While smooth operations are a universally shared business goal, device functionality is a life-or-death matter when it comes to sensitive industries like healthcare. The U.S. Food and Drug Administration (FDA) reports that medical device malfunctions caused nearly 83,000 deaths¹ over a recent 10-year period.

Maintenance is increasingly relevant as tech complexity and growing demand put continuous pressure on the healthcare industry. Sedgwick’s 2025 State of the Nation Report² in the U.K. and EU on medical device safety indicates that in 2024: 

• Medical device recalls were at an all-time high, increasing by 8.6% to a record total of 1,059 
• The top five causes for medical device recalls were failure, quality, software, mislabeling, and parts issues
• At 11.1%, device failure was the leading cause of recalls, posting the highest rate in more than five years

With the power to affect patient care and outcomes, medical device maintenance is crucial to avoid the unexpected failures, downtime, and costs that come with traditional maintenance models. AI-enhanced medical device management can save the healthcare industry thousands of dollars in revenue because medical equipment failure runs over $8,000 a minute.³

Traditional Medical Device Maintenance

An MDPI journal, Applied Sciences, summarizes the main types of medical device maintenance methods.⁴ 

1. Reactive: Monitoring is not continuous, and errors are only noticed and addressed, given clear signs of malfunction. 
2. Planned: Regularly scheduled inspections occur regardless of whether a failure has been indicated. 
3. Proactive: Faults are actively removed to enhance future performance. 

However, due to limited monitoring, accuracy, scalability, inconsistent manual record keeping, and other limitations, these methods can still lead to malfunctions. Unplanned downtime seriously impacts healthcare, maintenance costs, and data utilization, as well as related costly setbacks.

Predictive maintenance is a fourth approach that has been around for more than three decades. Originally, it used early artificial intelligence (AI) algorithms and sensors, which monitored the devices to diagnose and resolve problems before they occurred. As AI evolved, it leveraged advanced data management and predictive analytics to enhance these abilities. Now, agentic AI has started taking the human element out of the decision-making process.

How Agentic AI Enhances Predictive Maintenance for Medical Devices

Unlike traditional AI, agentic AI doesn’t require predefined rules or human oversight to perform tasks. Powered by large language and multi-modal foundation models, it can act autonomously, setting goals and completing tasks through machine learning (ML), deep learning, reinforcement learning, and natural learning processing. Statista projects⁵ agentic AI market value will exceed $47 billion by 2030, up from $5.1 billion in 2024, as it becomes more essential to business operations across industries. 

How does agentic AI take predictive maintenance a step further? The following key technologies enable AI agents’ ability to act independently:

• Integrating Sensors and IoT: Agentic AI continuously monitors device performance, sensing issues in real-time to recommend maintenance strategies accordingly. 
• Predictive Analytics and Self-Learning: Agentic AI analyzes large datasets to identify failures and recommend proactive repair strategies, continuously drawing information from its environment with the help of ML. 
• Machine-Human Communication: Agents harness user-friendly UX/UI design and dashboards to make data-driven failure insights easily understandable for humans. 
• Advanced Simulation and Computing Technologies: Agents leverage digital twin technology, augmented reality, virtual reality, and edge/cloud computing to simulate device behavior, enabling more efficient maintenance and greater precision. 
• Cross-System Collaboration: Agents share insights across networks of medical devices, aligning maintenance strategies across healthcare systems for streamlined efficiency organization-wide. 
• Automated Decision-Making and Optimization: Agentic AI minimizes manual intervention by autonomously making, without human involvement, maintenance decisions, freeing up personnel to focus on providing better healthcare. 

Benefits of Agentic AI in Predictive Maintenance for Medical Devices

93% of healthcare professionals⁶ believe AI agents have positively impacted the industry. With automated system control, agentic AI offers immediate awareness of device failures as well as data-driven insight into repair strategies. A powerhouse of autonomous early problem detection, it reduces failure rates to minimize downtime and promotes patient safety, operational efficiency, and cost optimization. 

The National Institute of Standards and Technology reports that adopting advanced predictive maintenance methods can provide cost savings as high as 98%,⁷ making agentic AI integration an even more valuable strategy. Furthermore, agentic AI can automate data-driven maintenance logs to support compliance with medical regulations set by the FDA, keeping procedures secure. 

Companies can choose between third-party predictive maintenance platforms or custom software solutions. There are pros and cons to both approaches. 

Off-the-shelf products usually include lower upfront costs and faster deployment. In comparison, custom software solutions may have higher upfront costs but lower ongoing expenses because they don’t have monthly subscription fees. If you buy a product, it may not allow customization—which tailored software solutions offer.

A hybrid method combines the best elements of buy vs. build. Companies can partner with software solution providers to customize products to work smoothly and effectively with their medical equipment and devices. 

Regardless of how healthcare companies choose, they should carefully consider the following issues: 

• Integration with Legacy Devices: Investing in IoT sensors, cloud integration, and other necessary technologies enhances digital connectivity to support effective integration. 
• Data Privacy and Security Concerns: Designing agentic AI solutions to comply with HIPAA, GDPR, and FDA regulations at every level, as well as keeping medical device data secure with strict cybersecurity measures. 
• Data Quality and Quantity: Making every effort to leverage high-quality data to ensure the best AI performance and avoid the inaccurate predictions that can come from a lack of real-world data. 
• Tech Adoption Challenges: Enacting change management and staff education strategies to help employees understand how to collaborate with AI. 

The following steps can guide businesses in successfully implementing agentic AI for predictive maintenance with medical devices: 

1. Define Implementation Objectives
2. Collect and Preprocess Data
3. Determine Issue Detection Methods
4. Train and Validate AI Models
5. Establish Evaluation Metrics
6. Continuously Monitor Performance
7. Comply with Security Regulations

There are also specific concerns relevant to any implementation of agentic AI. For one, designing controls to review agent performance is crucial, allowing for human intervention if needed. The American Council on Science and Health cited a study⁸ that found AI agent-human collaboration increased communication frequency by 45% and enhanced overall task focus and completion, showing the benefits of strategically incorporating human input into agentic AI implementation. 

Finally, organizations should decide what use cases are most relevant to their specific needs so a partnering developer can tailor the agentic AI solution accordingly. For example, AI agents can remotely track wearable health devices to support predictive maintenance for remote patient monitoring.

The Future of Agentic AI in Predictive Maintenance for Medical Devices

McKinsey emphasizes that implementing AI agents can help enterprises gain up to $4.4 trillion in value on an annual basis.⁹ In healthcare, where the maintenance of medical devices can save organizations millions of dollars in fines and patient compensation, the demand for agentic AI to enhance predictive maintenance will only grow. 

Furthermore, agentic AI is already impacting predictive maintenance for medical devices. Companies are using AI agents to monitor medical imaging devices, robotic surgery systems, remote patient monitoring systems, and other medical devices. GE Healthcare has enhanced MRI machine monitoring with advanced AI solutions to reduce downtime by 36%¹⁰ and support better patient care. 

As AI agents drive predictive maintenance for medical devices to new horizons, big data and analytics, digital twin technology, edge/cloud computing, and human-machine collaboration will pave the way for further technological innovation. Healthcare companies looking to enhance efficiency, reduce costly and dangerous equipment downtime, and provide greater care should look to agentic AI for elevated predictive maintenance solutions.

References

1. bit.ly/mposoftware06251
2. bit.ly/mposoftware06252
3. bit.ly/mposoftware06253
4. bit.ly/mposoftware06254
5. bit.ly/mposoftware06255
6. bit.ly/mposoftware06256
7. bit.ly/mposoftware06257
8. bit.ly/mposoftware06258
9. mck.co/4k5bAXC
10. bit.ly/mposoftware06259

MORE FROM THIS AUTHOR—AI, Agentic AI, Nanotech, Implants, and Microbots Combine for Precision Anti-Cancer Drug Delivery

Deepak Borole is a project manager at Chetu Inc., a global digital intelligence and software solutions provider, where he oversees general healthcare, including medical devices, remote healthcare, and specialty healthcare.