What is medical artificial intelligence?
Before diving into how AI is shaping the medical device industry, it’s important to establish a common definition for AI.
Techopedia defines AI as:
“[A] branch of computer science that aims to imbue software with the ability to analyze its environment… and then make decisions based on those analyses.”
Using this as our foundation, we define “medical artificial intelligence” as any medical technology that can collect health data, analyze it, and make/inform decisions based on the data.
It’s important to note that the field of AI is broad and that AI itself can be subdivided into many different disciplines and subcategories, which we will not cover in this post.
For further information about the high-level distinctions within the field of AI, we recommend this article from Codebots.
What devices use artificial intelligence?
As mentioned previously, AI has branched from the information technology sector into many different industries.
In 2017, the first AI-powered exchange traded fund (“ETF”) was launched, disrupting the world of finance.
Over the past few years, the e-commerce industry has been at the forefront of AI technology, using it to help consumers find products at lower prices.
But of all the areas where AI is having an impact, healthcare is one of the most important, with the market expected to surpass $50 billion by 2027.
Though AI can be found throughout the healthcare industry, we’ll highlight three areas where it is showing unique promise within the field of medical devices: diagnosis, pathogen tracking, and patient monitoring.
The ability to accurately diagnose patient problems can significantly impact patient outcomes.
Luckily, recent advances in AI technology are enabling healthcare providers to make more accurate diagnoses in less time.
Researchers recently used deep-learning technology to detect molecular changes in tumor tissue—a breakthrough that could enable less invasive, faster cancer detection.
The COVID-19 changed the way we think about healthcare.
With the emergence of concepts like “superspreaders,” the COVID-19 pandemic revealed that the world is much more complex than we could have ever imagined.
The traditional measures for virology and public health quickly proved ineffective in preparing us for the spread of a deadly virus in our hyperconnected world—a situation that forced us to innovate and build better models, quickly.
With the advances that have been made in the field of AI networking algorithms, virologists, healthcare providers, and technology engineers were able to come together to improve the accuracy of COVID-19 forecasting and mapping models.
In our blog post about remote patient monitoring systems, we discussed the different ways healthcare providers are improving patient outcomes by utilizing wireless patient monitoring technology.
By incorporating AI into remote patient monitoring systems, healthcare providers are taking treatment to a whole new level.
One company within the diabetes monitoring market recently released a device that feeds data collected from its non-invasive patch into an algorithm that can help inform treatment decisions.
What are some of the challenges associated with incorporating artificial intelligence in medical devices?
In the world of medical devices, AI is a relatively new field.
Unlike many traditional devices, which are typically developed by teams of material, mechanical, and electrical engineers, AI-based medical devices require software coding and programming expertise.
So, one of the biggest challenges with incorporating AI into medical devices is recruiting people who have the knowledge and ability to build AI systems.
Another challenge related to incorporating AI into medical devices is government regulation.
Unlike the challenge of finding and recruiting AI experts, which may be easier for some companies than others, figuring out the regulatory framework for AI-based devices is an obstacle the entire industry must overcome.
As the authors of the Nature article mentioned earlier note:
“[M]any recent medical devices, especially when AI/ML based, use algorithms that change and can adapt over time; these are described by the FDA as adaptive algorithms, for which current regulatory frameworks were not designed.”
Though the current regulatory framework may not be ideal for AI-based devices, there is hope that things will change as adoption of AI within the medical device market grows.
As you can see, within the medical device industry, AI is in its infancy.
However, the adoption of AI within the healthcare space is accelerating and doesn’t appear to be slowing any time soon.
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