Artificial Intelligence in Medical Devices: Everything You Need to Know

Measuring the impact of the FDA’s Regulation Proposal

 

With recent developments in technology, we have seen artificial intelligence and machine learning more commonly used across all aspects of our lives. Whether it’s our phones or smart speakers, this dynamic technology has applications in all industries, including the medical device industry. To ensure AI in medical devices can evolve while still protecting patient well-being and safety, the FDA recently released new information about its plan to regulate developments.


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Artificial Intelligence vs. Machine Learning

 

Artificial intelligence and machine learning are two similar terms people commonly use interchangeably when discussing this type of software or technology, but they actually mean different things.

 

Artificial intelligence refers to any kind of technology deemed to be “intelligent,” or capable of understanding new data and adapting its functions accordingly. Machine learning refers — not to the device or software itself — but rather to the method scientists use to “teach” software to adapt to new information through learning and updating stages. Usually, machine learning is applied in the form of decision trees, where a certain action will always lead to a certain response and each action after follows a set path.

 

Researchers are now using machine learning to improve the efficiency of AI in medical devices. Practitioners often use different kinds of software to help them examine and treat patients, and AI as a part of medical device software is growing a little more every day. Machine learning allows AI devices to monitor patients, improve medical imaging software and even the potential to deliver certain treatments to patients.

 

However, AI doesn’t learn the same way humans do. AI software and algorithms are taught to adapt based on statistics and patterns they gather from experience. This capability means AI medical devices have the potential to develop very quickly as they receive and analyze risk management data in real-time and in real-life applications.

 

FDA Concerns About Artificial Intelligence in Medical Devices

artificial intelligence

 

There are two main types of technological algorithms:

  • Locked algorithms: only understand information and analyze it based on the way they were programmed. If you ask a locked algorithm a question or input certain data, it will always offer the same solution or result.
  • Adaptive algorithms: can respond and recognize new patterns or opportunities as it receives new data, and adjust it’s process or response accordingly.

Adaptive algorithms in medicine are currently causing the FDA the most concern. When companies that create software or medical devices give AI-based programs or products the freedom to examine or help treat patients by allowing them to respond to real-world situations and data, they have less control over what the AI learns and how it uses that information to adapt. If left unchecked or under-regulated, this could pose the possibility of changes developing that could create patient risk.

 

The FDA is currently working to develop a framework that will allow for the safe use of ever-adapting AI in the hospital setting. To make sure patients stay protected, FDA representatives have indicated that if an AI medical device is approved, it would have to be monitored constantly for changes in its algorithm. Making sure all the information the AI absorbs and analyzes stays transparent and malleable is crucial for maintaining a secure healthcare practice.

 

Challenges of Implementing AI Medical Devices

 

Another concern that artificial intelligence medical devices FDA regulations will address is helping AI respond better to individual patients. Unfortunately, there is no one-size-fits-all approach. Some illnesses or conditions may affect people differently.

 

This means some situations might confuse an AI machine or cause it to not analyze things properly. Researchers and the FDA must account for such gray areas and variables that could cause an AI device to malfunction or compromise the safety of patients. Programming an AI algorithm to understand cause and effect or to strategize using a decision tree can’t account for all potential factors.

 

The Future of AI in Medical Devices

 

The FDA is working to develop appropriate regulations for the use of Software as a Medical Device. Products that are driven by AI software or processes fall under this category, but given their unique ability to adapt to new data, they likely require separate scrutiny. As AI continues to evolve in the coming years, the FDA will have to keep a close watch on its applications and uses in the medical field to ensure patient protection.

 

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