Author:
Rezazade Mehrizi Mohammad H.,Mol Ferdinand,Peter Marcel,Ranschaert Erik,Dos Santos Daniel Pinto,Shahidi Ramin,Fatehi Mansoor,Dratsch Thomas
Abstract
AbstractVarious studies have shown that medical professionals are prone to follow the incorrect suggestions offered by algorithms, especially when they have limited inputs to interrogate and interpret such suggestions and when they have an attitude of relying on them. We examine the effect of correct and incorrect algorithmic suggestions on the diagnosis performance of radiologists when (1) they have no, partial, and extensive informational inputs for explaining the suggestions (study 1) and (2) they are primed to hold a positive, negative, ambivalent, or neutral attitude towards AI (study 2). Our analysis of 2760 decisions made by 92 radiologists conducting 15 mammography examinations shows that radiologists’ diagnoses follow both incorrect and correct suggestions, despite variations in the explainability inputs and attitudinal priming interventions. We identify and explain various pathways through which radiologists navigate through the decision process and arrive at correct or incorrect decisions. Overall, the findings of both studies show the limited effect of using explainability inputs and attitudinal priming for overcoming the influence of (incorrect) algorithmic suggestions.
Publisher
Springer Science and Business Media LLC
Reference44 articles.
1. Borch, C. & Hee, M. B. Toward a sociology of machine learning explainability: Human–machine interaction in deep neural network-based automated trading. Big Data Soc. 9, 20539517221111360. https://doi.org/10.1177/20539517221111361 (2022).
2. Reyes, M. et al. On the interpretability of artificial intelligence in radiology: Challenges and opportunities. Radiol. Artif. Intell. 2, e190043. https://doi.org/10.1148/ryai.2020190043 (2020).
3. Konttila, J. & Väyrynen, K. Challenges of current regulation of ai-based healthcare technology (AIHT) and potential consequences of the European AI Act proposal. (2022) https://aisel.aisnet.org/scis2022/7/ (Accessed 13 Sept 2022).
4. Alberdi, E., Povykalo, A., Strigini, L. & Ayton, P. Effects of incorrect computer-aided detection (CAD) output on human decision-making in mammography. Acad. Radiol. 11, 909–918. https://doi.org/10.1016/j.acra.2004.05.012 (2004).
5. Povyakalo, A. A., Alberdi, E., Strigini, L. & Ayton, P. Evaluating “Human+ Advisory computer” systems: A case study. In HCI2004, 18th British HCI Group Annual Conf British HCI Group, Leeds. researchgate.net, 93–96. https://www.researchgate.net/profile/Andrey-Povyakalo/publication/254291567_EVALUATING_HUMAN_ADVISORY_COMPUTER’'_SYSTEMS_A_CASE_STUDY/links/53f324f00cf256ab87b079d7/EVALUATING-HUMAN-ADVISORY-COMPUTER-SYSTEMS-A-CASE-STUDY.pdf (2004).
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