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Towards Efficient Annotations for a Human-AI Collaborative, Clinical Decision Support System: A Case Study on Physical Stroke Rehabilitation Assessment

Authors:

Daniel P. Siewiorek,

Asim Smailagic,

Alexandre Bernardino,

Sergi Bermúdez i BadiaAuthors Info & Claims

IUI '22: Proceedings of the 27th International Conference on Intelligent User Interfaces

Pages 4 - 14

https://doi.org/10.1145/3490099.3511112

Published: 22 March 2022 Publication History

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Abstract

Artificial intelligence (AI) and machine learning (ML) algorithms are increasingly being explored to support various decision-making tasks in health (e.g. rehabilitation assessment). However, the development of such AI/ML-based decision support systems is challenging due to the expensive process to collect an annotated dataset. In this paper, we describe the development process of a human-AI collaborative, clinical decision support system that augments an ML model with a rule-based (RB) model from domain experts. We conducted its empirical evaluation in the context of assessing physical stroke rehabilitation with the dataset of three exercises from 15 post-stroke survivors and therapists. Our results bring new insights on the efficient development and annotations of a decision support system: when an annotated dataset is not available initially, the RB model can be used to assess post-stroke survivor’s quality of motion and identify samples with low confidence scores to support efficient annotations for training an ML model. Specifically, our system requires only 22 - 33% of annotations from therapists to train an ML model that achieves equally good performance with an ML model with all annotations from a therapist. Our work discusses the values of a human-AI collaborative approach for effectively collecting an annotated dataset and supporting a complex decision-making task.

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Cited By

Martínez-Cid SVallejo DHerrera VSchez-Sobrino SCastro-Schez JAlbusac J(2025)Explainable AI-driven decision support system for personalizing rehabilitation routines in stroke recoveryProgress in Artificial Intelligence10.1007/s13748-024-00357-6Online publication date: 2-Jan-2025
https://doi.org/10.1007/s13748-024-00357-6
Anderson AGuevara JMoussaoui FLi TVorvoreanu MBurnett M(2024)Measuring User Experience Inclusivity in Human-AI Interaction via Five User Problem-Solving StylesACM Transactions on Interactive Intelligent Systems10.1145/366374014:3(1-90)Online publication date: 24-Sep-2024
https://dl.acm.org/doi/10.1145/3663740
Belli Ode Castro H(2024)A new metric for reliable diagnosis of rotating machines applied to a multi-fault rotor using Bayesian neural networksJournal of the Brazilian Society of Mechanical Sciences and Engineering10.1007/s40430-024-05222-046:11Online publication date: 14-Oct-2024
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Towards Efficient Annotations for a Human-AI Collaborative, Clinical Decision Support System: A Case Study on Physical Stroke Rehabilitation Assessment

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cover image ACM Conferences

IUI '22: Proceedings of the 27th International Conference on Intelligent User Interfaces

March 2022

888 pages

ISBN:9781450391443

DOI:10.1145/3490099

Copyright © 2022 Owner/Author.

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives International 4.0 License.

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Published: 22 March 2022

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March 22 - 25, 2022

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Cited By

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https://doi.org/10.1007/s13748-024-00357-6
Anderson AGuevara JMoussaoui FLi TVorvoreanu MBurnett M(2024)Measuring User Experience Inclusivity in Human-AI Interaction via Five User Problem-Solving StylesACM Transactions on Interactive Intelligent Systems10.1145/366374014:3(1-90)Online publication date: 24-Sep-2024
https://dl.acm.org/doi/10.1145/3663740
Belli Ode Castro H(2024)A new metric for reliable diagnosis of rotating machines applied to a multi-fault rotor using Bayesian neural networksJournal of the Brazilian Society of Mechanical Sciences and Engineering10.1007/s40430-024-05222-046:11Online publication date: 14-Oct-2024
https://doi.org/10.1007/s40430-024-05222-0
Lee MChew C(2023)Understanding the Effect of Counterfactual Explanations on Trust and Reliance on AI for Human-AI Collaborative Clinical Decision MakingProceedings of the ACM on Human-Computer Interaction10.1145/36102187:CSCW2(1-22)Online publication date: 4-Oct-2023
https://dl.acm.org/doi/10.1145/3610218
Capel TBrereton M(2023)What is Human-Centered about Human-Centered AI? A Map of the Research LandscapeProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580959(1-23)Online publication date: 19-Apr-2023
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Uymaz PUymaz AAkgül Y(2023)Assessing the Behavioral Intention of Individuals to Use an AI Doctor at the Primary, Secondary, and Tertiary Care LevelsInternational Journal of Human–Computer Interaction10.1080/10447318.2023.223312640:18(5229-5246)Online publication date: 17-Jul-2023
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