It’s never been a more exciting time to build a career in healthcare! Thanks to AI technologies like machine learning (ML), robotics, and virtual reality, the field is transforming at a rapid pace – and breakthroughs that seemed impossible just a few years ago are now happening regularly. It’s no wonder that AI investment is a priority for 60% of healthcare leaders according to the Philips Future Health Index 2022 report.
While experts agree that it’s highly unlikely that AI or ML will become sophisticated enough to replace humans any time in the near future, these tools certainly have the capability to reduce the burden on healthcare professionals and support them to do their jobs more effectively. AI can quickly sift through massive amounts of data to provide insights and predictions, automate administrative tasks like record-keeping, optimize treatment plans, and much more. From surgical robots to rehabilitation wearables to virtual waiting rooms, here are five incredible ways that AI is changing healthcare and helping medical professionals improve – and save – patient lives.
The idea of using robotics in healthcare isn’t new, but the applications are becoming more innovative – and more widely available – every day. In fact, what was once highly specialized technology that was only found in operating rooms or labs is now something many people keep in their pockets or wear on their wrists. Fall detection technology, heart rate sensors, step counters, and other physical monitors found in smart phones and smart watches are all powered by AI and robotics!
In addition to the “cool” factor, which encourages people to purchase and use these tools, wearables makes it easier for nurses to monitor patients by providing real-time data over a continuous period of time. Using this steady stream of information, nurses can make more accurate diagnoses, identify potential chronic conditions, and even offer specific suggestions for how patients can change their behaviors for better health.
This robotic wearable technology is also used in orthotics, prosthetics, and exoskelotons, which incorporate sensors and deep machine learning to assist in rehabilitation for people with mobility issues caused by stroke, injury, or normal aging. Worn physically on the body, these devices optimize movement, develop strength, and provide stability, supporting physical therapists and occupational therapists in their efforts to help patients to recover and regain independence.
AI robotics is also taking a more prominent place in the operating room. While surgeons have long had the ability to physically control robotic devices to maneuver into hard-to-reach places or perform delicate procedures, new surgical robotics have the ability to execute a series of preprogrammed tasks. In some cases, they remain fully under the control of the surgeon, and in others, the surgeon complements the tasks being performed by the robot, so the robot essentially becomes a surgical assistant.
The potential for healthcare robotics is also expanding exponentially with the rise of virtual reality (VR) and augmented reality (AR), which have applications for both patients and doctors. Patients can step into a real or imaginary world to interact with 3D models and complete specific tasks that improve motor functions or manage pain, doctors can practice procedures and perfect their technique in a VR world, and AR can enhance MRI or CT data or provide images or support during surgery.
2. Natural Language Processing
Natural Language Processing is another exciting area in which AI is transforming healthcare and improving patient outcomes. Essentially a translator between humans and computers, NLP allows computers to understand human language – including written text and audio collected by a microphone – by converting it into code that other computers can easily analyze.
Before NLP, all the notes that doctors, nurses, lab technicians, physical therapists, or other providers wrote or recorded during patient visits were considered unstructured data – written text that couldn’t be understood by computers and therefore often went unused. However, with NLP, healthcare workers can now add these notes to patient records in a way that allows this data to be processed and analyzed by engines and machine learning algorithms.
This additional data gives healthcare providers a clearer picture of a patient’s overall health, leading to better results. Computers are able to “read” exactly what a patient shared in their own words, interpret the conversation, and analyze the context. From there, it can pull together details that otherwise would have been missed, spot conditions that were improperly coded, provide nuanced insight into a patient’s health, and make records easier to search.
In addition to improving individual healthcare outcomes, NLP is also accelerating research and leading to faster, more effective treatment options. Pharmaceutical and biotech companies have successfully used NLP to sift through mounds of clinical data, looking for patterns, trends, and other information that has led to breakthroughs in medical treatments and therapies.
3. Machine Learning
Machine learning is a type of artificial intelligence that allows computers to use data and algorithms in order to learn in the same way that humans do. Since the healthcare industry generates massive amounts of data, machine learning tools can drastically reduce the burden on everyone working in healthcare by analyzing that data faster and drawing more accurate conclusions that result in better diagnoses and treatments.
Due to this speed and efficiency, machine learning has been widely adopted in the healthcare industry. Computers are now used to comb through electronic health records looking for patterns and insights, allowing doctors and scientists to focus on interpreting what they find and using that information to make predictions. The way scientists were able to identify pending outbreaks of COVID-19 by analyzing wastewater samples is a great example of machine learning in action.
This predictive application of machine learning also allows doctors to use a patient’s makeup to determine which procedures or treatments are most likely to be successful – which is known as precision medicine. Machine learning can even be used to “learn” the difference between cancerous and healthy tissue, build predictive models that optimize drug development, and spot early signs of chronic disease – before patients have started showing symptoms.
This is just the tip of the iceberg, and the potential for machine learning to improve lives is unlimited. Deep learning frameworks are being created that assess a patient’s physical therapy performance and give it an object grade, so PTs are better able to monitor recovery and adjustment treatment plans. A system called TikTalk uses AI and ML to customize child speech therapy, providing personalized word lists and real-time feedback. Eventually, machine learning will likely touch nearly every aspect of medicine.
4. Chatbots and Beyond
There’s no question AI is transforming healthcare in ways that allow for more accurate diagnoses, more effective treatments, and improved outcomes, but it’s also improving the entire patient experience, which translates to higher quality care. Experts agree that when patients are more satisfied, they’re more likely to be engaged with their care – and that leads to better health management and better results.
One of the biggest advancements in healthcare involving AI is the chatbot. While healthcare chatbots have been around for more than 50 years, they’ve exploded in popularity in the last decade, and the industry is projected to have a value of nearly $950 billion by 2030. Most commonly used to check symptoms, schedule appointments, and provide medical information, healthcare chatbots can be accessed anytime and virtually anywhere, making them much more convenient for patients.
Along with chatbots, there are other exciting innovations that improve the patient experience – like Mend’s Enhanced Virtual Waiting Room. Telehealth providers can opt for the EVWR to engage waiting patients with a media library that includes TED talks, health information, and wellness videos.
Mend also has created an AI/Machine Learning technology-based platform that can use the web camera on a phone or computer to obtain FDA-approved vital signs while a patient waits for a digital or in-person appointment to begin. That means that instead of sitting around impatiently, patients can actively take their own blood pressure, heart rate, breathing assessment, and cardiac workload via Mend’s web cam technology as part of the digital check in process, turning waiting into productive use of wait time.
5. Automated Administration
Although slightly less glamorous than surgical robots or web cams that take vital signs, AI technology is also streamlining healthcare administration by taking care of mundane tasks like patient summaries and billing. In fact, it’s estimated that 40% of healthcare support staff tasks and 33% of practitioner tasks can potentially be automated, giving doctors, nurses, and therapists more time to take care of patients.
Automation is already being used to reduce the administrative burden for medical professionals. Through robotic process automation (RPA), computer programs equipped with machine learning, known as “software robots,” are used for everything from admitting patients to updating records to processing billing claims. Incredibly, use of RPA has been shown to eliminate up to 70% of the repetitive tasks associated with routine claims – while also reducing turnaround time by as much as 85%.
These are just some of the ways AI is transforming healthcare and delivering improvements for patients and providers alike – and more exciting changes are on the way! As technology evolves, AI has the potential to automate more tasks to reduce the burden on healthcare professionals and create ever-more-accurate predictions that lead to better pharmaceuticals, individualized treatment plans, and improved outcomes for patients. Working alongside AI, doctors, nurses, technicians, occupational and physical therapists and researchers will have more capacity and capability to do what they do best: focus on their patients and create the kinds of human interactions that save lives.