Making sense of AI
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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.
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
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:
While there remain deep challenges to using AI in medicine, there are also many opportunities for improving care. The technology can offer solutions that humans aren’t able to duplicate. Here are a few ways it can help:
VentureBeat has elsewhere covered the 10 top AI applications in the healthcare sector more broadly, and here in brief are the medical areas addressed there:
Leading tech providers that are investing heavily in AI are targeting the medical market as well.
Thousands of startups want to use the power of AI algorithms to change medicine. Summarizing them in a short article like this can’t be done. Still, it’s possible to offer a brief list with some examples for illustration.
Some obvious limitations of medical AI are similar to those that confound all AI algorithms: If the training data is spotty, biased, noisy or limited, the resulting model will echo all of these problems.
Gathering data is often more challenging in healthcare than in other domains. Between regulations, sensitivity of information, and difficulty in gathering information in a clinical setting, the datasets will naturally be less comprehensive and more prone to error. There is also not the same opportunity to redo the data-gathering that is possible in some other fields.
In many cases, medical datasets are much too small for training AI. While some AI models are based upon millions or billions of data elements, some medical studies include only a handful of patients. The scale is markedly different and there’s not the same opportunity to rely upon large datasets to squeeze out error.
Medical AI is also limited by the power of medicine itself. If human intelligence doesn’t have a workable solution, then AI can’t provide one either. If the medical science is unclear or imperfect, the AI will be too. As reflected in the survey of medical professionals referenced above, both the potential and the risks of medical AI are great. The challenge is to maximize the former, while keeping the latter within an acceptable range.
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