AI in Medical Devices: Trends and Power Supply Considerations

Artificial intelligence and machine learning (AI/ML) are changing the medical device landscape with promises of more effective treatments, improved patient outcomes, and faster diagnoses. However, the potential of AI medical devices also calls for a greater need for reliable power supplies to keep medical devices running safely and effectively.

In this blog, we’ll cover some emerging technologies and trends aided by AI that are impacting the medical device world. We’ll also discuss what medical device manufacturers should consider when choosing a power supply to support these emerging technologies.

Real-Time Imaging

Real-time medical imaging can use AI to analyze images in a few different ways. This includes image segmentation, which can identify objects within an image, as well as image analysis that can identify patterns and anomalies. Artificial intelligence can also fill in missing data through image reconstruction or provide guidance for procedures. With AI-powered real-time imaging, patients can receive earlier diagnoses with improved accuracy, often at reduced costs.

The use of real-time imaging has been applied to fluoroscopy, ultrasound, MRI, and more. However, implementing this technology can be challenging. Training AI algorithms requires massive datasets, and health care providers need to be mindful of patient privacy. It can also be difficult to gain regulatory approval with AI-enabled devices if manufacturers can’t effectively explain how AI algorithms make decisions.

Power Supply Needs for Real-Time Imaging

Powering these devices appropriately is a vital part of their effectiveness. High-performance, medical-grade power supplies are necessary for real-time imaging that doesn’t experience downtime or outages that can impede insights. These devices may also require higher wattage to effectively deliver real-time results, which introduces new complications for controlling heat, fan noise, and deciding where the burden of processing should take place.

Gaining FDA Approval for AI in Medical Devices

Currently, there’s no specific pathway for FDA approval of AI/ML devices, but most of these devices have been classed as moderate risk, or Class II. This means that manufacturers can use the 510(k) pathway and go through premarket review. However, if the device is deemed to be novel and poses a low or moderate risk, it may go through the de novo pathway. Still others that may be deemed to carry a higher risk may be classified as Class III and have to go through the premarket approval (PMA) before marketing. This involves “requiring the development of clinical evidence to support their approval.” The 510(k) pathway isn’t appropriate in all cases, since AI/ML technology can be very different from the older devices they’re trying to compare to, some of which may have a 20-year gap or more. We have seen companies go through the 510(k) pathway and de novo, and we would not be surprised if some AI-enabled devices are subject to PMA.   

A research paper published in The Lancet in 2023 recommended that the FDA focus more on the unique characteristics of AI/ML devices when approving them, and that the FDA improve the 510(k) pathway to ensure new devices that use artificial intelligence and machine learning are safe and effective. In 2025, the FDA approved most AI-powered medical devices without requiring clinical trials. The processes for these new devices will likely change the regulatory landscape in the years to come.

Trends in FDA Approval For AI/Machine Learning in Medical Devices

Currently, medical devices that have received FDA approval and contain AI/ML technology tend to share similar functions and serve similar purposes. Radiology is one of the biggest fields for FDA-approved devices that incorporate AI/ML, with more than 75% of approved devices belonging to this category as of June 2026.

This includes the Brainomix 360 Triage ICH for stroke detection, eMurmur Heart AI for heart murmur detection, Neosoma Brain Mets for brain tumor analysis, and INFINITT DPS for digital pathology. However, the complexity of the approved devices varies, with some simpler and others applying deep learning models. Some of the most popular applications include remote monitoring devices for sleep apnea or cardiovascular issues, advanced imaging, and real-time insights for endoscopies and colonoscopies.

Predictive Analytics

Predictive analytics uses AI to analyze vast amounts of data and identify patterns that may not be obvious to humans. The findings can be used to predict equipment maintenance needs or shine a light on potential patient issues.

For example, the integration of AI can be used with defibrillators to improve the accuracy of shock delivery or with continuous glucose monitors to personalize insulin therapy for diabetics. Some recent heart monitoring devices are designed for long-term use and can identify abnormal patterns that only occur intermittently. Predictive analytics can also be used to avoid major equipment breakdowns by identifying opportunities for preventative maintenance.

Generative AI

The average person may be used to encountering generative AI and large language models (LLMs) in their everyday lives or at their respective workplaces, but the use of these technologies in the medical field means that clinicians and technologists must consider HIPAA compliance and patient safety. Generative AI creates new content based on existing data, whereas LLMs process and generate human-like text. There are some promising use cases for generative AI in medical devices, such as summarizing patient visits or creating personalized care plans or even implants. However, training LLMs for medical applications requires a lot of patient data, which raises concerns about privacy and bias in AI models.

Edge Computing

Another technological area likely familiar to most is the Internet of Things (IoT), a market of devices that require real-time processing of generated data to function effectively. This includes wearable devices, such as smartwatches, as well as smart home devices like lights and locks.

The Internet of Medical Things (IoMT) market is expected to grow significantly, with devices generating large amounts of data that require real-time processing. Edge computing processes data closer to the devices, which can reduce latency and improve performance.

How Edge Computing Improves Medical Devices

Anything that transmits data to the cloud could be an example of an IoMT device, such as a wearable health tracker, smart pill, or other remote monitor. By enabling real-time data analysis, edge computing can improve the patient experience. It can also improve data security by processing data in a decentralized manner.

Edge computing is being used in medical devices with telemedicine, AI-powered diagnostics, and remote patient monitoring to achieve faster diagnoses and foster proactive interventions.

The Future of Digital Health in Medicine

Digital health is transforming how medicine is practiced, from remote monitoring to AI-assisted diagnostics. As digital health tools become more integrated into clinical workflows, the demand for reliable, compliant medical electrical equipment grows.

Stanford Medicine’s 2026 AI Index Report has identified the following trends that medical device manufacturers and healthcare entities should be on the lookout for in the near future:

• Smaller models having greater efficacy than larger ones in molecular biology

• Virtual cell models being developed that can help predict responses to drugs at the cellular level

• The need for more data to train biological models

• Increased adoption of clinical note creation using LLMs

• 258 AI devices being approved by the FDA in 2025, most of which not requiring pathways with new clinical trials

• Greater diagnostic efficacy being achieved by multi-agent AI systems

• Health-related searches receiving AI summaries 84-92% of the time on Google

• Ethics conversations being more prominent in medical journals, but still narrow in focus

• Conversations increasing around the use of medical digital twins aided by AI

Medical device manufacturers must anticipate how digital health trends will evolve. This includes preparing for stricter data security requirements, more complex AI algorithms, and the need for power supplies that can handle increased processing loads without compromising patient safety under EN 60601-1 standards.

Power Supply Considerations for AI-Enabled Medical Devices

RAM Technologies’ power supplies  are 60601-1 3.2 certified. When you’re designing your medical device and need help with a PSU, contact us  for details.