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Examining Physicians’ Explanatory Reasoning in Re-Diagnosis Scenarios for Improving AI Diagnostic Systems

Examining Physicians’ Explanatory Reasoning in Re-Diagnosis Scenarios for Improving AI Diagnostic... AI systems are increasingly being developed to provide the first point of contact for patients. These systems are typically focused on question-answering and integrating chat systems with diagnostic algorithms, but are likely to suffer from many of the same deficiencies in explanation that have plagued medical diagnostic systems since the 1970s (Shortliffe, 1979). To provide better guidance about how such systems should approach explanations, we report on an interview study in which we identified explanations that physicians used in the context of re-diagnosis or a change in diagnosis. Seven current and former physicians with a variety of specialties and experience were recruited to take part in the interviews. Several high-level observations were made by reviewing the interview notes. Nine broad categories of explanation emerged from the thematic analysis of the explanation contents. We also present these in a diagnosis meta-timeline that encapsulates many of the commonalities we saw across diagnoses during the interviews. Based on the results, we provided some design recommendations to consider for developing diagnostic AI systems. Altogether, this study suggests explanation strategies, approaches, and methods that might be used by medical diagnostic AI systems to improve user trust and satisfaction with these systems. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Cognitive Engineering and Decision Making SAGE

Examining Physicians’ Explanatory Reasoning in Re-Diagnosis Scenarios for Improving AI Diagnostic Systems

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References (43)

Publisher
SAGE
Copyright
Copyright © 2022, Human Factors and Ergonomics Society
ISSN
1555-3434
eISSN
2169-5032
DOI
10.1177/15553434221085114
Publisher site
See Article on Publisher Site

Abstract

AI systems are increasingly being developed to provide the first point of contact for patients. These systems are typically focused on question-answering and integrating chat systems with diagnostic algorithms, but are likely to suffer from many of the same deficiencies in explanation that have plagued medical diagnostic systems since the 1970s (Shortliffe, 1979). To provide better guidance about how such systems should approach explanations, we report on an interview study in which we identified explanations that physicians used in the context of re-diagnosis or a change in diagnosis. Seven current and former physicians with a variety of specialties and experience were recruited to take part in the interviews. Several high-level observations were made by reviewing the interview notes. Nine broad categories of explanation emerged from the thematic analysis of the explanation contents. We also present these in a diagnosis meta-timeline that encapsulates many of the commonalities we saw across diagnoses during the interviews. Based on the results, we provided some design recommendations to consider for developing diagnostic AI systems. Altogether, this study suggests explanation strategies, approaches, and methods that might be used by medical diagnostic AI systems to improve user trust and satisfaction with these systems.

Journal

Journal of Cognitive Engineering and Decision MakingSAGE

Published: Jun 1, 2022

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