AI-MED 2017-05-19T17:10:39+00:00

Welcome to AI-MED

Where Artificial Intelligence meets Medicine to leverage Smarter Medical Decisions

AI-MED introduces PAIRS ( Phyisican Assistant Artificial Intelligence System) An Ingenious & State of the Art Clinical Decision Support System (CDSS)

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Diagnostic Data Support

How is Pairs relevant to you ?

Some internal medicine cases in domain of connective tissue disorders or autoimmune diseases have multiple organ involvement. These cases are difficult to diagnose.
User choice for easy identification and entry of patient data.
(1). Automated dropdown listing.
(2). Natural Language Processor identifies patient data by using a SNOMED-CT algorithm.
Bayesian probabilistic belief networks are commonly used in artificial intelligence. Direct application of these for medical diagnosis is difficult. Alternative solution is an approx method which is implemented in PAIRS.
Errors in medical practice are common and are catastrophic to both patient and doctor. This can be prevented by use of Clinical Decision Support Systems (CDSS).
Completing patient data entry involves identification of corresponding features in PAIRS database. This involves use of NLP for similar or related features, acronyms. Good case building helps diagnosis.
User identifies either infection, neoplasia, autoimmune or others as expected disease process. Based on this PAIRS gives diagnosis from top 25 possibilities. It also suggests investigations for them

PAIRS Advantage

AI-MED helps you in validating your diagnosis though various factors

Featured on India Innovation Growth Programme 2006

Totally 30 000 disease-feature links for about 476 diseases and 2000 features.

diagnosis

Natural Language Processing based on SNOMED-CT data and algorithm. Diagnostic algorithm includes approximation method of Bayesian probabilistic belief networks as developed by Jaakkola & Jordan and published in Journal of Artificial Intelligence Research (Variational probabilistic inference and QMR-DT 10 (1999) 291-322.).

Confirmatory tests for each diagnostic possibility.

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