Trust in Clinical AI

Trust in Clinical AI: Expanding the Unit of Analysis

Browne, J.T. (TU Delft Methodologie en Organisatie van Design; Philips Research)
Bakker, Saskia (Philips Research)
Yu, Bin (Philips Research)
Lloyd, P.A. (TU Delft Methodologie en Organisatie van Design)
Ben Allouch, Somaya (Hogeschool van Amsterdam; Universiteit van Amsterdam)

Schlobach, Stefan (editor)
Perez-Ortiz, Maria (editor)
Tielman, Myrthe (editor)

2022

From diagnosis to patient scheduling, AI is increasingly being considered across different clinical applications. Despite increasingly powerful clinical AI, uptake into actual clinical workflows remains limited. One of the major challenges is developing appropriate trust with clinicians. In this paper, we investigate trust in clinical AI in a wider perspective beyond user interactions with the AI. We offer several points in the clinical AI development, usage, and monitoring process that can have a significant impact on trust. We argue that the calibration of trust in AI should go beyond explainable AI and focus on the entire process of clinical AI deployment. We illustrate our argument with case studies from practitioners implementing clinical AI in practice to show how trust can be affected by different stages in the deployment cycle.

Artificial Intelligence
Clinical AI
Trust
Trust Calibration

http://resolver.tudelft.nl/uuid:46dc9fe4-6192-49fb-b094-c835067bc7e4

https://doi.org/10.3233/FAIA220192

IOS Press

9781643683089

HHAI2022: Augmenting Human Intellect - Proceedings of the 1st International Conference on Hybrid Human-Artificial Intelligence

1st International Conference on Hybrid Human-Artificial Intelligence, HHAI 2022, 2022-06-13 → 2022-06-17, Amsterdam, Netherlands

Frontiers in Artificial Intelligence and Applications, 0922-6389, 354

Institutional Repository

conference paper

© 2022 J.T. Browne, Saskia Bakker, Bin Yu, P.A. Lloyd, Somaya Ben Allouch