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Cited by 82Artificial Intelligence in Healthcare and EducationMultimodal Machine Learning ApplicationsRadiology practices and education

Multimodal Healthcare AI: Identifying and Designing Clinically Relevant Vision-Language Applications for Radiology

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Yildirim, Nur, Richardson, Hannah, Wetscherek, Maria Teodora, Bajwa, Junaid, Jacob, Joseph, Pinnock, Mark Ames, Harris, Stephen, Coelho De Castro, Daniel +13 more·2024·Proceedings of the CHI Conference on Human Factors in Computing Systems
DOI 10.1145/3613904.3642013
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Abstract

Recent advances in AI combine large language models (LLMs) with vision encoders that bring forward unprecedented technical capabilities to leverage for a wide range of healthcare applications. Focusing on the domain of radiology, vision-language models (VLMs) achieve good performance results for tasks such as generating radiology findings based on a patient’s medical image, or answering visual questions (e.g., “Where are the nodules in this chest X-ray?”). However, the clinical utility of potential applications of these capabilities is currently underexplored. We engaged in an iterative, multidisciplinary design process to envision clinically relevant VLM interactions, and co-designed four VLM use concepts: Draft Report Generation, Augmented Report Review, Visual Search and Querying, and Patient Imaging History Highlights. We studied these concepts with 13 radiologists and clinicians who assessed the VLM concepts as valuable, yet articulated many design considerations. Reflecting on our findings, we discuss implications for integrating VLM capabilities in radiology, and for healthcare AI more generally.

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  • A variant of the grounded reporting task was explored in Tanida et al., where the model first located anatomical regions before generating region-level descriptions.

    “A variant of this task was explored in Tanida et al. 8 , where the model first located anatomical regions before generating region-level descriptions.”

    From MAIRA-2: Grounded Radiology Report Generation (2024) · cites this paper as [8]

    Section 6.2 (p. 7) describes a draft-report prototype that links findings to image locations, but it does not describe a model locating anatomical regions before generating region-level descriptions. Searches for Tanida and that sequence found no passage addressing the attributed study. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

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References 91 · Cited by 82

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Authors
Yildirim, N., Richardson, H., Wetscherek, M. T., Bajwa, J., Jacob, J., Pinnock, M. A., Harris, S., Coelho De Castro, D., Bannur, S., Hyland, S., Ghosh, P., Ranjit, M., Bouzid, K., Schwaighofer, A., Pérez-García, F., Sharma, H., Oktay, O., Lungren, M., Alvarez-Valle, J., Nori, A., & Thieme, A.
Affiliations
  • Carnegie Mellon University (US)
  • Microsoft (United Kingdom) (GB)
  • Cambridge University Hospitals NHS Foundation Trust (GB)
  • University College London (GB)
  • University College London Hospital NHS Foundation Trust, United Kingdom
  • Microsoft Health Futures, United Kingdom
  • Microsoft (India) (IN)
  • Microsoft (United States) (US)
ORCID
Venue
Proceedings of the CHI Conference on Human Factors in Computing Systems
Publisher
ACM
Published
2024-05-11
Pages
1-22
DOI
10.1145/3613904.3642013
OpenAlex
W4392120599
Cited by
82
References
91
Citations by year
2026: 28 · 2025: 45 · 2024: 9
FWCI
22.08
Type
conference paper
Language
en
Open access
Licence
Locations
  • CHI Conference on Human Factors in Computing Systems (publishedVersion, cc-by-nd)
  • arXiv (Cornell University) (submittedVersion, cc-by-nc-nd)
  • UCL Discovery (University College London) (submittedVersion, cc-by-nd)
  • arXiv (Cornell University)
Funders
  • Wellcome Trust (Z/17/Z, 209553/Z/17/Z)
  • National Institute for Health and Care Research
  • University College London Hospitals NHS Foundation Trust
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiology practices and education
Keywords
vision-language models, radiology, large language models, radiology report generation, visual question answering, visual search

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

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