An Architecture to Support Graduated Levels of Trust for Cancer Diagnosis with AI

Olya Rezaeian, Alparslan Emrah Bayrak, Onur Asan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Our research addresses the critical challenge of building trust in Artificial Intelligence (AI) for Clinical Decision Support Systems (CDSS), focusing on breast cancer diagnosis. It is difficult for clinicians to trust AI-generated recommendations due to a lack of explanations by the AI especially when diagnosing life threatening diseases such as breast cancer. To tackle this, we propose a dual-stage AI model combining U-Net architecture for image segmentation and Convolutional Neural Networks (CNN) for cancer prediction. This model operates on breast cancer tissue images and introduces four levels of explainability: basic classification, probability distribution, tumor localization, and advanced tumor localization with varying confidence levels. These levels are designed to offer increasing detail about diagnostic suggestions, aiming to study the effect of different explanation types on clinicians’ trust in the AI system. Our methodology encompasses the development of explanation mechanisms and their application in experimental settings to evaluate their impact on enhancing clinician trust in AI. This initiative seeks to bridge the gap between AI capabilities and clinician acceptance by improving the transparency and usefulness of AI in healthcare. Ultimately, our work aims to contribute to better patient outcomes and increased efficiency in healthcare delivery by facilitating the integration of explainable AI into clinical practice.

Original languageEnglish
Title of host publicationHCI International 2024 Posters - 26th International Conference on Human-Computer Interaction, HCII 2024, Proceedings
EditorsConstantine Stephanidis, Margherita Antona, Stavroula Ntoa, Gavriel Salvendy
Pages344-351
Number of pages8
DOIs
StatePublished - 2024
Event26th International Conference on Human-Computer Interaction, HCII 2024 - Washington, United States
Duration: 29 Jun 20244 Jul 2024

Publication series

NameCommunications in Computer and Information Science
Volume2119 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference26th International Conference on Human-Computer Interaction, HCII 2024
Country/TerritoryUnited States
CityWashington
Period29/06/244/07/24

Keywords

  • Artificial Intelligence (AI)
  • Breast Cancer
  • Clinical Decision Support Systems (CDSS)
  • Explainablity
  • Trust

Fingerprint

Dive into the research topics of 'An Architecture to Support Graduated Levels of Trust for Cancer Diagnosis with AI'. Together they form a unique fingerprint.

Cite this