Explaining healthcare AI adoption: A decade review of the ASEAN research landscape

Abstract

Artificial intelligence (AI) is revolutionizing the world we live in. It holds significant promise for improving performance across various sectors. Researchers worldwide have been working on developing solutions that leverage this technology for application by diverse user groups. A key category of AI is healthcare AI; smart solutions designed to address the needs of various stakeholders in the sector, from medical practitioners to patients. Crucial for any AI deployment are good adoption rates from the intended users and support from key stakeholders. Therefore, understanding the factors that influence its adoption is essential. This paper aims to provide insights through a systematic literature review of research related to healthcare AI adoption. Specifically, it reports on research works from ASEAN countries published over the past decade, between 2014 and 2024, identified through a systematic search of the Lens.org database. The search string focused on terms such as “Artificial Intelligence,” “Medical,” “Healthcare,” “Explainable,” “Transparency,” “Trust,” and “Adoption.” The motivation for this search stems from developments in Explainable Artificial Intelligence (XAI). XAI is vital for fostering trust and facilitating the widespread adoption of AI-driven clinical decision support systems (CDSS) in healthcare. While AI has the potential to augment clinical decision-making, the lack of transparency in traditional “black box” models can undermine trust and hinder adoption. XAI techniques aim to make AI systems more interpretable by providing explanations and visualizations of the decision-making process. This transparency is essential for overcoming skepticism and encouraging the adoption of CDSS in clinical practice. The findings of this review will offer insights into the current research landscape in the region and inform future research on the adoption and use of healthcare AI. These issues are particularly relevant in the ASEAN context, where varying levels of digital literacy and healthcare infrastructure necessitate explainable and transparent AI to build user trust and support effective integration.