AI chatbots for healthcare maintenance: transforming total productive maintenance in the Industry 5.0 era

Abstract

Purpose This paper introduces MedMaintBot, an AI chatbot designed to support biomedical technicians and non-expert users like nurses. The study explores the impact of integrating such an AI chatbot into Total Productive Maintenance (TPM) practices in healthcare, aligned with Industry 5.0 (I5.0) principles. Design/methodology/approach This study adopts a multi-phase methodology, starting with a literature review on technology integration in TPM within healthcare settings. It presents the chatbot development pipeline and conducts a large-scale validation study across 250 queries covering five medical devices (MDs) to demonstrate the chatbot's real-time, context-aware guidance capabilities. Performance analysis further evaluates MedMaintBot's potential to optimize TPM practices and support sustainability goals in healthcare maintenance. Findings The study reveals that MedMaintBot enhances TPM within healthcare by delivering accurate, context-aware guidance (Accuracy = 0.713, Relevance = 0.810), supporting nurse autonomy in routine maintenance and reducing technician dependency. While clarity and completeness were slightly below ideal for complex tasks, over 80% of autonomy-related queries were validated, showing strong support for first-level interventions. Combined with dynamic Large Language Model (LLM) switching between GPT-4 and MedLLaMA2, MedMaintBot strikes a balance between performance, cost and privacy, positioning it as a scalable and sustainable tool for healthcare maintenance. Research limitations/implications This research provides valuable insights for practitioners and researchers on enhancing autonomous maintenance (AM) through AI–chatbot integration, offering a scalable framework for integrating AI into TPM practices. It also encourages further studies to address gaps in procedural completeness and contextual continuity and assess scalability across diverse maintenance environments. Practical implications By providing real-time, context-aware guidance, the chatbot helps reduce user-induced errors, allowing non-expert users, such as nurses, to perform maintenance tasks. This not only reduces the burden on specialized technicians but also ensures better equipment availability, contributing to more streamlined healthcare operations and improved patient care. Social implications MedMaintBot promotes a more inclusive and resilient healthcare environment by empowering non-expert users with AI-driven support. Its adaptability aligns with the human-centric principles of Industry 5.0, fostering collaboration between technology and healthcare personnel. Originality/value This research is among the first to examine the integration of innovative AI chatbot with TPM practices within the healthcare sector, particularly in the context of I5.0. It demonstrates how such a system can significantly enhance operational efficiency, empower non-expert users and support sustainability in healthcare, offering a roadmap extending AI-assisted maintenance to broader industrial and resource-constrained environments.

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