Entering the era of photorealistic AI-generated medical images

Clicks: 35
ID: 320247
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #33 of 69 articles by views in the nigerian postgraduate medical journal

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Abstract Artificial intelligence (AI)-generated medical images are reaching photorealistic quality, which could help medical education within visually driven specialties such as ophthalmology and dermatology. Such technology may make customizable teaching materials more accessible. However, as real and generated images become harder to distinguish, several concerns emerge. AI hallucinations may pollute real medical image databases, evidence-based medicine could be harmed, risks of fraud increase, and learners’ non-technical clinical skills and visual diagnostic ability may decline. To address these challenges, we propose a three-stage human-machine collaborative framework. Stage 1 uses AI-generated disease images for static feature recognition, Stage 2 simulates disease trajectories for dynamic clinical reasoning, and Stage 3 integrates supervised human-machine collaborative diagnosis for real-world decision-making. Our previous work demonstrated that training with AI-generated images significantly improved medical students’ diagnostic accuracy for infectious keratitis from 42.68% to 71.27% (n = 37, P < .001) and diagnostic confidence from 1.6 to 3.1 on a 5-point scale (n = 13, P < .001). This framework may help students develop essential skills for AI-integrated healthcare, including identifying AI hallucinations and maintaining diagnostic autonomy. Medical educators must shift from passively implementing AI tools to actively governing their use. This requires redefining educators as both technical instructors and ethical supervisors, building better detection tools for AI-generated materials, and adding AI education within core curricula. We aim to prepare young doctors for a future AI-integrated healthcare environment.
Reference Key
openalex_W7167792378 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Wenjia Xie
Are you one of these authors? Claim your profile
Journal the nigerian postgraduate medical journal
Year 2026
DOI
10.1093/postmj/qgag085
URL
https://doi.org/10.1093/postmj/qgag085
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.