Healthcare is beginning to explore machine learning’s vast potential. Technology that uses algorithms to identify behaviors or make predictions can benefit virtually every part of the industry. Machine learning models can use unstructured data, including images, text, or the spoken word, to perform tasks that previously only humans could do, like recognizing an anomaly in an image or transcribing speech. Also, although a machine learning model also bases its decision-making on known parameters, the system can “learn” as new data is collected and new outcomes occur.
Machine Learning Healthcare Applications
The complex world of healthcare took a cautious approach to using this technology, but it is now embracing a variety of valuable machine learning healthcare applications, such as:
Machine learning can provide healthcare clinicians the data they need to make optimal treatment decisions for specific patients. IBM Watson Oncology. Genomics, and Clinical Trial Matching Solutions, for example, can help physicians deliver personalized medicine while saving substantial amounts of time reviewing patients’ records.
Machine learning can also aid in diagnosing conditions based on vast stores of data. Researchers at Johns Hopkins used machine learning technology to develop a tool that detects age-related macular degeneration — and it’s as accurate as detection by human ophthalmologists.
Classifying areas of radiological images as either healthy or cancerous tissue can be complicated by an array of variables. Machine learning algorithms can learn to identify lesions based on all previous screenings and data, taking those variables into account each time, and resulting in more accurate classifications.
Machine learning can guide surgical robots with great precision, enabling finer detail and eliminating human error.
Machine learning systems can be central to studies, but they also may make participation easier. Duke University Health System developed an app, Autism & Beyond, powered by the Apple Research Kit, which used the front-facing camera of an iPhone and facial recognition algorithms for a study on diagnosing autism in young children. The app made it possible to enroll more people in the first month than in a previous nine-month study.
Machine Learning Benefits for Healthcare Administration
In addition to the innovations that machine learning makes possible for patient care, there are also machine learning applications that can help healthcare organizations improve efficiency and save time. Machine learning can take on the challenge of managing mountains of records, analyzing them, finding patterns, and suggesting next steps. For example, machine learning technology can help a healthcare organization create an electronic smart records system that keeps patient records up to date, brings priority issues to the forefront, and suggests treatment options.
In addition, voice recognition or optical character recognition (OCR) capabilities of machine learning systems can save time updating records in an EHR or retrieving data more easily. In an industry that is always looking for ways to operate more efficiently and cost-effectively, machine learning solutions that streamline workflows and reduce errors would be highly valued.
Keys to Continued Machine Learning Healthcare Adoption
Machine learning implementation in healthcare is in its early stages, but there are some vital factors for continued adoption. Global management consulting firm McKinsey & Company points out that the biggest criticism of machine learning in healthcare is that machine algorithms are a “black box” and healthcare providers don’t have a clear understanding of how they generate their conclusions or predictions. Explainable AI is crucial for healthcare — providers won’t accept machine learning algorithms that aren’t transparent.
When you give healthcare providers a look inside the black box, it’s also vital that the machine learning system uses credible scientific and clinical standards and proper context. The McKinsey & Company article points out, “Without such a context, machine learning could conclude that cigarette lighters cause lung cancer.” Machine learning will only be as valuable as the data you use and how you use it.
The Future of Machine Learning in Healthcare
The healthcare ecosystem, from providers and patients to regulators and investors, are optimistic about the advances in care that machine learning can deliver, but widespread use is still years in the future. Gartner’s Hype Cycle for Machine Learning in Medical Devices puts most applications in the five- to 10- year range before they are productive throughout the industry.
Imaging analytics, an area in which value has already been demonstrated, will likely be the focus in the near future, but the number of healthcare applications for machine learning will grow and expand. Patient-facing tools including health apps, behavior modification tools, and voice-activated virtual assistants may also be introduced in the coming years.
Medtech companies that recognize machine learning’s potential and the healthcare trends this technology can support can be at the forefront of innovation. Will your business lead the way?
About the Author
Carevoyance contributor Bernadette Wilson of B Wilson Marketing Communications is an experienced journalist, writer, editor, and B2B marketer, specializing in content for technology companies.