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Guidelines and Experience Using Imaging Biomarker Explorer (IBEX) for Radiomics
Published on: January 8, 2018
AI in medical imaging: Practical vision-language model prompt engineering for radiologists
Tianpei Yang1, Daniel Stahlhoven2, Hyun Soo Ko3
1Department of Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.
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.
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