The Application of Artificial Intelligence in Healthcare-Resource Allocation Before and During the COVID Pandemic: Scoping Review (Preprint)

Abstract

Imbalanced healthcare-resource distribution has been central in unequal health outcomes and political tension around the world. Artificial intelligence (AI) has emerged as a promising tool for facilitating resource distribution, especially during emergencies. However, no comprehensive review exists on the use and ethics of AI in healthcare-resource distribution.

This study aims to conduct a systematic scoping review of the application of AI in healthcare-resource distribution and to explore the ethical and political issues in such situations.

A scoping review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews. A systematic and comprehensive search of relevant literature was conducted in Medline (Ovid), PubMed, Web of Science, and Embase from inception to February 2022. The review included qualitative and quantitative studies investigating the application of AI in healthcare-resource allocation.

The review involved 22 articles, including 9 on model development and 13 on theoretical discussion, qualitative studies, or review studies. Of the 9 on model development and validation, 5 were conducted in emerging economies, 3 in developed countries, and one study in a global context. In terms of content, 4 focused on resource distribution at the health-system level and 5 on resource allocation at the hospital level. Of the 13 qualitative studies, 8 were discussions on the COVID pandemic and the rest on hospital resources, outbreaks, screening, human resources, and digitalisation.

This scoping review synthesised evidence on AI in health resource distribution, focusing on the COVID pandemic. The results suggest that the application of AI has potential to improve efficacy in resource distribution, especially during emergencies. Efficient data sharing and collecting structures are needed to make reliable and evidence-based decisions. Health inequality, distributive justice, and transparency must be considered when deploying AI models in real-world situations.