This exploratory research investigates the optimization of artificial intelligence (AI) network resource allocation within healthcare contexts by employing methods that are motivated by amounts. In light of the ever-increasing complexity of healthcare data and the growing demand for efficient deployment of computer resources, it is possible that existing methods will abruptly fail to meet the requirements. This study intends to devise new techniques to effectively allocate resources within artificial intelligence networks that have been adapted for healthcare operations. These methodologies will be derived from the perceptivity of amount-inspired computing. One of the goals of this investigation is to improve the scalability, speed, and delicacy of AI-driven healthcare systems. This will be accomplished by incorporating principles inspired from amount computing, such as superposition and trap, into resource allocation algorithms. This paper is to provide insight into how quantum-inspired methods can be used to revise resource allocation processes in healthcare AI networks. © 2025 Elsevier B.V., All rights reserved.