MYCIN was an early expert system developed in 1976 at Stanford University to advise physicians on bacterial infections and appropriate treatment. It achieved higher accuracy rates than human doctors in tests, correctly diagnosing patients and recommending treatment in more cases than physicians. However, MYCIN was never widely implemented in clinical use due to concerns about legal liability if it provided incorrect diagnoses and the risk that doctors would rely too heavily on its recommendations instead of consulting experts.

1. University of Education, Okara Campus
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2. •In 1965 DENDRAL was
used in expert system
•MYCIN was developed in
1976
University of Education, Okara Campus
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3. WHY MYCIN
 Disease diagnosis and therapy selection
 Advise for non expert physicians with time
consideration and incomplete evidence as
 Bacterial infections of the blood
 Extended to other elements
University of Education, Okara Campus
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4. PROBLEM BEFORE MYCIN
• ROBERTS AND VISCONTI:-
 Only 13% patients are treated rationally
 66% are being given irrational treatment
 21% are being given questionable treatment
• IRRATIONAL MEANS, FOR EXAMPLE: Using a contra-indicated combination
 Using the wrong agent for a specific organisms
 Not taking the required culture
University of Education, Okara Campus
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5. DEVELOPMENT OF MYCIN
• Developed by ADWARD SHORTLIFFE in 1976
a STANFORD UNIVERSITY in the artificial intelligence
in medicine group
• First system that was implemented into the medical
field
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6. HOW MYCIN WAS TESTED FOR
ACCURACY
• 10 patients for a particular disease were chosen
• Their information was sent to 8 physicians and MYCIN
• All then came up with recommended treatment
• Those recommendations along with what the patient
actually received as treatment was sent to 8
unidentified specialists
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7. WHO WON FROM THE TEST
• MYCIN won the test
• MYCIN received the highest score as for as
accuracy and effectiveness of diagnosis
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8. STAGES IN DAIGNOSIS AND
TREATMENT
• Decide if there is significant infection
• Identify the causing organism(s) by clinical and
laboratory evidence
• Decide what antibiotic agent the organisms are
sensitive to
• Prescribe the optimal drug combination for particular
case
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9. RESULTS
 Never implemented for routine clinical use
 Shown to be competent by panels of experts even in
case where experts themselves disagreed on
conclusions
 Key contributions
 Reuse of production rules( expansion knowledge aquision )
University of Education, Okara Campus
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10. PROBLEMS
• If the system gives an incorrect diagnosis, who is
to be blamed or sued
• Physicians rely to heavily on system instead of
consulting other experts
University of Education, Okara Campus
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11. Resources

Wikipedia Page
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Building Expert Systems in Prolog
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Available at: http://www.amzi.com/ExpertSystemsInProlog/xsipfrtop.htm
Artificial Intelligence - A modern Approach by Stuart Rassell, Peter
Norvig
PC AI – Expert Systems
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Available at: http://en.wikipedia.org/wiki/Expert_system
Available at: http://www.pcai.com/web/ai_info/expert_systems.html
VisiRule Web Demos (A simple example):
http://www.lpa.co.uk/vrs_dem.htm
University of Education, Okara Campus
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