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What Would it Take to get Biomedical QA Systems into Practice?

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

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What Would it Take to get Biomedical QA Systems into Practice? / Kell, Gregory; Marshall, Iain J.; Wallace, Byron C. et al.

Proceedings of the 3rd Workshop on Machine Reading for Question Answering, MRQA 2021. ed. / Adam Fisch; Alon Talmor; Danqi Chen; Eunsol Choi; Minjoon Seo; Patrick Lewis; Robin Jia; Sewon Min. Association for Computational Linguistics (ACL), 2021. p. 28-41 (Proceedings of the 3rd Workshop on Machine Reading for Question Answering, MRQA 2021).

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

Harvard

Kell, G, Marshall, IJ, Wallace, BC & Jaun, A 2021, What Would it Take to get Biomedical QA Systems into Practice? in A Fisch, A Talmor, D Chen, E Choi, M Seo, P Lewis, R Jia & S Min (eds), Proceedings of the 3rd Workshop on Machine Reading for Question Answering, MRQA 2021. Proceedings of the 3rd Workshop on Machine Reading for Question Answering, MRQA 2021, Association for Computational Linguistics (ACL), pp. 28-41, 3rd Workshop on Machine Reading for Question Answering, MRQA 2021, Punta Cana, Dominican Republic, 10/11/2021.

APA

Kell, G., Marshall, I. J., Wallace, B. C., & Jaun, A. (2021). What Would it Take to get Biomedical QA Systems into Practice? In A. Fisch, A. Talmor, D. Chen, E. Choi, M. Seo, P. Lewis, R. Jia, & S. Min (Eds.), Proceedings of the 3rd Workshop on Machine Reading for Question Answering, MRQA 2021 (pp. 28-41). (Proceedings of the 3rd Workshop on Machine Reading for Question Answering, MRQA 2021). Association for Computational Linguistics (ACL).

Vancouver

Kell G, Marshall IJ, Wallace BC, Jaun A. What Would it Take to get Biomedical QA Systems into Practice? In Fisch A, Talmor A, Chen D, Choi E, Seo M, Lewis P, Jia R, Min S, editors, Proceedings of the 3rd Workshop on Machine Reading for Question Answering, MRQA 2021. Association for Computational Linguistics (ACL). 2021. p. 28-41. (Proceedings of the 3rd Workshop on Machine Reading for Question Answering, MRQA 2021).

Author

Kell, Gregory ; Marshall, Iain J. ; Wallace, Byron C. et al. / What Would it Take to get Biomedical QA Systems into Practice?. Proceedings of the 3rd Workshop on Machine Reading for Question Answering, MRQA 2021. editor / Adam Fisch ; Alon Talmor ; Danqi Chen ; Eunsol Choi ; Minjoon Seo ; Patrick Lewis ; Robin Jia ; Sewon Min. Association for Computational Linguistics (ACL), 2021. pp. 28-41 (Proceedings of the 3rd Workshop on Machine Reading for Question Answering, MRQA 2021).

Bibtex Download

@inbook{c51c4bc65521471fb085237bbf0b305d,
title = "What Would it Take to get Biomedical QA Systems into Practice?",
abstract = "Medical question answering (QA) systems have the potential to answer clinicians' uncertainties about treatment and diagnosis on-demand, informed by the latest evidence. However, despite the significant progress in general QA made by the NLP community, medical QA systems are still not widely used in clinical environments. One likely reason for this is that clinicians may not readily trust QA system outputs, in part because transparency, trustworthiness, and provenance have not been key considerations in the design of such models. In this paper we discuss a set of criteria that, if met, we argue would likely increase the utility of biomedical QA systems, which may in turn lead to adoption of such systems in practice. We assess existing models, tasks, and datasets with respect to these criteria, highlighting shortcomings of previously proposed approaches and pointing toward what might be more usable QA systems.",
author = "Gregory Kell and Marshall, {Iain J.} and Wallace, {Byron C.} and Andr{\'e} Jaun",
note = "Funding Information: This work was supported in part by the National Institutes of Health (NIH), grant R01-LM012086. GK holds a doctoral studentship co-sponsored by Metadvice and the Guy's and St Thomas' Biomedical Research Centre. Funding Information: This work was supported in part by the National Institutes of Health (NIH), grant R01-LM012086. Publisher Copyright: {\textcopyright} 2021 Association for Computational Linguistics.; 3rd Workshop on Machine Reading for Question Answering, MRQA 2021 ; Conference date: 10-11-2021",
year = "2021",
language = "English",
series = "Proceedings of the 3rd Workshop on Machine Reading for Question Answering, MRQA 2021",
publisher = "Association for Computational Linguistics (ACL)",
pages = "28--41",
editor = "Adam Fisch and Alon Talmor and Danqi Chen and Eunsol Choi and Minjoon Seo and Patrick Lewis and Robin Jia and Sewon Min",
booktitle = "Proceedings of the 3rd Workshop on Machine Reading for Question Answering, MRQA 2021",

}

RIS (suitable for import to EndNote) Download

TY - CHAP

T1 - What Would it Take to get Biomedical QA Systems into Practice?

AU - Kell, Gregory

AU - Marshall, Iain J.

AU - Wallace, Byron C.

AU - Jaun, André

N1 - Funding Information: This work was supported in part by the National Institutes of Health (NIH), grant R01-LM012086. GK holds a doctoral studentship co-sponsored by Metadvice and the Guy's and St Thomas' Biomedical Research Centre. Funding Information: This work was supported in part by the National Institutes of Health (NIH), grant R01-LM012086. Publisher Copyright: © 2021 Association for Computational Linguistics.

PY - 2021

Y1 - 2021

N2 - Medical question answering (QA) systems have the potential to answer clinicians' uncertainties about treatment and diagnosis on-demand, informed by the latest evidence. However, despite the significant progress in general QA made by the NLP community, medical QA systems are still not widely used in clinical environments. One likely reason for this is that clinicians may not readily trust QA system outputs, in part because transparency, trustworthiness, and provenance have not been key considerations in the design of such models. In this paper we discuss a set of criteria that, if met, we argue would likely increase the utility of biomedical QA systems, which may in turn lead to adoption of such systems in practice. We assess existing models, tasks, and datasets with respect to these criteria, highlighting shortcomings of previously proposed approaches and pointing toward what might be more usable QA systems.

AB - Medical question answering (QA) systems have the potential to answer clinicians' uncertainties about treatment and diagnosis on-demand, informed by the latest evidence. However, despite the significant progress in general QA made by the NLP community, medical QA systems are still not widely used in clinical environments. One likely reason for this is that clinicians may not readily trust QA system outputs, in part because transparency, trustworthiness, and provenance have not been key considerations in the design of such models. In this paper we discuss a set of criteria that, if met, we argue would likely increase the utility of biomedical QA systems, which may in turn lead to adoption of such systems in practice. We assess existing models, tasks, and datasets with respect to these criteria, highlighting shortcomings of previously proposed approaches and pointing toward what might be more usable QA systems.

UR - http://www.scopus.com/inward/record.url?scp=85132162300&partnerID=8YFLogxK

M3 - Conference paper

AN - SCOPUS:85132162300

T3 - Proceedings of the 3rd Workshop on Machine Reading for Question Answering, MRQA 2021

SP - 28

EP - 41

BT - Proceedings of the 3rd Workshop on Machine Reading for Question Answering, MRQA 2021

A2 - Fisch, Adam

A2 - Talmor, Alon

A2 - Chen, Danqi

A2 - Choi, Eunsol

A2 - Seo, Minjoon

A2 - Lewis, Patrick

A2 - Jia, Robin

A2 - Min, Sewon

PB - Association for Computational Linguistics (ACL)

T2 - 3rd Workshop on Machine Reading for Question Answering, MRQA 2021

Y2 - 10 November 2021

ER -

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