 2025Article
- Text to image generators for anatomical illustrations: potential and limitations.Surgical and radiologic anatomy : SRA · 2025Article
- Demographic inaccuracies and biases in the depiction of patients by artificial intelligence text-to-image generators.NPJ digital medicine · 2025Article
- Dall-E in hand surgery: Exploring the utility of ChatGPT image generation.Surgery open science · 2025Article
- Quality assurance and validity of AI-generated single best answer questions.BMC medical education · 2025Article
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Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We evaluated the use of text-to-image models (Microsoft's Bing Image creator (powered by DALL·E) and Shutterstock's AI image generator) to generate realistic images of human faces and their associated pathology, which may be useful for medical education, given they may overcome issues of patient privacy and requirement for consent. These models have potential to augment rare medical image datasets for medical education, as well as provide greater inclusivity and representation of diverse populations.
Indexed as
Identifiers
What OpenQuestion holds
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.