Evidence map›Paper›PMID 39639085›Full record

ArticleScientific reports2024

A comparative analysis of text-to-image generative AI models in scientific contexts: a case study on nuclear power.

Veda Joynt, Jacob Cooper, Naman Bhargava, Katie Vu, O Hwang Kwon, Todd R Allen, Aditi Verma, Majdi I Radaideh

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

8 authors.

Veda JoyntDepartment of Nuclear Engineering and Radiological Sciences, University of Michigan, Ann Arbor, MI, 48109, USA.
Jacob CooperDepartment of Nuclear Engineering and Radiological Sciences, University of Michigan, Ann Arbor, MI, 48109, USA.
Naman BhargavaDepartment of Statistics, University of Michigan, Ann Arbor, MI, 48109, USA.
Katie VuDepartment of Computer Science and Engineering, University of Michigan, Ann Arbor, MI, 48109, USA.
O Hwang KwonDepartment of Nuclear Engineering and Radiological Sciences, University of Michigan, Ann Arbor, MI, 48109, USA.
Todd R AllenDepartment of Nuclear Engineering and Radiological Sciences, University of Michigan, Ann Arbor, MI, 48109, USA.
Aditi VermaDepartment of Nuclear Engineering and Radiological Sciences, University of Michigan, Ann Arbor, MI, 48109, USA.
Majdi I RadaidehDepartment of Nuclear Engineering and Radiological Sciences, University of Michigan, Ann Arbor, MI, 48109, USA. radaideh@umich.edu.

Funding

Fastest Path to Zero at the University of Michigan G028455U.S. Department of Energy DE-NE0009497
6 · The paper itself

Abstract

In this work, we propose and assess the potential of generative artificial intelligence (AI) as a tool for facilitating public engagement around potential clean energy sources. Such an application could increase energy literacy-an awareness of low-carbon energy sources among the public therefore leading to increased participation in decision-making about the future of energy systems. We explore the use of generative AI to communicate technical information about low-carbon energy sources to the general public, specifically in the realm of nuclear energy. We explored 20 AI-powered text-to-image generators and compared their individual performances on general and scientific nuclear-related prompts. Of these models, DALL-E, DreamStudio, and Craiyon demonstrated promising performance in generating relevant images from general-level text related to nuclear topics. However, these models fall short in three crucial ways: (1) they fail to accurately represent technical details of energy systems; (2) they reproduce existing biases surrounding gender and work in the energy sector; and (3) they fail to accurately represent indigenous landscapes-which have historically been sites of resource extraction and waste deposition for energy industries. This work is performed to motivate the development of specialized generative tools to improve energy literacy and effectively engage the public with low-carbon energy sources.

Indexed as

DALL-EGenerative AINuclear powerPrompt engineeringPublic policyText-to-image generation

Identifiers

PMID39639085
PMCPMC11621808

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.