Expert Systems are a subset of systems built with Artificial Intelligence (AI). Expert Systems use AI to replicate the cognitive thinking of an expert in a field. Expert Systems started in the 70’s and are made up of a set of rules and specialist information on the specific area of expertise. As Expert Systems have access to a vast amount of information (known as the knowledge base) and to the state-of-the-art analysis tools, they can outperform any single human expert.
Expert systems are usually available in narrow disciplines such as chemical analysis or radiology. Over the last 30 years, Expert Systems have become available in many fields, from healthcare or telecommunications to geology or genetic engineering.
Taking radiology as an example, a Radiology Expert System will have been taught from the reasoning process a group of experts follow to decipher a radiological image and come up with a diagnostic. The advantage of such an expert system is that it will have access to a much higher image analysis technologies, diagnosed radiologies and computational capabilities than what a group of experts would have access to when in front of a specific patient’s radiology images.
In the past, Expert Systems worked with a set of “what if” kind of rules programmed into a computer, together with a vast amount of information for comparison. However, nowadays, with the evolution of Machine Learning (ML) Techniques, Expert Systems depart more and more from the traditional “rule programming” while increasing their performance to levels impossible to achieve by a human being.
Read more about Artificial Intelligence (AI) and Machine Learning (ML)
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