AI in medical imaging: Practical vision-language model prompt engineering for radiologists.
Authors
Yang T,Stahlhoven D,Ko HS
Affiliations (3)
Affiliations (3)
- Department of Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia. Electronic address: [email protected].
- Department of Radiology, Wellington Regional Hospital, Wellington, New Zealand.
- Department of Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia; The Sir Peter MacCallum Department of Oncology, University of Melbourne, VIC, Australia; Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Germany.
Abstract
The progressive advancement of generative artificial intelligence and in particular vision-language models (VLM) has led to its adoption and integration in radiology. Prompting, the process of providing instructional and contextual information (input) to the model, serves as a critical interaction influencing the quality and clinical usefulness of the output. This article provides an overview of the core prompting principles, provides practical examples of radiological application, and discusses limitations of VLMs relevant to clinical practice and education.
Topics
Journal Article