A human-AI collaborative approach for clinical decision making on rehabilitation assessment

Publication Type

Conference Proceeding Article

Publication Date

5-2021

Abstract

Advances in artificial intelligence (AI) have made it increasingly applicable to supplement expert’s decision-making in the form of a decision support system on various tasks. For instance, an AI-based system can provide therapists quantitative analysis on patient’s status to improve practices of rehabilitation assessment. However, there is limited knowledge on the potential of these systems. In this paper, we present the development and evaluation of an interactive AI-based system that supports collaborative decision making with therapists for rehabilitation assessment. This system automatically identifies salient features of assessment to generate patient-specific analysis for therapists, and tunes with their feedback. In two evaluations with therapists, we found that our system supports therapists significantly higher agreement on assessment (0.71 average F1-score) than a traditional system without analysis (0.66 average F1-score, p

Keywords

Decision support systems, Explainable and interactive machine learning, Human-ai interaction/collaboration, Personalization, Stroke rehabilitation assessment

Discipline

Artificial Intelligence and Robotics

Research Areas

Data Science and Engineering

Publication

Proceedings of the ACM Conference on Human Factors in Computing Systems. CHI ’21

First Page

1

Last Page

14

ISBN

9781450380966

Identifier

10.1145/3411764.3445472

Publisher

Association for Computing Machinery

City or Country

Virtual, Online

Additional URL

http://doi.org/10.1145/3411764.3445472

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