Machine learning algorithms are increasingly used in healthcare spaces to help turn large volumes of complex patient data into precise, actionable insights that help increase diagnostic accuracy and create personalized treatment plans.
The transformative power of machine learning (ML) lies in its ability to analyze massive datasets and detect patterns. This article will walk you through all the possibilities these algorithms bring to healthcare, from enhanced diagnostic accuracy to improved patient outcomes.
Understanding how machine learning algorithms are used in healthcare
Machine learning is a subset of artificial intelligence (AI) that uses datasets and algorithms to imitate how humans learn. Think of ML algorithms as a curious observer that examines trends in data and “learns” from them.
For example, an ML algorithm can analyze thousands of radiology images to pinpoint abnormalities faster than a human radiologist could. Using machine learning algorithms in healthcare helps shorten treatment timelines while reducing diagnostic errors.
Nature Medicine published a study in 2019 that showed that machine learning algorithms outperformed human physicians at diagnosing different cancers on mammograms, detecting abnormalities in eye scans, and diagnosing lung cancer, highlighting how these algorithms can be more efficient at identifying crucial health indicators.
Why the use of ML algorithms in healthcare is revolutionary
Now that we’ve gone over the basics of how machine learning works, let’s explore some ways the technology is revolutionizing the healthcare space:
Improved diagnostics
ML algorithms can predict a patient’s health risks based on their medical history, genetics, and lifestyle. Machine learning can evaluate individual risk factors and provide customized treatments for each patient instead of using the one-size-fits-all approach to healthcare.
Enhanced diagnostics of medical images
ML algorithms have made a tremendous impact with their ability to analyze medical scans like magnetic resonance imaging (MRI), computed tomography (CT) scans, and X-rays.
These algorithms can detect the slightest abnormalities in images that the human eye could miss. For example, Google Health’s algorithm for mammograms led to fewer false positives and negatives than physicians.
Enhanced analytics for disease management
ML algorithms can also be used to predict disease outbreaks, patient deterioration, and readmissions by examining historical data. These improved analytics allow for proactive interventions.
For example, ML algorithms can forecast the likelihood of complications by examining a patient’s vitals, current medications, and demographic data, leading to faster interventions before problems develop.
Increased administrative efficiency
Machine learning algorithms are a valuable aid for the less-glamorous side of healthcare: administration. Machine learning can help reduce administrative burdens, from task scheduling to billing, giving staff more time to focus on their patients instead of paperwork.
ML platforms can optimize appointment schedules and predict no-show rates, increasing the operational efficiency of healthcare professionals while improving patient experience.
Reasons to consider taking a course on the use of machine learning algorithms in healthcare
Wondering how taking a course on the use of ML in the medical industry can benefit your career? Some of the reasons why taking a course on machine learning is a strategic move for healthcare professionals to make today include:
Keeps you ahead of the competition
The healthcare industry has embraced artificial intelligence faster than many other industries. Learning about using ML algorithms now puts you at the forefront of the industry.
Physicians, healthcare administrators, and IT specialists who understand how to integrate ML algorithms into everyday workflows are in extremely high demand.
It equips you to innovate
Understanding how the machine learning algorithms used in healthcare work equips you with the tools to help push innovation in the field. Learning how to leverage AI tools will allow you to work more efficiently, position you as an innovator, increase the accuracy of diagnoses, and improve patient outcomes.
Even in a digital age, manual processes remain a considerable constraint for healthcare providers. Going over complicated medical records, reviewing medications, and tracking gaps in a patient’s care requires significant time commitments. Machine learning can help to streamline these processes, reducing the burden on healthcare providers.
Leads to improved patient satisfaction
Machine learning allows you to take a more patient-centered approach in your practice by freeing up the time that would have been spent on other tasks.
Machine learning tools can quickly sort through large datasets, such as libraries of patients with similar genetics, diagnosis, and medical histories that would take a physician an entire lifetime to get through, allowing for customized treatment options for patients and predictions on their side effects, costs, mortality rates, and effectiveness.
Ready to embrace the future of healthcare?
Machine learning algorithms have emerged as invaluable tools that are transforming how we prevent, diagnose, and treat diseases. Understanding how ML algorithms work and their applications in the healthcare industry can set you apart as a forward-thinking healthcare professional.
Sign up for our Machine Learning Algorithms in Healthcare course to learn how to use artificial intelligence to change lives, including your career path.