AHFE Open AccessPreliminary Survey on Trust Levels in AI-Clinical Decision Support Systems Among Medical Professionals
Abstract
Artificial Intelligence-based Clinical Decision Support Systems (AI-CDSS) have the potential to enhance clinical decision-making. However, trust remains a critical challenge influencing their adoption, and the specific direction of trust among medical professionals remains unclear. This study aims to provide empirical evidence on current trust levels in AI-CDSS among medical professionals. A revised version of questionnaire measuring trust in automation was utilized, employing a five-point Likert scale. A total of 29 Thai medical professionals, including both junior and senior practitioners, participated in this study. The findings reveal a spectrum of trust levels, with an average trust score of 3.05 (SD = 0.44). The majority of participants exhibited moderate trust; however, there were tendencies of undertrust and overtrust toward AI-CDSS in 10.34% and 27.59% of participants, respectively. Concerns regarding the capability, reliability, and transparency of AI-CDSS were identified as key barriers to trust. These findings provide valuable insights into trust perceptions, contributing to the development of more trustworthy AI-CDSS solutions and informing strategies for their effective integration into clinical practice.
Keywords: Trust, Artificial Intelligence (AI), Clinical Decision Support System (CDSS), Healthcare, Adoption
DOI: 10.54941/ahfe1006213
Cite this paper
More from this volume
- Data-Driven Insights into Diabetes-Related Hospital Readmissions in the United States: Trends and Predictors
- A Sliding-Window Batched Framework: Optimizing Retrieval-Augmented Generation (RAG) for Trustworthy AI under the EU AI Act
- A Method of Structured Standard Terminology Based on Decoupling Approach
- Convo-Based Attitude Analysis of Twitter Big Data: A Case Study on Ukraine-Russia War Dataset
- Smart Cities: are they really accessible and truly smart?
- AI Optimization of Resolution Strategy in Utility Billing and Revenue Assurance
- Behavioural Intentions of Natural Farming Farmers to Adopt Digital Platforms for Purchasing Inputs: A Structural Equation Modeling-Based Multi-Group Analysis
- AIToys: A conceptual definition and future research agenda
- FITMag: A Framework for Generating Fashion Journalism Using Multimodal LLMs, Social Media Influence, and Graph RAG
- Challenges and Opportunities in E-commerce Distribution Networks in Johannesburg.
- Revolutionizing Logistics Management with Blockchain Technology
- Interpretable AI-Generated Videos Detection using Deep Learning and Integrated Gradients