AI chatbots for healthcare maintenance: transforming total productive maintenance in the Industry 5.0 era
Bibliographic record
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".