You all have heard about using ML in the healthcare sector. And, some of you might have wondered if this technology provides real value in health care. While some people still don’t know the value of ML, the health catalyst believes that it is one of the most essential life-saving technologies ever introduced in healthcare. Read below some of the applications and advantages of ML for the healthcare industry.
The identification and diagnosis of disease and ailments are one of the primary advantages of machine learning in healthcare. It can diagnose cancers and other genetic diseases in the initial stage when they are tough to catch.
Medical Imaging Diagnosis
Machine learning and deep learning have given breakthrough technology called the Computer Vision to the healthcare sector. For example, Microsoft’s initiative InnerEye works on the image diagnostic tools for the analysis of the picture.
ML has been playing a significant role in customized treatments. It helps with the predictive analytics of health. It is also a boon for the research and better assessment of the diseases. Earlier physicians were limited to choose a specific set of diagnoses and treatments based on a patient’s symptomatic history and genetic information. But ML has opened great avenues and multiple options for doctors. The IBM Watson Oncology is an example of ML technology. It uses the medical history of the patient to help generate multiple treatment options.
Maintaining the health records of patients is a tiring process. ML eases this process and saves time, effort, and money. Today, the healthcare industry is using the cutting edge of ML technology to develop the next generation of intelligent and smart health records. The ML-based tolls help with diagnosis, clinical treatment suggestions, etc.
The outbreak of the pandemic has changed the tech industry entirely. Today, ML and AI-based technologies are used by the healthcare sector to monitor and predict epidemics around the world. ML for the healthcare industry is helping to predict everything from malaria outbreaks to severe chronic infectious diseases. ProMED-mail is a primary example of it. It is an Internet-based reporting platform that monitors evolving diseases and emerging ones and provides outbreak reports in real-time.
Radiology is one of the most sought-after applications of machine learning in healthcare. ML-based algorithms learn from the multitude of different samples to make diagnose easier and find the variables. It also helps in medical image analysis. It classifies objects such as lesions into categories such as normal or abnormal, lesion or non-lesion, etc.
Today, technology-enabled smart healthcare is no longer a flight of fancy. ML is playing a critical role in patient care, billing, and medical records. The healthcare specialists can develop alternate staffing models, IP capitalization, smart healthcare, and reduced administrative and supply costs with the use of ML. Machine learning in healthcare has got gradual acceptance worldwide.