Details
| Originalsprache | Englisch |
|---|---|
| Fachzeitschrift | i-com |
| Publikationsstatus | Elektronisch veröffentlicht (E-Pub) - 18 Feb. 2025 |
Abstract
Data-driven storytelling has grown significantly, becoming prevalent in various fields, including healthcare. In medical narratives, characters are crucial for engaging audiences, making complex medical information accessible, and potentially influencing positive behavioral and lifestyle changes. However, designing characters that are both educational and relatable to effectively engage audiences is challenging. We propose a GenAI-assisted pipeline for character design in data-driven medical stories, utilizing Stable Diffusion, a deep learning text-to-image model, to transform data into visual character representations. This approach reduces the time and artistic skills required to create characters that reflect the underlying data. As a proof-of-concept, we generated and evaluated two characters in a crowd-sourced case study, assessing their authenticity to the underlying data and consistency over time. In a qualitative evaluation with four experts with knowledge in design and health communication, the characters were discussed regarding their quality and refinement opportunities. The characters effectively conveyed various aspects of the data, such as emotions, age, and body weight. However, generating multiple consistent images of the same character proved to be a significant challenge. This underscores a key issue in using generative AI for character creation: the limited control designers have over the output.
ASJC Scopus Sachgebiete
- Psychologie (insg.)
- Sozialpsychologie
- Informatik (insg.)
- Information systems
- Betriebswirtschaft, Management und Rechnungswesen (insg.)
- Betriebswirtschaft, Management und Rechnungswesen (sonstige)
- Sozialwissenschaften (insg.)
- Kommunikation
- Informatik (insg.)
- Mensch-Maschine-Interaktion
- Informatik (insg.)
- Computernetzwerke und -kommunikation
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in: i-com, 18.02.2025.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - AI-based character generation for disease stories
T2 - A case study using epidemiological data to highlight preventable risk factors
AU - Mittenentzwei, Sarah
AU - Garrison, Laura A.
AU - Budich, Beatrice
AU - Lawonn, Kai
AU - Dockhorn, Alexander
AU - Preim, Bernhard
AU - Meuschke, Monique
N1 - Publisher Copyright: © 2025 the author(s), published by De Gruyter, Berlin/Boston 2025.
PY - 2025/2/18
Y1 - 2025/2/18
N2 - Data-driven storytelling has grown significantly, becoming prevalent in various fields, including healthcare. In medical narratives, characters are crucial for engaging audiences, making complex medical information accessible, and potentially influencing positive behavioral and lifestyle changes. However, designing characters that are both educational and relatable to effectively engage audiences is challenging. We propose a GenAI-assisted pipeline for character design in data-driven medical stories, utilizing Stable Diffusion, a deep learning text-to-image model, to transform data into visual character representations. This approach reduces the time and artistic skills required to create characters that reflect the underlying data. As a proof-of-concept, we generated and evaluated two characters in a crowd-sourced case study, assessing their authenticity to the underlying data and consistency over time. In a qualitative evaluation with four experts with knowledge in design and health communication, the characters were discussed regarding their quality and refinement opportunities. The characters effectively conveyed various aspects of the data, such as emotions, age, and body weight. However, generating multiple consistent images of the same character proved to be a significant challenge. This underscores a key issue in using generative AI for character creation: the limited control designers have over the output.
AB - Data-driven storytelling has grown significantly, becoming prevalent in various fields, including healthcare. In medical narratives, characters are crucial for engaging audiences, making complex medical information accessible, and potentially influencing positive behavioral and lifestyle changes. However, designing characters that are both educational and relatable to effectively engage audiences is challenging. We propose a GenAI-assisted pipeline for character design in data-driven medical stories, utilizing Stable Diffusion, a deep learning text-to-image model, to transform data into visual character representations. This approach reduces the time and artistic skills required to create characters that reflect the underlying data. As a proof-of-concept, we generated and evaluated two characters in a crowd-sourced case study, assessing their authenticity to the underlying data and consistency over time. In a qualitative evaluation with four experts with knowledge in design and health communication, the characters were discussed regarding their quality and refinement opportunities. The characters effectively conveyed various aspects of the data, such as emotions, age, and body weight. However, generating multiple consistent images of the same character proved to be a significant challenge. This underscores a key issue in using generative AI for character creation: the limited control designers have over the output.
KW - character generation
KW - data-storytelling
KW - GenAI
KW - healthcare
KW - medicine
UR - http://www.scopus.com/inward/record.url?scp=85219083354&partnerID=8YFLogxK
U2 - 10.1515/icom-2024-0041
DO - 10.1515/icom-2024-0041
M3 - Article
AN - SCOPUS:85219083354
JO - i-com
JF - i-com
SN - 1618-162X
ER -