The medical device remains a crucial component in improving the quality of life. Key players in the medical technology arena are going on the AI track to invent cutting-edge devices with high precision and automation. Expectations are high as the future of healthcare delivery is poised for steady growth with AI onboard. 

Picture a smart sensor device that estimates the possibility of a heart attack or an imaging system that uses algorithms to spot a brain tumor – these are real-world evidence of AI medical technologies in action. Application design teams harmonizing AI technologies into medical devices made these realities. A medical device is any device created for medical use. Its use transverses the prevention, diagnosis, or treatment of specific medical conditions.

From the common stethoscope to the advanced cardiac pacemaker, about 2 million different medical devices are classified into over 7,000 generic groups depending on their uses. And for people living with either acute or chronic disease conditions, medical intervention in the form of a medical device can be the ultimate lifesaver. For example, heart disease is the leading cause of death worldwide, with about 3 million people worldwide living with an implantable pacemaker. As heart disease is becoming more prominent even among low and middle-income nations, many people will need to get one to improve their cardiac function. 

With – a high consumer preference for wearable devices, an increasing geriatric population, and a high number of patients in need of implantable technologies, demand for medical devices is expected to soar. As a result, the global medical device industry is projected to grow from its current market value of US$ 455 billion to US$ 657 billion by the year 2028, according to reports by Fortune Business Insights. Device failure is a major stumbling block to the growth of disruptive medical technologies. And by integrating AI into the device development process, failure rates can be reduced to a minimum. 


A medical device must have an intended medical use and be able to execute it before called one. Understanding how a medical device is classified is important because it determines its development process.  The FDA classifies devices based on the risk posed on consumer health. 

Class 1

Medical devices belonging to this category pose the lowest threat to consumers. Examples include surgical tools, dental floss, and oxygen masks. Such devices are subject to general control. 

Class 2

Class 2 devices are subject to general controls together with special controls because they pose more threats to consumers than class 1 devices. Special control entails that the device meets specific – testing requirements, performance standards, and labeling requirements. Examples are infusion pumps, powered wheelchairs, and X-ray machines.

Class 3

Devices under this category are essential to sustaining human life, and hence they are subject to premarket approval and general control. Examples are breast implants, blood bank software, pacemakers, and life support machines. 


The process of developing a medical device is not for the faint-hearted. It begins with the device discovery and ideation stage, where medical researchers spot an unmet medical need. They then create an idea to birth the new device. This is followed by creating a document called ‘proof of concept’ to determine if the concept will fly or not. Medical researchers, alongside biomedical engineers, proceed to build a prototype version that is not for human use. The prototype is tested and refined under controlled laboratory conditions. Once it can show fewer potential risks, it has passed the preclinical research–prototype phase. 

The third stage is the ‘pathway to the approval stage.’ Remember the classification of devices? It plays a major role in this stage. The device is properly assigned to one of the three regulatory classes based on the risk it poses. The greater the risk to consumer health, the higher the classification, the stricter the regulatory control the device is subject to. 

For each medical device class, the regulatory controls include two assessments. First, the substantial equivalence to show that the device is safe and effective in comparison with a legally marketed medical device not subject to premarket approval. Second, enough scientific evidence that the health benefits of using such a device far outweigh the risks. And that the device will improve the quality of life of a large number of a target population. The regulatory team fact-checks all information and decides whether to approve it or not. Device approval is followed by post-market device safety monitoring to check the emergence of new safety concerns. 


Despite passing regulatory processes, medical devices still fail due to regulatory loopholes and lax oversight in the design process. In the last decade, medical device failure has led to over 80,000 deaths, 2 million injuries, and billions of dollars in lawsuits. But here’s how AI can help. 

Reduced Failure Rates

Incorporating AI systems into the development process of a medical device can predict its performance and failure rate before it gets to the market. This is achievable by utilizing data sets from potential consumers and carrying out a possible scenario analysis. In addition, analyzing data and performance records of a medical device recalled due to failure can reveal underlying causes of what went wrong. Machine learning can also detect other factors interfering with the performance of such medical devices. By so doing, medical practitioners and hospitalists are better informed of interfering factors, suitable environmental conditions, and unique handling guides that will ensure optimal performance. 

Faster Device Manufacturing Time and Less Cost

Under normal conditions, a medical device can take 3 to 7 years to reach the market. This cost medical device companies an average sum of US$ 31 million or US$ 94 million depending on the approval pathway. Machine learning can help medical researchers speed up the ideation process of identifying an unmet medical need and suggesting designs that will scale through. This will reduce waiting times and costs. Patients in dire need of such a device can have it in good time before their medical condition becomes terminal.


To upscale the precision of diagnostic medical devices and reduce malfunction rates, biomedical engineers are now incorporating clear-cut AI technologies into them. The availability of data and big data analytics in healthcare is easing the process. The greatest benefit of embedding AI software in a medical device is the ability to leverage real-world data to improve its performance while enhancing consumer health. RWDs in the form of wearable devices, the Internet of medical things, and medical tricorder are fast becoming the consumer’s first point of access to healthcare. Pratik Agrawal, Director, Data Science and Informatics Innovation at Medtronic, described these technologies as “empowering patients to take care of themselves.”  Wearable devices can save and transmit data about a patient’s health status to health care practitioners thus, reducing hospital wait times. They can also spot underlying medical conditions real quick by analyzing any deviation in standard vital signs.  

As AI-powered medical devices collect real-time data, there is enough data to – monitor post-market safety and any adverse event that can affect regulatory decisions. This is possible via RWD and predictive analysis, which spots maintenance issues in medical devices before breakdowns or catastrophic accidents. 


The biggest players in the medical device industry are using AI and machine learning to create brilliant solutions for better health outcomes. Philips uses precision diagnostics, powerful imaging technologies, workflow informatics, and longitudinal data with insights from AI to diagnose and treat oncology patients. Not keeping itself confined to cancer care, Philips signed a merger agreement to acquire BioTelemetry. BioTelemetry focuses on AI-based data analysis, cardiac diagnostics, and wearable heart monitors. 

Medtronic decided to focus on incorporating AI into the surgical and robotics aspect of orthopedic care by acquiring French AI-enabled spinal surgery company Medicrea. Medtronic plans to take advantage of Medicrea’s UNiD ASI – a pre-surgical platform that uses predictive modeling algorithms to measure and digitally reconstruct spines. This will help orthopedic surgeons view surgical permutations and identify potential outcomes and challenges pre-surgery.

For sure, AI has earned its spot at the medical device development process table, even though it’s still a work in progress. With major roadblocks and lapses in the development framework removed, biomedical engineers and research experts can focus on novel medical technologies. This will reduce the incidence of lawsuits faced by medical device companies, and their consumers also enjoy better health outcomes.

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