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One of the most challenging and valuable domains for AI is medicine. Both the opportunities and the dangers are great in applying the technology to healthcare overall.
The value of improved medical care is immediate, especially for people suffering from diseases that cannot presently be adequately treated. Artificial intelligence (AI) may have the potential to see what humans cannot and provide a level of care that is otherwise beyond our reach. And when AI algorithms work well, they can be shared widely in cost-lowering ways.
Risks and rewards
There are, however, both risks and rewards to medical AI. In a 2020 survey of medical professionals, 79% of respondents reported believing that the technology could be useful or very useful. But 80% completely or partially agreed that the risks to privacy could be very high, while 40% completely or partially rated the potential risks “more dangerous than nuclear weapons.”
AI has enabled the development of technologies that go beyond natural human processes, among other risks. Nanotechnology, gene editing, in-vivo networking (INV), the Internet of Bodies and amalgams such as the Internet of Bio-Nano Things (IoBNT) are among the technologies that offer both promise and potential harm.
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Also read: 10 top artificial intelligence (AI) applications in healthcare
What are the challenges of medical AI?
Scientists approaching medical AI want to leverage the technology’s natural abilities while limiting the potential harm. All applications of AI come with challenges, but using this technology to improve health is particularly complicated. Here are some of the challenges:
Imperfect sensors: Data gathered from medical sensors is often noisier and less precise than in other domains such as photographic classification. This is especially true when sensors penetrate a living, breathing human being. CT or MRI scanners return pixelated and blocky images with many artifacts that can cloud or obscure the details in question. X-rays may be better but they …