Vision & Imaging Blog
Emphasizing and Implementing Safe Practices for Using AI in Medical Fields
Machine vision and AI are already having a major impact on the healthcare sector. They can do remarkable tasks such as finding diseases on medical imaging scans. These cutting-edge technologies can perform tasks faster and more efficiently than existing devices or even doctors who have trained for years in their fields of study.
In the past, machine vision and AI results were often plagued with false positives. But new machine learning algorithms are eliminating those errors. However, the implementation of machine vision and AI for healthcare brings new challenges. A report recently published in Science magazine titled “Algorithms on regulatory lockdown in medicine” takes a look at the challenges facing regulators regarding artificial intelligence.
You may have asked yourself some of these questions. What risks will Artificial Intelligence devices bring? How should AI be managed? How do we know what factors to consider first? The report in Science explored these questions.
Machine Vision and AI Are Essential to Healthcare
It didn’t talk long for machine vision and AI to become deeply entrenched in healthcare. Manufacturers are looking to AI to create drugs, health sensors, and machine vision analysis tools. AI is helping to formulate drugs that work faster. AI algorithms can also help to find evidence of conditions such as hemorrhages on the brain.
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How Will Apple’s $500 Billion Investment for US Manufacturing, Education, and AI Play Out?
On February 24th, Apple announced a “commitment… to spend and invest more than $500 billion in the U.S. over the next four years”, with plans for new manufacturing plants, enhanced support for manufacturing partners via their Advanced Manufacturing Fund, and a myriad of other initiatives. As part of this plan, Apple projects 20,000 new hires, with roles focused on R&D, silicon engineering, software development, and AI/ML engineering.
To accomplish all this, the FDA has approved software with “locked algorithms.” Such software must provide the same result each time and not change with each use. These programs have become invaluable to the healthcare industry. But AI benefits most when it can evolve in response to new data. New algorithms, called “adaptive algorithms,” are formulated.
Regulations and Risks Must Be Reviewed
Adaptive algorithms would blur the lines between offering up research data and practicing medicine. This poses a problem. Do we want machines practicing medicine? The system would be extremely valuable and likely very effective. But would it be safe? The healthcare industry is highly regulated. Regulating an adaptive algorithm could be tricky as it is constantly changing.
The report suggested that regulators prioritize risk monitoring. Instead of planning for future algorithm changes, it would be best to perform continuous risk assessments. Regulators must develop new processes to monitor, identify, and manage risks. These processes would be relevant to any business that develops AI embedded products and services.
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