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Human-Centered AI for Medical Imaging

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Artificial Intelligence for Human Computer Interaction: A Modern Approach

Part of the book series: Human–Computer Interaction Series ((HCIS))

Abstract

Medical imaging is the primary data source most physicians refer to when making a diagnosis. However, examination of medical imaging data, due to its density and uncertainty, can be time-consuming and error-prone. The recent advent of data-driven artificial intelligence (AI) provides a promising solution, yet the adoption of AI in medicine is often hindered by the ‘black box’ nature. This chapter reviews how AI can distil new insights from medical imaging data and how a human-centered approach can transform AIs role as one that engages patients with self-assessment and personalized models and as one that enables physicians to comprehend and control how AI performs a diagnosis, thus able to collaborate with AI in making a diagnosis.

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Notes

  1. 1.

    We consider pathology as a type of medical imaging specialty, as the recent development in digital pathology has fostered a growing body of work on processing digitalized whole slide images, although traditionally images obtained from removed tissues for studying pathology are not considered medical imaging.

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Authors and Affiliations

  1. UCLA, Los Angeles, USA

    Yuan Liang, Lei He & Xiang ‘Anthony’ Chen

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  1. Yuan Liang
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  2. Lei He
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  3. Xiang ‘Anthony’ Chen
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Correspondence to Xiang ‘Anthony’ Chen .

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  1. Google Research (United States), Mountain View, CA, USA

    Yang Li

  2. Advanced Interactive Technologies Lab, ETH Zurich, Zurich, Switzerland

    Otmar Hilliges

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Liang, Y., He, L., ‘Anthony’ Chen, X. (2021). Human-Centered AI for Medical Imaging. In: Li, Y., Hilliges, O. (eds) Artificial Intelligence for Human Computer Interaction: A Modern Approach. Human–Computer Interaction Series. Springer, Cham. https://doi.org/10.1007/978-3-030-82681-9_16

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  • DOI: https://doi.org/10.1007/978-3-030-82681-9_16

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