While the repercussions of a fully AI-driven world remain a mystery, it is gradually becoming evident that AI could soon be providing us with more accurate solutions for health issues that earlier went undetected. Though the digital transition in health care remains slow and cautious, in the past five years more than 90 percent of hospitals have transferred from paper-based systems to electronic systems.
Here are three ways AI could transform health care.
A history of medical records remains crucial for timely treatments. Life-threatening risks can be diagnosed at early stages using predictive analysis. This allows doctors to make prompt decisions and act efficiently on the results to save lives and unnecessary costs.
Interestingly, firms like Google and CareSkore are stepping in to make diagnosis simpler with the aim of shrinking time between tests and treatments. The Google Deep Mind Health project, although in the nascent stage, intends to use AI to learn the most effective treatment for different patients. Mining of medical records could happen within minutes, leaving enough room for early diagnosis and required treatments.
Another firm, CareSkore, a Chicago-based cloud-driven predictive analytics platform, applies machine learning to anticipate mortality. Last year, it received $4.3 million in funding to drive its concept, which includes informing doctors of patients who are more likely to skip appointments and fail to take medications. Using predictive analysis, CareSkore can update the health care organizations of a patient’s readmission rate, along with other information like the risk of hospitalization and risk of mortality. This data can then be used by doctors to provide appropriate help and advice. Interestingly, patients can use the same technology to notify doctors if new symptoms emerge and ask further questions. The system uses 23 environmental data sources, including Google Maps and Google APIs, to gauge the clinical, social, behavioral, geographic, and economic data of patients. Filtering allows users to focus on the meaningful data to cut out all the noise.
Earlier this year, Stanford University reported that its computer scientists, dermatologists, and engineers were training an algorithm to spot rashes and moles associated with skin cancer, which may help detect the condition earlier. The software uses database images with deep learning, a type of artificial intelligence modeled after neural networks in the brain.
Researchers are currently conducting studies to provide computer vision-based assistive technology solutions and revolutionize care for people with amputations.
According to recent statistics, there are as many as 500,000 upper-limb amputees in the U.S., with 185,000 new amputations every year. An AI-driven approach can allow people with amputations to detect things with a prosthetic more like a real hand and transform their lives.
This year, biomedical researchers at Newcastle University developed a bionic limb that comes with an AI-powered camera. The prototype prosthetic limb can grasp objects by applying computer vision technology in the installed camera that assesses shape and size of the object. The researchers have used deep learning to identify 500 such objects that wearers can reach for automatically. So far, the neural network allows recognition of objects that it is trained to identify, which would mean that AI memory would need an upgrade anytime a new object needs to be identified.
AI is also revolutionizing the lives of visually impaired individuals. With the goal of building vision through artificial intelligence, Aipoly has helped over 350,000 people with visual impairments explore the world using Aipoly Vision. The app allows neural networks to run on your phone, understand the camera’s input, and describe what it sees. It also identifies colors and objects, and, in an upcoming version, can even understand complex scenes and position and relationships of objects near it.
A similar app called EyeSense is also making strides in providing independence to the blind and visually impaired. The unique design is powered by advanced computer vision and AI techniques that interpret the visual world and describe it out loud for you.
For AI to reach its full potential in an industry that could benefit from it the most, the results have to be reassuring and accurate. A significant hurdle is the cost associated with creating AI solutions for basic health care needs. Secure access to data also remains a concern, which can delay the introduction of AI in regular medical centers.
However, if AI’s success rate remains high in trial and testing stages, accurate predictions could enhance early diagnosis, cut costs of misdiagnosed treatments, and revolutionize the current health care system.
Deena Zaidi is a Seattle-based contributor for financial websites like TheStreet, Seeking Alpha, Truthout, Economy Watch, and icrunchdata.
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