- Machine learning and deep neural networks have spurred significant advances in artificial intelligence.
- Major AI applications in healthcare include diagnostics, robotic surgeries and virtual nursing assistants.
- Healthcare AI is projected to reach $6.6 billion in value by 2021.
- Adoption of AI could save the U.S. healthcare industry $150 billion annually by 2026.
In Star Wars: The Empire Strikes Back, Luke Skywalker is rescued from the frozen wastes of Hoth after a near-fatal encounter, luckily to be returned to a medical facility filled with advanced robotics and futuristic technology that treat his wounds and quickly bring him back to health. Of course, that’s the stuff of science fiction … for now.
The healthcare industry could be headed toward yet another high-tech makeover (even as it continues to adapt to the advent of electronic health records systems and other healthcare IT products) as artificial intelligence (AI) improves. Could AI applications become the new normal across virtually every sector of the healthcare industry? Many experts believe it is inevitable and coming sooner than you might expect.
What is artificial intelligence?
AI could be simply defined as computers and computer software that are capable of intelligent behavior, such as analysis and learning. It is a broad category at the cutting edge of technological development, growing and changing every day.
Machine learning and neural networks
Machine learning is the foundation of modern AI and is essentially an algorithm that allows computers to learn independently without following any explicit programming. As machine learning algorithms encounter more data, the algorithms’ performance improves.
Deep learning is a subset of machine learning that functions in a similar way with a slight twist. Deep learning goes a step further, making inferences based on the data it has encountered before. In other words, deep learning enables an AI application to draw its own conclusions. It works through an artificial neural network, which is a set of machine learning algorithms that work in tandem. A neural network loosely resembles the human brain, with a series of “neurons” that “fire” when certain stimuli (in this case, data) are present.
“Conventional machine learning solutions aren’t cognitive; they are trained from data but lack the ability to leap beyond missing or broken data and build a hypothesis about potential actions,” said AJ Abdallat, CEO of Beyond Limits. “Machine learning can be effective in detecting something anticipated, but it fails when confronted by the unexpected.”
To take artificial intelligence to the next level, Abdallat said, developers must emphasize both deductive and inductive reasoning, and emulate those cognitive patterns in the machines they design. A benefit of dynamic, deep learning solutions, he added, is that they can explain their reasoning and conclusions, a major benefit for complex decision-making.
How is AI used in healthcare?
AI is still a relatively new technology, especially in the healthcare industry where adoption remains in its infancy. As AI and machine learning tools become more sophisticated, their use cases have expanded; however, adoption of AI remains low, according to John Frownfelter, chief medical information officer at Jvion.
“We’re still in the hype phase where many organizations are trying to understand how it fits into an overall strategy,” said Frownfelter. “Early AI was seen … with more of an emphasis on pattern recognition for billing processes. It has evolved to a much more sophisticated use of deep machine learning and leveraging the power of big data.”
Modern AI applications include wide-ranging use cases, from cybersecurity to radiographic imaging, Frownfelter said. As AI applications continue to improve, the entire healthcare industry could undergo a shift. Here are some of the major ways AI is expected to shape healthcare in the coming years.
AI excels at categorizing data, especially once it has been exposed to large amounts of data on the subject. That creates great promise for AI when it comes to diagnostics – medical imaging analysis and patient medical records, genetics, and more can all be combined to improve diagnostic outcomes. Moreover, AI tools can use similar information to craft unique treatment approaches and offer recommendations to doctors.
“The really interesting developments are in the clinical arena,” said Frownfelter. “Clinical prescriptive analytics is probably the closest AI is getting to support direct patient care in 2019.”
Robotic surgeries allow surgeons to use smaller tools and make more precise incisions. Surgeons (and patients) could also benefit from AI by combining medical records with real-time data during operations, as well as drawing on data from previous successful surgeries of the same type. Accenture, a technology consulting firm, estimates that AI-enabled, robot-assisted surgery could save the U.S. healthcare industry $40 billion annually by 2026.
Virtual nursing assistants
Think of virtual nursing assistants like an Alexa for your hospital bedside. These virtual assistants replicate the typical behavior of a nurse by assisting patients with their daily routines, reminding them to take medications or go to appointments, helping answer medical questions and more. Accenture estimates that virtual nursing assistants could be the second-largest source of annual savings for the U.S. healthcare industry, cutting as much as $20 billion in costs.
Administrative workflow assistance
Naturally, medical practices, hospitals and other points of care result in a great deal of paperwork. In fact, it was consolidating and digitizing these records that led to the industry-wide adoption of electronic health records systems. AI has already started to make its way into these systems and can be used to streamline administrative functions as well. Accenture estimates that new efficiencies in administrative workflow due to emerging AI technologies could result in $18 billion in annual savings.
Read more: https://www.businessnewsdaily.com/15096-artificial-intelligence-in-healthcare.html