Artificial intelligence in pathology services : a policy approach to enhance diagnostic efficiency at Mater Dei Hospital

Abstract

The integration of Artificial Intelligence (AI) within Pathology Services presents opportunities to improve diagnostic efficiency, accuracy and service delivery in public healthcare systems. This study explores the adoption of AI in the Pathology Department at Mater Dei Hospital through a policy-oriented perspective, focusing on how strategic public policy initiatives can support effective implementation while addressing institutional and operational challenges. Using a qualitative research design, the study employs semi-structured interviews with key stakeholders, together with document analysis of national health strategies, AI policy frameworks, and institutional guidelines. The research investigates the practical, ethical, technical, and organisational challenges encountered during the implementation of AI technologies in Pathology Services, such as data governance, workforce readiness, infrastructure limitations, regulatory uncertainty and resistance to change. It further examines existing and potential strategies to overcome these barriers, including targeted training programmes, investment in digital infrastructure, clear regulatory standards and interdisciplinary collaboration. The study analyses how public policy can be tailored to maximise the benefits of AI integration such as improved diagnostic turnaround times, enhanced decision support and optimised resource allocation while mitigating risks related to accountability, bias and patient safety. By harmonising institutional practices with national digital health and AI strategies, the research highlights the role of coherent policy frameworks in promoting sustainable and ethical AI adoption. The findings aim to inform policymakers and healthcare leaders by offering evidence-based recommendations to guide AI integration in Pathology Services at Mater Dei Hospital and comparable public healthcare settings, contributing to more efficient, resilient, and patient centred diagnostic services.