ArticleJournal of medical Internet research2023
What Does DALL-E 2 Know About Radiology?
Article in Journal of medical Internet research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 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
29 citing papers in PubMed, 72 citations in OpenAlex.
- Blind spots in artificial intelligence systems: poor identification of self-generated medical images-evidence-based cross-sectional study.Frontiers in medicine · 2026Article
- Detectability and healthcare implications of generative AI-synthesized chest radiographs: a blinded radiologist reader study.Frontiers in medicine · 2026Article
- Reinforcing the stereotype: an analysis of AI-generated images of patients with obstructive sleep apnea.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2025Article
- Limitations of Artificial Intelligence Generated Images for Hand Surgery Patient Education.Journal of hand surgery global online · 2025Article
- Generative artificial intelligence in medicine.Nature medicine · 2025Review
- 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
- Representation of Demographics in Otolaryngology by Artificial Intelligence Text-to-Image Platforms.Laryngoscope investigative otolaryngology · 2025Article
- Designing Personalized Multimodal Mnemonics With AI: A Medical Student's Implementation Tutorial.JMIR medical education · 2025Article
- Evaluating Microsoft Bing with ChatGPT-4 for the assessment of abdominal computed tomography and magnetic resonance images.Diagnostic and interventional radiology (Ankara, Turkey) · 2025Article
- A Validity Analysis of Text-to-Image Generative Artificial Intelligence Models for Craniofacial Anatomy Illustration.Journal of clinical medicine · 2025Article
- A Future Picture: A Review of Current Generative Adversarial Neural Networks in Vitreoretinal Pathologies and Their Future Potentials.Biomedicines · 2025Review
- Assessing the Current Limitations of Large Language Models in Advancing Health Care Education.JMIR formative research · 2025Article
- Gender and racial diversity Assumed by text-to-image generators in microsurgery and plastic surgery-related subspecialities.Journal of hand and microsurgery · 2025Article
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- Generative artificial intelligence in ophthalmology: current innovations, future applications and challenges.The British journal of ophthalmology · 2024Review
- Reinforcing Stereotypes in Health Care Through Artificial Intelligence-Generated Images: A Call for Regulation.Mayo Clinic proceedings. Digital health · 2024Review
- ChatGPT and generative AI in urology and surgery-A narrative review.BJUI compass · 2024Review
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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
6 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Generative models, such as DALL-E 2 (OpenAI), could represent promising future tools for image generation, augmentation, and manipulation for artificial intelligence research in radiology, provided that these models have sufficient medical domain knowledge. Herein, we show that DALL-E 2 has learned relevant representations of x-ray images, with promising capabilities in terms of zero-shot text-to-image generation of new images, the continuation of an image beyond its original boundaries, and the removal of elements; however, its capabilities for the generation of images with pathological abnormalities (eg, tumors, fractures, and inflammation) or computed tomography, magnetic resonance imaging, or ultrasound images are still limited. The use of generative models for augmenting and generating radiological data thus seems feasible, even if the further fine-tuning and adaptation of these models to their respective domains are required first.
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.