As technology continues to advance every aspect of healthcare, software incorporating artificial intelligence (AI), and specifically the subset of AI known as machine learning (ML), has become an important part of an increasing number of medical devices.
AI’s remarkable ability to process vast amounts of data, recognize patterns, and provide actionable insights makes it a valuable tool in modern healthcare. Implementing AI in healthcare enhances patient engagement, improves diagnostics, reduces costs, and boosts reliability, leading to better overall healthcare outcomes.
Over the past decade, the US FDA has reviewed and authorized a growing number of devices legally marketed with AL/ML and expects this trend to continue. As of October 2022, 521 of these devices received FDA market approval, with an impressive 75% dedicated to radiology. Currently, AI applications in radiology primarily focus on providing estimations of how likely a certain patient is to have complications based on radiological imaging and other types of medical imaging.
AI in healthcare offers the potential to revolutionize patient care, optimize healthcare operations, and address some of the most significant challenges faced by healthcare systems globally.
