Towards Visual Dialog for Radiology
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Olga Kovaleva, Chaitanya Shivade, Satyananda Kashyap, Karina Kanjaria, Joy Wu, Deddeh Ballah, Adam Coy, Alexandros Karargyris, Yufan Guo, David Beymer Beymer, Anna Rumshisky, Vandana Mukherjee Mukherjee. Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing. 2020.
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X-rays are only one of many data points available for a patient's diagnosis, and including additional parameters such as medications, lab data, and clinical data in AI agents is important to overcome diagnostic limitations.
“However, they also pointed out that X-rays are only one of the many data points available (e.g., medications, lab data, clinical data) for a patient's diagnosis and emphasize the importance of including these additional parameters in AI agents to overcome diagnostic limitations.”
From Explainable AI in medical imaging: An overview for clinical practitioners – Beyond saliency-based XAI approaches (2023) · cites this paper as [41]
RadVisDial is the first publicly available dataset for visual dialog in radiology.
“For example, in [41], Visual Dialog was deployed in radiology specific to chest X-ray images bringing forth RadVisDial, the first publicly available dataset for visual dialog in radiology.”
From Explainable AI in medical imaging: An overview for clinical practitioners – Beyond saliency-based XAI approaches (2023) · cites this paper as [41]
Diagnostic processes often require assessing multiple data points, such as different X-ray views, comparisons between modalities, or changes over time.
“However, diagnostic processes often rely on the assessment of multiple data points [41], such as different X-Ray views (lateral, oblique, or anteroposterior), comparisons between modalities, or evaluating changes over time (e.g., follow-up examinations).”
From Explainable AI in medical imaging: An overview for clinical practitioners – Beyond saliency-based XAI approaches (2023) · cites this paper as [41]
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- Authors
- Kovaleva, O., Shivade, C., Kashyap, S., Kanjaria, K., Wu, J., Ballah, D., Coy, A., Karargyris, A., Guo, Y., Beymer, D. B., Rumshisky, A., & Mukherjee, V. M.
- Affiliations
- University of Massachusetts Lowell (US)
- Amazon (United States) (US)
- IBM (United States) (US)
- ORCID
- Venue
- Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing
- Published
- 2020
- Pages
- 60-69
- OpenAlex
- W3037181498
- Cited by
- 38
- References
- 37
- Citations by year
- 2026: 1 · 2025: 12 · 2024: 11 · 2023: 12 · 2022: 2
- FWCI
- 1.08
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Workshop on Biomedical Language Processing (BioNLP) (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Keywords
- visual dialogue, radiology, medical image interpretation, multimodal dialogue, medical question answering
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