In the early days of artificial intelligence research, scientists were determined to create computer systems that could replicate human expertise. One of the first successful "expert systems" was MYCIN, developed in the 1970s at Stanford University. Though never used in real-world medicine, MYCIN demonstrated the potential of AI in healthcare – paving the way for today‘s medical AI applications.
Seeking to Codify the Knowledge of Human Experts
In the 1970s, AI researchers became excited by the concept of expert systems – programs aimed at representing and automating the skills of human experts within specialized domains. A pioneering system was DENDRAL, created in the 1960s to analyze chemical compounds for novel discoveries. MYCIN built upon DENDRAL‘s techniques, aiming to capture the expertise of infectious disease specialists.
The goal was to encode the heuristic reasoning used by clinicians in an AI system. This included the ability to make diagnoses based on symptoms, handle uncertainty, and explain the logic behind conclusions. Researchers believed MYCIN could show AI‘s promise in augmenting medical experts.
Architecture: Rules and Backward Chaining Search
MYCIN‘s knowledge base consisted of around 600 rules acquired from extensive interviews with infectious disease doctors. These rules represented empirical associations and patterns that clinicians used instinctually in diagnosing difficult infections.
For example, rules connected patient symptoms and test results to the likelihood of certain bacterial organisms being responsible for an infection. Other rules linked bacteria types to optimal antibiotic therapies.
MYCIN‘s inference engine used a technique called backward chaining search. This involved starting with a hypothesis of infection, then working backwards to gather evidence to prove or disprove the hypothesis and determine the bacterial culprit.
The system would pose a series of yes/no questions to collect findings about a patient‘s infection. Each piece of evidence supported or detracted from potential bacteria according to the rules. MYCIN used a scoring system to handle the uncertainty involved.
Diagnosis and Treatment Recommendations
MYCIN focused on diagnosing serious infections like meningitis, bacteremia, and bacteria-caused clotting disorders. For a patient case, it would determine the likelihood of various bacterial organisms being responsible based on provided symptoms and test results.
The system could recommend appropriate antimicrobial drugs according to the diagnosed infection. It would also suggest the proper dosage customized to factors like patient age and kidney function.
Unlike a black-box AI, MYCIN could explain its reasoning at each step. This increased physician confidence in its diagnostic and therapeutic recommendations.
Pioneering Performance, But Reluctance to Adopt
In thorough evaluations, MYCIN demonstrated expertise and accuracy comparable to infectious disease specialists in diagnosing certain difficult infections like meningitis. It outperformed general practitioners in selecting proper antibiotics and dosages.
However, MYCIN never progressed beyond testing to real-world usage. Doctors were still reluctant to fully trust AI reasoning. Liability concerns also hampered adoption of the non-human expert system.
Influence: Enabling a New Generation of AI in Medicine
Though not adopted itself, MYCIN proved the potential for AI systems to replicate specialized medical knowledge. It showed AI could reason about uncertainty and explain conclusions – overcoming two major challenges.
MYCIN directly inspired many later medical AI systems, including:
- CADUCEUS – expert system for internal medicine
- QMR – diagnoses related to internal medicine
- Internist-I – large medical diagnosis system
The EMYCIN system provided a reusable framework based on MYCIN‘s architecture. This enabled easier development of diagnostic expert systems.
Despite limitations like brittleness and lack of common sense, MYCIN paved the way for the modern use of AI to enhance healthcare. Powerful medical AI today delivers improved diagnosis, treatment, and outcomes across specialties – with MYCIN laying the foundation decades ago.