Deep learning has been used to transform artificial intelligence (AI) development, whether it is from beating players in games like Go or poker to improving self-driving AI. But perhaps the most important changes for most of us is how AI advances and machine learning are affecting healthcare. In January, a medical startup won FDA approval for an AI-assisted cardiac imaging system called Arterys, and AI is playing vital roles in other health fields such as fighting cancer and aging. NVIDIA boasts that with deep learning, “AI can help doctors make faster, more accurate diagnoses. It can predict the risk of a disease in time to prevent it.”
These changes in healthcare will democratize the healthcare system, enabling ordinary citizens to obtain reliable health information. But this does not mean that doctors will become obsolete. Instead, doctors will work alongside AI as the two cover each other’s weaknesses, revolutionizing health care and saving lives.
Removing manual labor
To get an idea of how AI will change things, let us take a look at Arterys and how it uses AI. Arterys was developed by mining a data set of more than 3,000 cardiac cases where it looked at the heart and blood flow. By being hooked to a MRI machine, Arterys can examine blood flow and MRI images to generate editable contours. It can provide an accurate picture of a heart in seconds, a process which once took an hour.
Creating that accurate picture before required manual labor more than creative thinking, but that hour is now freed up by AI. Instead of taking the time to piece together MRI images, doctors can now come up with potential treatments while they leave this sort of manual labor to AI.
A better diagnosis system
Removing aspects of manual labor is just one area where AI could make things easier. Think about the countless conditions which a doctor is expected to memorize and recall their symptoms of at the drop of a hat to diagnose a patient. An AI is superior at memorizing massive amounts of data compared to a human, and AI can look at multiple symptoms described by a human to quickly diagnose what is wrong with a patient. And with machine learning, an AI could learn what diseases are more likely, improving its accuracy over time.
This is particularly useful from the process of democratizing health care, as a recent AI algorithm from Stanford to detect skin cancer shows. Patients are told to go to a doctor if they notice a new, strange mole. However, it can be difficult and costly to visit a doctor for what may be just a mole. But the AI algorithm, through analyzing 130,000 images of skin cancer and learning what it looks like, is able to match dermatologists in determining whether a bump is skin cancer or not. This algorithm can fit on a mobile device and can be the difference between life and death.
In addition to a specific algorithm for skin cancer, there is also the example of Ada. Ada is a more general diagnostic AI software which can give a diagnosis once someone reads off a list of symptoms. A recently released video by Ada shows how Ada and Amazon Alexa can work together, giving you questions once you start listing symptoms to help diagnose you.
Using AI to self-diagnose is not yet substitute for a doctor or an immigration lawyer. However, it is still a step up from researching diseases on your own and the Stanford example shows how AI and machine learning can make major progress in the case of specific diseases.
Fighting the bureaucracy
In addition to making diagnoses and cutting out manual labor, the biggest way in which AI could save health care may be in a field which have nothing directly to do with medical care – namely, cutting down the administrative and bureaucratic costs which have sent prices spiraling. A 2015 study found that the United States healthcare system “wastes an estimated $375 billion annually in billing and insurance-related paperwork.” While politicians on both sides of the aisle have proposals on how to fix this bureaucratic problem, artificial intelligence and technology can massively slash costs.
AI can process data entry, track databases, and overall handle the grunt work which today employs thousands of healthcare workers and wastes doctors’ time. And as it learns by going over the mounds of data, it will become more efficient and let the doctors take over the human side of actually caring for patients.
AI doctors will not be supplanting humans anytime soon. But by removing much of the grunt work and memorization, they will make it easy for doctors to actually focus on caring for patients and saving their lives. The fusion between man and machine will build a better and healthier society.
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