Medical domain knowledge in domain-agnostic generative AI Article Swipe
Jakob Nikolas Kather
,
Narmin Ghaffari Laleh
,
Sebastian Foersch
,
Daniel Truhn
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1101/2022.01.10.22269025
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1101/2022.01.10.22269025
The text-guided diffusion model GLIDE (Guided Language to Image Diffusion for Generation and Editing) is the state of the art in text-to-image generative artificial intelligence (AI). GLIDE has rich representations, but medical applications of this model have not been systematically explored. If GLIDE had useful medical knowledge, it could be used for medical image analysis tasks, a domain in which AI systems are still highly engineered towards a single use-case. Here we show that the publicly available GLIDE model has reasonably strong representations of key topics in cancer research and oncology, in particular the general style of histopathology images and multiple facets of diseases, pathological processes and laboratory assays. However, GLIDE seems to lack useful representations of the style and content of radiology data. Our findings demonstrate that domain-agnostic generative AI models can learn relevant medical concepts without explicit training. Thus, GLIDE and similar models might be useful for medical image processing tasks in the future - particularly with additional domain-specific fine-tuning.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2022.01.10.22269025
- https://www.medrxiv.org/content/medrxiv/early/2022/01/11/2022.01.10.22269025.full.pdf
- OA Status
- green
- Cited By
- 4
- References
- 8
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4205608468
All OpenAlex metadata
Raw OpenAlex JSON
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- DOI
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https://doi.org/10.1101/2022.01.10.22269025Digital Object Identifier
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Medical domain knowledge in domain-agnostic generative AIWork title
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-01-11Full publication date if available
- Authors
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Jakob Nikolas Kather, Narmin Ghaffari Laleh, Sebastian Foersch, Daniel TruhnList of authors in order
- Landing page
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https://doi.org/10.1101/2022.01.10.22269025Publisher landing page
- PDF URL
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https://www.medrxiv.org/content/medrxiv/early/2022/01/11/2022.01.10.22269025.full.pdfDirect link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://www.medrxiv.org/content/medrxiv/early/2022/01/11/2022.01.10.22269025.full.pdfDirect OA link when available
- Concepts
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Domain (mathematical analysis), Generative grammar, Computer science, Generative model, Artificial intelligence, Image (mathematics), Domain knowledge, Natural language processing, Mathematics, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
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4Total citation count in OpenAlex
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2024: 3, 2023: 1Per-year citation counts (last 5 years)
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8Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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