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

Sammanfattning

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