Evidence map›Paper›PMID 39712811›Full record

ArticleCureus2024

Evaluating Text-to-Image Generated Photorealistic Images of Human Anatomy.

Paula Muhr, Yating Pan, Charlotte Tumescheit, Ann-Kathrin Kübler, Hatice Kübra Parmaksiz, Cheng Chen, Pablo Sebastián Bolaños Orozco, Soeren S Lienkamp, Janna Hastings

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
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

9 authors.

Paula MuhrFaculty of Medicine, Institute for Implementation Science in Health Care, University of Zurich, Zurich, CHE.
Yating PanDigital Society Initiative, University of Zurich, Zurich, CHE.
Charlotte TumescheitFaculty of Medicine, Institute for Implementation Science in Health Care, University of Zurich, Zurich, CHE.
Ann-Kathrin KüblerDigital Society Initiative, University of Zurich, Zurich, CHE.
Hatice Kübra ParmaksizDigital Society Initiative, University of Zurich, Zurich, CHE.
Cheng ChenDigital Society Initiative, University of Zurich, Zurich, CHE.
Pablo Sebastián Bolaños OrozcoDigital Society Initiative, University of Zurich, Zurich, CHE.
Soeren S LienkampFaculty of Medicine, Institute for Anatomy, University of Zurich, Zurich, CHE.
Janna HastingsFaculty of Medicine, Institute for Implementation Science in Health Care, University of Zurich, Zurich, CHE.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGenerative artificial intelligence (AI) models that can produce photorealistic images from text descriptions have many applications in medicine, including medical education and the generation of synthetic data. However, it can be challenging to evaluate their heterogeneous outputs and to compare between different models. There is a need for a systematic approach enabling image and model comparisons.

methodTo address this gap, we developed an error classification system for annotating errors in AI-generated photorealistic images of humans and applied our method to a corpus of 240 images generated with three different models (DALL-E 3, Stable Diffusion XL, and Stable Cascade) using 10 prompts with eight images per prompt.

resultsThe error classification system identifies five different error types with three different severities across five anatomical regions and specifies an associated quantitative scoring method based on aggregated proportions of errors per expected count of anatomical components for the generated image. We assessed inter-rater agreement by double-annotating 25% of the images and calculating Krippendorf's alpha and compared results across the three models and 10 prompts quantitatively using a cumulative score per image. The error classification system, accompanying training manual, generated image collection, annotations, and all associated scripts, is available from our GitHub repository at https://github.com/hastingslab-org/ai-human-images. Inter-rater agreement was relatively poor, reflecting the subjectivity of the error classification task. Model comparisons revealed that DALL-E 3 performed consistently better than Stable Diffusion; however, the latter generated images reflecting more diversity in personal attributes. Images with groups of people were more challenging for all the models than individuals or pairs; some prompts were challenging for all models.

conclusionOur method enables systematic comparison of AI-generated photorealistic images of humans; our results can serve to catalyse improvements in these models for medical applications.

Indexed as

ai in medicineanatomical educationdiffusion modelgenerative ailarge multi-modal modelmedical imagephotorealistic synthetic anatomical imagetext-to-image generation

Identifiers

PMID39712811
PMCPMC11663238

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.