Receptivity of an AI Cognitive Assistant by the Radiology Community: A Report on Data Collected at RSNA

Karina Kanjaria, Anup Pillai, Chaitanya Shivade, Marina Bendersky, Vandana Mukherjee, Tanveer Syeda-Mahmood

2020

Abstract

Due to advances in machine learning and artificial intelligence (AI), a new role is emerging for machines as intelligent assistants to radiologists in their clinical workflows. But what systematic clinical thought processes are these machines using? Are they similar enough to those of radiologists to be trusted as assistants? A live demonstration of such a technology was conducted at the 2016 Scientific Assembly and Annual Meeting of the Radiological Society of North America (RSNA). The demonstration was presented in the form of a question-answering system that took a radiology multiple choice question and a medical image as inputs. The AI system then demonstrated a cognitive workflow, involving text analysis, image analysis, and reasoning, to process the question and generate the most probable answer. A post demonstration survey was made available to the participants who experienced the demo and tested the question answering system. Of the reported 54,037 meeting registrants, 2,927 visited the demonstration booth, 1,991 experienced the demo, and 1,025 completed a post-demonstration survey. In this paper, the methodology of the survey is shown and a summary of its results are presented. The results of the survey show a very high level of receptiveness to cognitive computing technology and artificial intelligence among radiologists.

Paper Citation

in Harvard Style

Kanjaria K., Pillai A., Shivade C., Bendersky M., Mukherjee V. and Syeda-Mahmood T. (2020). Receptivity of an AI Cognitive Assistant by the Radiology Community: A Report on Data Collected at RSNA. In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 5: HEALTHINF; ISBN 978-989-758-398-8, SciTePress, pages 178-186. DOI: 10.5220/0008984901780186

in Bibtex Style

@conference{healthinf20,
author={Karina Kanjaria and Anup Pillai and Chaitanya Shivade and Marina Bendersky and Vandana Mukherjee and Tanveer Syeda-Mahmood},
title={Receptivity of an AI Cognitive Assistant by the Radiology Community: A Report on Data Collected at RSNA},
booktitle={Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 5: HEALTHINF},
year={2020},
pages={178-186},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008984901780186},
isbn={978-989-758-398-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 5: HEALTHINF
TI - Receptivity of an AI Cognitive Assistant by the Radiology Community: A Report on Data Collected at RSNA
SN - 978-989-758-398-8
AU - Kanjaria K.
AU - Pillai A.
AU - Shivade C.
AU - Bendersky M.
AU - Mukherjee V.
AU - Syeda-Mahmood T.
PY - 2020
SP - 178
EP - 186
DO - 10.5220/0008984901780186
PB - SciTePress