Navigating diversity and inclusion in AI-driven healthcare: a stakeholdercentric approach

Abstract

Despite the potential of AI to significantly improve diagnostic accuracy, patient care, and operational efficiency in healthcare, ethical challenges such as biases inherent in AI systems can lead to unfair outcomes and exacerbate existing healthcare disparities. Drawing on insights from existing literature, this research aims to address the critical literature gap in the strategic and comprehensive integration of stakeholder collaboration with bias mitigation methodologies. Through a qualitative research design incorporating semi-structured expert interviews and literature analysis, this study searches to contribute valuable insights into the effective implementation of collaborative ethical AI development in healthcare. The result of this study presents an inclusive stakeholder collaboration framework designed to serve as a proactive guidance tool for stakeholders throughout the AI healthcare tool's lifecycle, with a focus on bias mitigation and ethical development. This framework outlines each phase of the AI lifecycle, and in each phase the algorithm's susceptibility to certain biases, bias mitigation strategies, ethical design strategies, and the responsible stakeholders are included.