Risk of AI in Healthcare: A Comprehensive Literature Review and Study Framework

TL;DR

This paper reviews AI risks in healthcare by analyzing 39 articles, categorizing risks into clinical data, technical, and socio-ethical genres, to support future research and risk mitigation strategies.

Contribution

It provides a comprehensive categorization of AI risks in healthcare, aiding future empirical and qualitative research efforts.

Findings

Identified three main genres of AI risks: clinical data, technical, socio-ethical.

Analyzed 39 articles to classify and understand AI risks in healthcare.

Offers a resource for developing evidence-based risk mitigation strategies.

Abstract

This study conducts a thorough examination of the research stream focusing on AI risks in healthcare, aiming to explore the distinct genres within this domain. A selection criterion was employed to carefully analyze 39 articles to identify three primary genres of AI risks prevalent in healthcare: clinical data risks, technical risks, and socio-ethical risks. Selection criteria was based on journal ranking and impact factor. The research seeks to provide a valuable resource for future healthcare researchers, furnishing them with a comprehensive understanding of the complex challenges posed by AI implementation in healthcare settings. By categorizing and elucidating these genres, the study aims to facilitate the development of empirical qualitative and quantitative research, fostering evidence-based approaches to address AI-related risks in healthcare effectively. This endeavor contributes to building a robust knowledge base that can inform the formulation of risk mitigation strategies, ensuring safe and efficient integration of AI technologies in healthcare practices. Thus, it is important to study AI risks in healthcare to build better and efficient AI systems and mitigate risks.