Physician Assistant Artificial Intelligence Reference System

AM Mohan RaoUmashankar Adi Kotturu

A. Sri Kailash

www.ai-med.in/pairs/

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Pain factors

• Misdiagnosis– Common vs rare disease

• Missed diagnosis– Errors in clinical data

• Delayed diagnosis- Complex case

• Treatment costs- Unnecessary testing

• Drugs- Side effects

• Reasoning-Uncertainty of clinical data

• Perception-Vast domain knowledge

• Diagnostic bias -Experience

• Inference -Infection? Neoplasia?

• Training -Latest advances

Patient Doctor

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Solutions

• Internet- Google search- Social media

• Colleagues- Seminars, discussions

• Journals, books -Access to quality information

• Timely advise -Emergencies

• Resources -Limitations in remote settings

• Web application -Bi-layered Google search -WhatsApp Messages

• Mobile app -Artificial intelligence

• Database -SNOMED CT 426 000 terms

• Diagnostic Decision Support (DDS)

-Logic & probability

• Natural Language Processing (NLP)

-Text & XML records

General Ai-med.in

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Physician Assistant Artificial Intelligence Reference System(PAIRS)

• Web application -Bi-layered Google Search, DDS

• Mobile app -Android and iPhone, NLP & DDS

• NLP- Based on SNOMED CT algorithm

• DDS -Based on Bayesian method

• Database-PAIRS specific: 18 397 for 485 diseases and 1964 findings

-SNOMED CT: 426 000 terms, 5190055 relationships

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Search EnginePAIRS Google

Bi-layered Single layered

SNOMED CT algorithm + Google search

Google search alone

Pathophysiological + Computer based

Computer algorithm alone

Context based Word based

Limited relevant search Exhaustive irrelevant search

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Google Search

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PAIRS Search

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PAIRS Google Search

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Diagnostic engine

• Inference engine -3 levels

• Feature given disease - (a).concomitant in assertion & negation - (b).concomitant in assertion only - (c).concomitant in negation only

• Ontological class -Both system and organ are shared -Only system is shared -Neither system nor organ are shared

• Word vectors -Medical text corpus 50 million words

• Bayesian probability -Lower bounds

• Feature given disease -(a). Biopsy (b). Deep tendon reflexes brisk (c). Loss of tendon reflexes

• Ontological class - of disease feature links

• Word vectors - 485 diseases, 1964 findings

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PAIRS DDSPatient data entry: directly or by file (txt or xml)

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PAIRS DDS: Diagnostic types

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PAIRS DDSDiagnostic output for different types

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Marketing | strategy

• Licensing -Hospitals -Medical colleges -Residents and Medical students -Pharmaceutical companies -Telemedicine

• Advertisement -Drugs and brands

-Side effects

• Development -Database -Diagnosis

• Evaluation -tertiary hospital

• Publications -PAIRS evaluations

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Team

• AM Mohan Rao -Full time employee -Worked in Nobel laureates environment at a top notch research institute in US -Committed to work for breakthrough technology in

Medicine. 35 years experience.

• Dr. N.S.N. Rao -Professor of Pediatrics

• Dr. P.N. Rao -Gastroenterologist

• Dr. Ravi Kalaputapu –Strategic advisor

• Uma Shankar Adi -Entrepreneur and evangelist -20 years experience

in research and development

• Sri Kailash Design engineer

• Anand Pothapragada Web site and MySql

Main Innovator Project associates

Advisors

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Financials |Projections

• Product development completed

• Require evaluations in hospital for 2-4 months

• Licensing product to corporate hospitals

• Brands and drug ads to pharma companies

• Money needed for office set up

• Build up a team to include full time doctors, software and marketing professionals.

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References

• 1. Subsumptive reflection in SNOMED CT: a large description logic-based terminology for diagnosis

http://arxiv.org/abs/1512.03516• 2. Using SNOMED CT concepts for PAIRS https://

www.researchgate.net/publication/221426464_Using_SNOMED_CT_concepts_for_PAIRS

• 3. And now, artificial intelligence as a medical toolhttp://www.thehindu.com/2003/06/09/stories/2003060903150500.htm

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Thank you