Off late, artificial intelligence (AI) has become the buzzword for almost all industry sectors; this is no different for the healthcare arena. However, AI is a massive infrastructure undertaking, and most organizations get lost in the process of injecting this technology into their IT environment.
As many organizations continue to look at big data analytics to improve healthcare, the scope of machine learning and deep learning solutions in healthcare also increases simultaneously. Machine learning and deep learning are no newbies for the healthcare industry; however, their progress is. This makes AI hard to be deployed and efficiently managed. The lack of ability to efficiently manage AI stops organizations from using real-time analytics at the point of care. The Internet of Things (IoT) in its part is also in need of more powerful analytics infrastructure. Wearable medical devices can evidently reduce the number of patients visiting doctors, as they act as precaution tools, helping the patient analyze his physique. These devices endow physicians with the following benefits; remotely monitor the patients, get a better understanding of the patient’s lifestyle and habits, and collect accurate data. However, organizations that do not have functioning AI solutions cannot obtain, handle, or process this data in real time.
The harder the analytic solutions get to deploy in the healthcare industry, the more promising sign it leaves behind for the future of population health, predictive analytics, and quality patient care