AI-Enabled MedTech Market to Surpass $4 Billion by 2024

With the COVID-19 pandemic having sparked an increased interest in the adoption of new technologies in the healthcare industry, artificial intelligence (AI)-enabled medical technologies will become much more common over the next five years, with the AI platforms market expected to grow from $1.5 billion in 2019 to $4.3 billion in 2024, according to GlobalData. One AI-enabled healthcare technology of note is a new algorithm developed by researchers from University of Edinburgh, which uses AI to detect and diagnose heart attacks in women faster and more accurately.
 
“The COVID-19 pandemic forced many healthcare providers to address the challenge of how to provide high-quality healthcare with as little contact as possible,” said Kamilla Kan, a medical analyst at GlobalData. “This triggered a huge shift towards digitalization in a relatively short time. Now the ball is well and truly rolling, and we are seeing more and more use cases for AI-powered technology in healthcare settings.”
 
GlobalData highlights multiple areas within healthcare in which AI can be applied, including data management, remote surgery, diagnostic and procedural AI assistants, drug discovery, and clinical trial design. 
 
“The use of AI in the healthcare space is expected to continue to increase in the next five years, and such a rapid surge of AI-powered platforms will only help further improve the technology’s applications in this space going forward. AI will greatly contribute in reducing the time needed to diagnose and treat patients, and will ease communication between medical personnel and patients,” Kan noted.
 
One of the more recent examples of AI in healthcare was an algorithm designed to help diagnose heart attack in women. The algorithm was developed by University of Edinburgh research team and funded by the British Heart Foundation (BHF). 
 
“University of Edinburgh AI-driven algorithm has the potential to detect heart attack with 99.5% accuracy regardless of age and gender of the patient. Additionally, it has also identified the people who need to stay at hospital for future tests with 83.7% accuracy,” Kan concluded.