Written by: Alyssa Sevilla
Clinically reviewed by: Robert Philibert
Healthcare is facing a revolution as technology advances, and artificial intelligence (AI) and machine learning (ML) techniques have become more accessible across different industries. From predicting heart disease to uncovering correlations between different diseases, AI and ML transform how we diagnose and treat conditions like coronary heart disease. This blog will discuss how machine learning and artificial intelligence are revolutionizing healthcare and giving researchers more insight into patients than ever before.
At its core, machine learning is a type of artificial intelligence (AI) that uses algorithms to learn from data without being explicitly programmed. In healthcare, ML techniques can identify patterns and trends in data sets, allowing for more accurate diagnoses and treatments. ML algorithms can analyze large amounts of patient information quickly and accurately, which can help identify correlations between diseases or even predict the likelihood of developing certain conditions.
Another application of machine learning in healthcare is predictive analytics. Predictive analytics uses ML algorithms to assess a patient’s risk factors and then create a model that predicts their probability of developing certain conditions like heart disease. This allows doctors to better target treatments and preventative care based on an individual’s risk profile. Predictive analytics can also identify high-risk patients who benefit most from targeted interventions, such as lifestyle changes or preventive screenings. Meaning quicker and more accurate allocation of care for patients in need.
One of the most exciting applications of machine learning in healthcare is diagnosing cardiovascular diseases like coronary heart disease (CHD). ML algorithms have been trained to recognize patterns in electrocardiograms (ECGs) which can help detect CHD earlier than traditional methods, like lipid panel testing. Researchers have found that AI-enhanced ECGs can identify markers for CHD with greater accuracy than before. Furthermore, this technology has been shown to reduce false positives by up to 40% compared with conventional methods.
Despite these successes, there are still many challenges in integrating ML and AI into our healthcare system. One issue is the need for more comprehensive datasets so that algorithms can be trained effectively on diverse populations across different cultures and countries. Additionally, there needs to be better regulation around data privacy so that healthcare providers are comfortable sharing patient information with third-party vendors who use machine learning technologies. Finally, there needs to be better infrastructure in place so that hospitals can access the right technology quickly and efficiently and ensure that it meets safety standards as outlined by regulatory bodies such as HIPAA (Health Insurance Portability & Accountability Act). The greatest hurdle amongst healthcare leaders will be facing that breakthroughs in technology can be trusted to fill in the gaps medical professionals sometimes miss.
Machine learning is revolutionizing healthcare by making diagnosis faster, more accurate, and cost-effective while opening new doors for personalized medicine tailored specifically toward individual patients’ needs. While this technology has already shown great promise in improving outcomes for many medical conditions like heart disease, there are still many challenges ahead, such as data privacy regulations and access issues that require further attention before this technology is adopted into mainstream healthcare systems around the world.
Resources:
- “United States: Baxter Presents Data at ASHP Meeting Indicating Machine Learning May Enhance Infusion Pump Programming Safety.” MENA Report, Albawaba (London) Ltd., Dec. 2021.