Artificial Intelligence is the buzzword you probably are hearing everywhere. From data analytics, cyber security, robots, logistics, automatic cars, military equipment to education, AI is hailed to be the next big thing. The question we want to address is – what are the prospects of AI with respect to healthcare?
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Medical experts worldwide are already applying AI technology to improve diagnosis, treatment recommendations, and healthcare administration. But there are more promising opportunities like:
Let’s explore each of these advantages in this blog.
AI Is As Effective As Healthcare Professionals
Many ongoing studies and observations say that AI could be as effective as health care professionals when it comes to medical diagnosis. For example, research funded by the NHS Foundation Trust in the UK has shown that the diagnoses made by AI deep learning models were as accurate as those of health professionals. AI could accurately diagnose diseases in 87% percent of cases compared to the accuracy rate of 86% for the same by
healthcare professionals. While these studies are made on limited test samples and biases could have indeed played a role in the actual results, it does prove a case for AI based medical diagnosis.
While AI diagnosis cannot replace the keen eye of an expert physician, it can help close the gap between the tech industry and healthcare and provide better means to optimize health care operations.
Combining Data Sources through Sensor Fusion
Radiology is one specific department in healthcare that can readily employ AI-driven analysis. Radiology uses different kinds of scanning tech such as X-rays and CT scans to gather information and diagnose diseases. Depending on the case, both X-ray and CT scans could be used in succession to evaluate a health condition properly.
The more information or inferences you can make out of these scan images, the more accurate your diagnosis could be. In addition, using image recognition AI software enables better diagnosis as it allows radiologists to combine and interpret data from multiple imaging sources and get deeper insights into the patient’s pathology. […]
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