MYCIN. This is the name of the very first AI program developed in the medical field at Stanford University in the 1970s. Its goal was to recommend treatments to physicians for certain infectious diseases. Although this program was only live for a couple of years, it opened the door to greater innovations and breakthroughs. 

Today, AI helps doctors make diagnoses, it aids surgeons in defining the “next best step” for their patients and it assists researchers when screening new drugs. But the road to a fully AI-driven healthcare system is still long and tortuous. The MYCIN program was dropped because of low acceptability by physicians and poor integration into their daily practice. Sound familiar? If it does, that’s because these challenges still exist today, over 50 years later. In today’s episode, you can expect some turbulence, because there is no such thing as disruption without some friction. Ladies and gentlemen, please take your seat, fasten your seat belts and prepare for take-off. 

We have the pleasure of welcoming on the podcast James Somauroo, CEO at SomX and fellow podcast host of the HealthTech Podcast and HealthTech Pigeon.

What this episode contains:

• AI anecdotes 


• Barrier to AI adoption 


• Evolving AI conversation 

What you will learn in this episode: 

• Artificial intelligence was created in the 1950s


• One of the pioneers in this field was mathematician Alan Turing. 


• the “Turing test” to assess a machine’s ability to act “smart”. 


• The idea was for a human examiner to evaluate the discussion between a human and a machine without knowing which was which. 

Two main movements emerged after the creation of the test: those advocating for a strong artificial intelligence, capable of human thinking and those advocating for a weak artificial intelligence, capable of helping humans in their tasks. 

• one of the first and most famous experiments regarding artificial intelligence in healthcare is SPHINX, a program designed to help doctors identify patients with jaundice, which is a yellow pigmentation of the skin in people suffering from liver conditions. 


• There are a growing number of applications of AI in healthcare: algorithms capable of identifying tumors on X-Rays, AI to help doctors interpret ECGs. 


• During Covid, AI was used by hospitals to predict the incoming flow of patients and adapt their activity. The same technology was used by governments to anticipate the number of masks or vaccines that should be ordered depending on the evolution of the situation.

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