MYCIN: the beginning of artificial intelligence in medicine

November 15, 2018

The world of artificial intelligence is expanding into more and more areas, and its presence in our daily lives is increasingly common—from video games to the creation of smart homes. Much of this progress has been driven by expert systems, originally designed to learn from humans and replicate their behaviour. Today, we’re taking a look at how one of these systems became a milestone in the medical field.

MYCIN was one of the earliest expert systems in the history of AI. Its goal was to support the diagnosis of blood diseases, assisting doctors and saving them time in clinical decision-making. It could identify the bacteria responsible for infections in patients and recommend appropriate antibiotics, adjusting doses based on the patient’s weight. It also detected severe infectious diseases such as meningitis and bacteraemia.

MYCIN was a pioneer in proving that a machine could match—and sometimes surpass—human doctors in specific tasks.

Development of MYCIN began in the early 1970s at Stanford University as part of the PhD thesis of Edward Shortliffe, under the supervision of several experts including Bruce Buchanan. The system was written in Lisp and took five to six years to complete. Its name, "MYCIN", was inspired by the names of some of the antibiotics it recommended.

How MYCIN worked: a breakthrough that never reached hospitals

MYCIN was an expert system built on a series of cause-and-effect rules, with a database containing around 500 rules. For the program to function, the user had to answer a set of yes/no questions.

Based on the input, MYCIN would provide a list of likely bacterial causes along with a confidence score. Its behaviour closely resembled that of a real doctor—it could even explain the reasoning behind its conclusions and prescribe the necessary medication to treat the infection.

Its 70% diagnostic accuracy often outperformed medical specialists.

Although MYCIN was highly successful, it was never implemented in hospitals. Its results were strong—it achieved around 70% accuracy, often exceeding that of human experts in tests carried out under the same conditions.

A legacy that lives on

Despite its high diagnostic accuracy, MYCIN faced significant criticism and was never adopted in hospitals due to legal concerns. If the program delivered an incorrect diagnosis that negatively affected a patient’s health—or even resulted in death—who would be held accountable?

MYCIN opened the debate on AI's legal responsibility in critical decision-making.

While the full potential of expert systems in medicine is yet to be realised, many initiatives are already under way. Today, these systems are also being used successfully in a wide range of sectors: from selecting the best candidates for credit approval in banking, to mineral exploration and genetic engineering.