Healthcare has long been an early adopter of technological advances. These days, machine learning plays a key role in many health innovations, including the development of new medical procedures, the handling of patient data and records and the treatment of chronic diseases. Express Healthcare brought this interesting information to our attention in their article, “Transforming healthcare with AI: The road to improved patient outcomes.“
The healthcare sector is evolving even with enormous challenges accelerated by the increase in lifestyle-related disorders, global population explosion and waves of pandemics. Patient expectations have shifted to a more streamlined experience, intelligent data analytics, effective payment models and access to telehealth.
Machine learning can deliver critical insight to clinicians at the point of decision making and replace manual processes. However, many clinicians don’t reap these benefits due to a lack of understanding and data infrastructure. This isn’t limited to healthcare. Most organizations have little knowledge on how artificial intelligence (AI) systems make the decisions they do, and as a result, how the results are being applied in the various fields that AI and machine learning are being applied. Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms.
Melody K. Smith
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