Machine learning is a form of artificial intelligence in which algorithms are used to process large amounts of data. These algorithms allow a computer to detect patterns, learn from them, and execute tasks automatically without needing instructions or an end-user to guide it. Given both the complexity of this technology as well as its potential adoption in a number of industries; it comes as no surprise that machine learning and automation have great potential in a data-heavy industry like the healthcare sector. Machine learning is useful for a number of tasks relevant to the world of healthcare, including classification, treatment recommendation, data clustering, data-based predictions, outlier detections, automation, and data ranking. Machine learning has already seen use in the medical industry; such as when predictive analysis models were used to predict COVID-19 mortality rates. Today, we’ll discuss twelve distinct use cases of machine learning and how this emerging technology can provide better, more accurate medical care for all.
Clinical decision-making is the process of analyzing symptoms and data in order to determine what disease or disorder a patient may be experiencing, the best course of treatment for them, and potential problems which can arise from their prognosis. This machine learning model can also be used to improve patient care and has been a driving force behind electronic health record systems, or EHR’s which host this information to provide faster & more accurate treatment.
Patient recordkeeping can be a painstaking and meticulous process. For those looking to automate their recordkeeping; machine learning can be used for what’s known as Optical Character Recognition technology, or OCR. This technology can scan physician handwriting and group it automatically, helping improve data entry and data fluidity. With this improved task management system, you can make choices that better improve patient care.
Developed by Microsoft, machine learning is already being utilized to help improve medical imaging technology. Microsoft’s InnerEye project was built to improve 3D radiology images, as well as tools which can be used to differentiate between healthy cells and potentially cancerous cells. Machine learning is also used in the meta-analysis, the grouping of data to gain a better understanding of a medical or scientific phenomenon. Machine learning found that 87% of human specialists worked without error for compiling meta-analysis, whereas machine learning algorithms had a clearance rate of 92.5%.
Given that machine learning deals largely with swathes of various data, ML algorithms can also be used to create personalized medical plans. Using medication data, you can make complex decisions involving treatment plans; allowing you to easily account for possible drug interactions and minimizing the potential side effects of prescribed medication.
One of the primary methods of handling disease in the healthcare system is by preventing it in the first place. This is done by helping patients modify their lifestyles through what is known as preventive medicine. By analyzing data about patient health with programs like Somatix; you can better learn about habits and routines which can contribute to patient health problems and in turn, help you end these behaviours to improve patient care.
One of the largest challenges of handling dangerous or terminal diseases such as cancer, AIDs, HIV, or others is identifying these conditions in the early stage in order to deliver a more successful treatment regimen. Algorithms have been created to monitor a number of health conditions, such as the progress of Diabetes, liver and kidney conditions, and the study of various cancers (oncology).
It is important for physicians to gather an accurate picture of a patient’s healthcare history in order to treat them properly. By using machine learning algorithms, you’ll be more capable of asking relevant questions to collect the appropriate data for your patients, as well as the ability to use predictive analytics and group data by various types.
Improving care is a major goal of the healthcare industry. With machine learning, life for the disabled and the elderly can improve their day-to-day lives but using things such as smart reminders for appointments and medication, as well as helping mobility by giving optimal walking paths, and acquiring help for patients in the event of healthcare emergencies. This form of machine learning is already in use, with 75% of all elderly care being handled by AI in Japan.
Surgery requires great precision, a steady hand, and the ability to adapt to sudden changes in a fast-paced environment. While not in major use currently, this is one of the main goals of machine learning in the healthcare sector. Robotic surgery can be useful for better surgery modelling and planning, reducing post-op complications, and simplifying common tasks like performing incisions and suturing patients.
By utilizing data related to the medical industry and the various substances prescribed by doctors for diseases, disorders, or other health problems, ML algorithms can be utilized to find new drugs which work better and can be used for the treatment of similar health issues. This is ideal for those who need to create treatment plans for unique illnesses or for patients with specific treatment requirements.
Clinical trials are used to test new medical procedures, drugs, and other services in order to improve treatments and be adopted for more wide-scale use. Because of the importance of clinical research, these trials are often costly and time-consuming. Thankfully, machine learning can be used to test various demographic samples similar to a clinical trial and utilize data points to analyze its overall effectiveness. This was used during the COVID-19 pandemic to improve mortality rate predictions, among others.
Understanding how diseases spread and which sectors of the public are more at risk for infection is also a key component of proper healthcare. By understanding the way diseases spread, we have the tools to better prepare for outbreaks and detect signs of potential epidemics early on. Infectious disease threat modelling is completed by analyzing satellite data, news, social media information, and other forms of data to help gather an overall picture of the severity of an outbreak.