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AI-MED
The document outlines the Physician Assistant Artificial Intelligence Reference System (PAIRS), focusing on diagnostic challenges like misdiagnosis and the need for advanced tools. It details a web and mobile application leveraging natural language processing and Bayesian methods to enhance diagnostic decision support based on a comprehensive medical database. The development team includes experienced professionals, and there's a strategy for licensing and marketing the product to hospitals and pharmaceutical companies.
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AI-MED
- 1. Physician Assistant ArtificialIntelligence Reference System AM Mohan Rao Umashankar Adi Kotturu A. Sri Kailash www.ai-med.in/pairs/
- 2. 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 2
- 3. Solutions • Internet - Googlesearch - 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 3
- 4. Physician Assistant ArtificialIntelligence 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 4
- 5. Search Engine PAIRS Google Bi-layeredSingle 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 5
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- 9. Diagnostic engine • Inferenceengine -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 9
- 10. PAIRS DDS Patient dataentry: directly or by file (txt or xml) 10
- 11. PAIRS DDS: Diagnostictypes 11
- 12. PAIRS DDS Diagnostic outputfor different types 12
- 13. 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 13
- 14. Team • AM MohanRao -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 14
- 15. Financials |Projections • Productdevelopment 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. 15
- 16. References • 1. Subsumptivereflection 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_S NOMED_CT_concepts_for_PAIRS • 3. And now, artificial intelligence as a medical tool http://www.thehindu.com/2003/06/09/stories/200306 0903150500.htm 16
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