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AI for healthcare: Scaling Access and Quality of Care for Everyone

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Talk given with Anitha Kannan at MLConf 2019. Link to abstract here: https://mlconf.com/speakers/anitha-kannan/

Publié dans : Technologie

AI for healthcare: Scaling Access and Quality of Care for Everyone

  1. 1. AI for healthcare: Scaling access and quality of care for everyone Anitha Kannan Xavier Amatriain MLConf 10/08/2019
  2. 2. ● >50% world’s population with no access to essential health services ● US… ○ 10% of adult population has no health insurance ○ 28% of working adults are under insured Healthcare access is a major issue Kaiser Family Foundation analysis of the 2017 National Health Interview Survey Merrit Hawkins, 2017 survey shortage of 120,000 physicians by 2030
  3. 3. Patient-Doctor interaction ● Doctors have ~15 minutes to capture pertinent information about a patient, diagnose + recommend treatment ● 30% of the medical errors causing ~400k deaths a year are due to misdiagnosis 2069 doctors solve 1572 HumanDx cases
  4. 4. Online search and/or Healthcare access? “72% of internet users say they looked online for health information within the past year” “More than ⅓ use Internet to self-diagnose” [Pew Research] 1.4M daily 25M daily Need more than Google can deliver Less cost and friction than PCP visit
  5. 5. We have an opportunity to reimagine healthcare
  6. 6. We have an obligation opportunity to reimagine healthcare
  7. 7. Looking Forward: Towards AI powered Learning Health Systems ● AI + human practitioners for Quality Care ● Less than 20% of the cost for best healthcare access ● Mobile First Care, 24/7 always on
  8. 8. What are we doing? ● Mission: Provide the world's best healthcare for everyone ● Product: User-facing mobile primary care app ● Team: Building an awesome and diverse team ● Approach: State-of-the-art AI/ML + product/UX/clinical AI-based interaction AI + Health coaches AI + Doctors
  9. 9. Breakthroughs in AI & healthcare
  10. 10. Peer-reviewed research at Curai
  11. 11. AI “in the wild”: Learning health systems
  12. 12. Automation/AIAutomation/AI Automation/AI in healthcare Pertinent information gathering Assessment (Diagnosis, Triaging etc) Plan (Next steps, treatments) Chief complaints
  13. 13. AI in the wild: Desired properties ● Easily extensible ○ Incrementally/iteratively learn from “physician-in-the-loop” or from additional data ● Knows what it does not know ○ Models uncertainty in prediction ○ Enables fall-back to “physician-in-the-loop”
  14. 14. Automation/AI in healthcare Automation/AIAutomation/AI Pertinent information gathering Assessment (Diagnosis, Triaging etc) Plan (Next steps, treatments) Chief complaints
  15. 15. AI for assisted diagnosis (since 1980s) ● Expert systems ○ Mycin, Internist-1, DxPlain, VDDx, QMR ● Covers over 1000 diseases and 3500+ findings ○ Most comprehensive diagnosis model, so far ○ 30+ years of expert curation based on research and evidence-based literature
  16. 16. Expert systems in the wild? ● Not easy to extend ○ Costly, time consuming and time-delayed ○ Poor generalization to new places ● Does not know what “it doesn’t know” ○ Constrained to diseases in the system
  17. 17. Assisted diagnosis in the wild 1. Extensibility a. Diagnosis as a ML task i. Expert systems as a prior b. Modeling less prevalent diseases i. Low-shot learning 2. Knowing what you don’t know a. Measures of uncertainty in prediction b. Allows fall-back to “physician-in-the-loop”
  18. 18. Assisted diagnosis in the wild 1. Extensibility a. Diagnosis as a ML task i. Expert systems as a prior b. Modeling less prevalent diseases i. Low-shot learning 2. Knowing what you don’t know a. Measures of uncertainty in prediction b. Allows fall-back to “physician-in-the-loop”
  19. 19. x Clinical case simulator Example of simulated case Knowledge base central to expert systems Expert systems as prior
  20. 20. ML models for diagnosis clinical cases simulated from expert system From expert systems to ML model for diagnosis
  21. 21. ML models for diagnosis clinical cases simulated from expert system From expert systems to ML model for diagnosis clinical cases from other sources eg. electronic health records
  22. 22. Assisted diagnosis in the wild 1. Extensibility a. Diagnosis as a ML task i. Expert systems as a prior b. Modeling less prevalent diseases i. Low-shot learning 2. Knowing what you don’t know a. Measures of uncertainty in prediction b. Allows fall-back to “physician-in-the-loop”
  23. 23. Assisted diagnosis in the wild 1. Extensibility a. Diagnosis as a ML task i. Expert systems as a prior b. Modeling less prevalent diseases i. Low-shot learning 2. Knowing what you don’t know a. Measures of uncertainty in prediction b. Allows fall-back to “physician-in-the-loop”
  24. 24. Open-set diagnosis Amblyopia Gastroenteritis Diseases within diagnostic scope
  25. 25. Open-Set diagnosis Universe of diseases Amblyopia Diabetic Ophthalmoplegia Gastroenteritis is aware and avoid misclassifying unknown diseases as known Diseases within diagnostic scope
  26. 26. Open-Set diagnosis Universe of diseases Amblyopia Diabetic Ophthalmoplegia Extra diseases Gastroenteritis avoids misclassifying unknown diseases as known. Diseases within diagnostic scope Entropic open-set loss: Maximize predictive entropy of unseen examples
  27. 27. AI in-the-wild: Desired properties ● Easily extensible ○ Incrementally/iteratively learn from “physician-in-the-loop” or from additional data ● Knows what it does not know ○ Models uncertainty in prediction ○ Enables fall-back to “physician-in-the-loop”
  28. 28. Medical Information gathering “in-the-wild” Real users with health issues that an AI medical agent may not understand
  29. 29. Looking Forward... ● AI + human practitioners for Quality Care ● Less than 20% of the cost for best healthcare access ● Mobile First Care, 24/7 always on AI-based interaction AI + Health coaches AI + Doctors https://firstopinionapp.com/ 39.6 M Californians with access to high quality affordable primary care

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