Across industries, artificial intelligence (AI) is changing how we communicate, consume information, and buy goods and services. Fortunately, the health industry isn’t left behind in this fast-growing experience; AI is already transforming patient experience, how physicians practice medicine, and how the pharmaceutical sector functions in health care.
From answering the phone to reviewing medical records, developing therapeutic drugs, analyzing radiology images, developing clinical diagnoses and treatment plans, and even conversing with patients, the future of AI in healthcare is a force to reckon with. We will be discussing some of the most fascinating AI trends in healthcare.
Impact of AI on Diagnosis
Statistics have shown that approximately 400,000 hospitalized patients experience avoidable harm each year, leading to about 100,000 deaths. These deaths are mainly caused by insufficient medical history data, heavy caseloads, and a wrong/slow rate of diagnosis, which increases the risk of fatal human errors. Artificial intelligence can identify and anticipate diseases — no matter how big or small — faster than most human medical experts.
Linus Health, for instance, has an early-detection platform for cognitive tests to modernize brain health. With the help of its proprietary assessment technology, DCTclock, which is the digital gold standard for pencil-and-paper clock drawing tests. Used in the detection of cognitive impairment, DCTclock’s algorithm analyzes more than 50 metrics to reflect the patient’s cognitive function.
AI in Drug Discovery
The health industry has been plagued by the ever-skyrocketing cost of developing drugs and the thousands of hours it takes to research these medications. It costs approximately $1.3 billion to put each drug through a clinical trial, with only 10 percent of the drugs successfully being marketed.
Thankfully, biopharmaceutical companies have noted how technology improves the health sector with AI-assisted drug discovery. Researchers can now analyze enormous volumes of patient outcome data using AI to pinpoint drugs more likely to be effective against particular diseases. They can screen substances that are affordable, simple to create, and safe for human consumption. Examples include BioXcel Therapeutics, Valo Health, etc.
AI Transforming Patient’s Experience
As the saying goes, time is money. This is just the case in the healthcare industry, as the ability of hospitals to provide a seamless experience for their patients is a significant factor in having more patients come in daily. As with any business, reviews can be a significant factor in patients deciding to visit or return to a hospital. The faster and more satisfactorily patients are attended to, the more willing they are to return when needed.
With the help of live physical therapists on a digital therapeutics platform run by Kaia Health, patients’ care is now tailored to their schedules. The platform offers individualized programs, including case studies, workout plans, stress-reduction exercises, and learning resources for treating chronic back pain. Another feature of Kaia Health is a PT-grade automated feedback coach with AI technology.
AI in Healthcare Data Management
Healthcare personnel face the challenge of managing large volumes of patient data. Valuable information often gets lost in overwhelming data, which can be life-threatening if not found in time. In a hospital setting, every second counts, and failure to properly connect and interpret data can be fatal.
Artificial Intelligence can keep track of each part of data — from patient information to medication prescription and drug development. The ability to swiftly track and establish connections between information within minutes, rather than waiting for years, holds great significance.
Robot-Assisted Surgery
With each passing day, robot-assisted surgery keeps getting popular. Robot-assisted surgery involves surgical robots in the operating room to help with an operation. The most popular technology in use today consists of cameras, magnifiers, and tiny surgical tools mounted on robotic arms that doctors can operate in real-time.
AI algorithms can also analyze CT scans and MRI pictures to produce 3D representations of a patient’s anatomy. With this information from the algorithm, doctors and surgeons are less prone to making fatal mistakes as they are guided more accurately on their patient’s body structure.
Doctors and surgeons can reduce the risk of problems by using this knowledge to direct them around critical body structures in patients. These technologies give medical professionals the precision, adaptability, and control necessary to carry out a variety of complicated treatments.
Conclusion
The healthcare industry has already achieved substantial strides in data analytics, robot-assisted surgery, virtual nursing assistants, and drug discovery.
However, numerous obstacles remain, like ensuring AI systems are adequately taught to avoid blunders, ensuring that patients’ data are secure from unauthorized individuals, and avoiding marginalization caused by the AI being trained without including all races.
Nevertheless, these obstacles shouldn’t stand as a reason not to embrace artificial intelligence in the health sector because they offer better treatment at cheaper costs; new technologies are expected to keep altering how patients interact with physicians and nurses.
