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AI-Driven Decision Support Systems for Early Breast Cancer Detection: Adoption Implications in Healthcare Contexts

Tsoupras, Georgios LU and Syed, Zayn Ali (2025) INFM10 20251
Department of Informatics
Abstract
This thesis investigates the adoption implications of AI-driven Clinical Decision Support Systems (AI-CDSS) for early breast cancer detection within healthcare workflows. Despite advancements in AI technology and its demonstrated potential to enhance diagnostic accuracy and efficiency, real-world adoption remains limited due to technical, organisational, and ethical challenges. Using a qualitative, interpretivist approach, the study draws on eight expert interviews from clinical, technical, and administrative domains. The findings, analysed through the lens of Socio-Technical Systems (STS) theory, reveal six key themes influencing adoption: technical interoperability, clinician trust and explainability, organisational readiness, workflow... (More)
This thesis investigates the adoption implications of AI-driven Clinical Decision Support Systems (AI-CDSS) for early breast cancer detection within healthcare workflows. Despite advancements in AI technology and its demonstrated potential to enhance diagnostic accuracy and efficiency, real-world adoption remains limited due to technical, organisational, and ethical challenges. Using a qualitative, interpretivist approach, the study draws on eight expert interviews from clinical, technical, and administrative domains. The findings, analysed through the lens of Socio-Technical Systems (STS) theory, reveal six key themes influencing adoption: technical interoperability, clinician trust and explainability, organisational readiness, workflow impact, ethical and legal concerns, and the positioning of AI as a collaborative tool. The study highlights that successful adoption of AI-CDSS demands not only robust technical performance but also trust-building, ongoing training, clear accountability policies, and ethical governance. It concludes with practical recommendations for aligning AI tools with clinical workflows, fostering stakeholder collaboration, and ensuring transparency in decision-making processes (Less)
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author
Tsoupras, Georgios LU and Syed, Zayn Ali
supervisor
organization
course
INFM10 20251
year
type
H1 - Master's Degree (One Year)
subject
keywords
Artificial Intelligence (AI), Clinical Decision Support Systems (CDSS), Breast Cancer Detection, Socio-Technical Systems (STS), Healthcare Technology Adoption, Explainable AI (XAI), Organisational Readiness, Ethical AI, Workflow adoption, Trust in AI
language
English
id
9203488
date added to LUP
2025-06-19 21:40:19
date last changed
2025-06-19 21:40:19
@misc{9203488,
  abstract     = {{This thesis investigates the adoption implications of AI-driven Clinical Decision Support Systems (AI-CDSS) for early breast cancer detection within healthcare workflows. Despite advancements in AI technology and its demonstrated potential to enhance diagnostic accuracy and efficiency, real-world adoption remains limited due to technical, organisational, and ethical challenges. Using a qualitative, interpretivist approach, the study draws on eight expert interviews from clinical, technical, and administrative domains. The findings, analysed through the lens of Socio-Technical Systems (STS) theory, reveal six key themes influencing adoption: technical interoperability, clinician trust and explainability, organisational readiness, workflow impact, ethical and legal concerns, and the positioning of AI as a collaborative tool. The study highlights that successful adoption of AI-CDSS demands not only robust technical performance but also trust-building, ongoing training, clear accountability policies, and ethical governance. It concludes with practical recommendations for aligning AI tools with clinical workflows, fostering stakeholder collaboration, and ensuring transparency in decision-making processes}},
  author       = {{Tsoupras, Georgios and Syed, Zayn Ali}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{AI-Driven Decision Support Systems for Early Breast Cancer Detection: Adoption Implications in Healthcare Contexts}},
  year         = {{2025}},
}