AI has gone from a buzzword to an everyday reality, and is often a shorthand for “generative AI,” customer-facing large language models like ChatGPT and Claude that people use in their day-to-day lives at work and home. The medical industry is not exempt from these advancements, either. However, the use cases in this industry extend beyond appointment summaries or chatbots. They also come with additional considerations around compliance and patient safety.
We’ll talk about how artificial intelligence is being used in medicine, the purpose of AI in medical devices, recent treatment of AI by the FDA, and what manufacturers need to consider when building an AI-enabled or integrated device. We’ll also talk about why IEC 60601-1 3.2 compliance, and any subsequent revised regulatory guidelines, remain critical for medical devices.
How is Artificial Intelligence used in medicine?
Artificial intelligence, while still in its evolving stages, is being used in the medical field in many ways. We are likely to see these uses continue to grow and change as AI develops, but one of the places you can go to learn more about the latest uses (or potential paths) is the New England Journal of Medicine. At the time of publishing, some of the latest articles covered autonomous prescription renewals, the efficacy of AI diagnoses versus human expertise, and whether AI will be able to respond with “I don’t know,” the way a clinician would when faced with a knowledge gap or complex health scenario. Journals like these will also cover the regulatory pathways that manufacturers and developers will have to take to gain approval for new AI-enabled devices and software.
FDA Regulations and Clinical Decision Support Software (CDS)
One of the common uses of artificial intelligence is CDS software. While it has been used in different contexts, according to the FDA, CDS includes “computerized alerts and reminders for providers and patients; clinical guidelines; condition-specific order sets; focused patient data reports and summaries; documentation templates; diagnostic support; and contextually relevant reference information.”
On January 6, 2026, the FDA provided its final guidance on clinical decision support (CDS) software, which helps draw a line between CDS that is not a medical device (“Non-Device CDS”) and CDS that is regulated as a device by exempting specific low-risk software from medical device regulation that qualify under four statutory criteria.
If this CDS software is deemed a device, it needs to meet all FDA regulations for medical devices. The organization has created a helpful graphic for manufacturers and developers to determine whether the software they are using can be treated as a medical device.
Four Categories of Device-Level CDS Software
To be a device, the CDS software needs to fall into one of four categories:
- Acquisition, processing, and analysis of medical patterns, images, or signals
- Analysis, printing, or display of medical information, such as waveforms or continuous signals/patterns
- The software provides information such as the probability of a condition, risk scores, or outputs that are time-critical
- Does not provide a basis for recommendations
The goal of much of the CDS software currently available is to personalize care to patients in an evidence-based manner. Software that falls under the FDA’s oversight needs to ensure compliance to be marketed and sold.
AI and Machine Learning in Medical Devices – What’s FDA-Approved?
In addition to specific guidance around CDS software, the FDA has a list of approved artificial intelligence and machine learning-enabled medical devices on its site. The agency has a total of 1,430 devices listed as of June 2026, and has made it possible to download all names and details in an Excel file. More than 3/4 of the approved devices are currently in the radiology category. The other fields where AI use is becoming more common and FDA-approved include cardiovascular, neurology, anesthesiology (mainly for sleep apnea concerns), gastroenterology/urology, and hematology.
Internet of Things (IoT) Devices and Chronic Disease Management
Internet of Things (IoT) devices are often associated with smart homes – bulbs, locks, and fitness trackers are a few examples. IoT medical devices are interrelated wireless devices that are connected in a way that allows medical professionals to monitor their patients, keep them in the loop, manage their data, and cut down on resources used to treat, diagnose, and work with a patient. This constant interaction afforded by IoT devices can help improve patient outcomes.
One major struggle for patients living with chronic diseases is that their conditions are long-term, but appointments can be so short and far between. Often, patients are tasked with self-monitoring and reporting, which can be hard to maintain or unreliable. Sleep devices like Nox Medical’s DeepRESP use AI to analyze sleep and respiratory data, supporting a diagnosis for sleep apnea or other neurological conditions, while patients sleep with the devices at home. Medtronic’s LINQ II™ insertable cardiac monitor allows for long-term cardiac monitoring supported by AI algorithms that can help identify infrequent signals.
AI in Radiology
Advanced imaging in radiology and other fields can help doctors spot items that might be borderline or help highlight where they should look closer at areas of concern. Medical imaging with artificial intelligence can improve diagnostics, pairing large sets of clinical data with the imaging experience. Philips Lumify offers ultrasound technology with real-time insights, helping medical professionals make faster decisions in critical moments.
Medical devices can also be assisted by standalone software technologies, like ARTAssistant by Manteia Technologies, which adds a layer of artificial intelligence, advanced algorithms, and big data to adaptive radiotherapy practices.
AI in Gastroenterology
Early detection and treatment are vital for gastroenterological issues. Iterative Health is one company that is integrating AI into its medical devices. Driven by precision medicine, Iterative Health is bringing AI-driven tools to gastroenterology, helping physicians detect, localize, and segment better when conducting endoscopies. Magentiq Eye has developed technology that offers AI-aided colonoscopies, improving the efficacy of detecting polyps with deep learning.
The Core Goals of AI in Medical Devices
The goals of AI in medical devices are expanding as quickly as the technology is evolving, but at the core, these tools can automate repetitive tasks, process and analyze data faster than humans, improve the ability to predict disease risks and other outcomes at potentially earlier stages, and evaluate and monitor patients more effectively with real-time remote monitoring support. The future of AI will likely continue to lean on these core goals.
What AI medical device manufacturers need to know about certified parts
Medical device manufacturers using AI to improve the patient and practitioner experience will continue to grow and develop the field in ways we only see in glimpses. These new devices coming on the market will need to show they meet several compliance requirements, including IEC 60601-1, the standard for medical electrical equipment.
Power Supply Considerations for AI Medical Devices
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The Future of Machine Learning Regulations in Healthcare
As artificial intelligence and machine learning continue to evolve, healthcare regulations will need to adapt. The FDA has acknowledged that traditional approval pathways may not fit changing ML algorithms. Future regulations may include:
- Continuous monitoring of algorithm performance post-market
- Requirements for real-world evidence collection
- Updated IEC 60601 standards for AI-driven medical electrical equipment
Medical device manufacturers should monitor these regulatory developments closely to ensure future compliance.
More Resources:
Downloadable Guide to IEC 60601-1
U.S. Medical Device Market Size – Should You Seek FDA Approval?
Europe’s Medical Device Market Size – Should You Sell in the EU?
Global Medical Device Industry Statistics