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AI in Healthcare: From Hype to Impact (updated)

  1. Chen & Decary, ITCH 2019 AI in Healthcare: From Hype to Impact Workshop presented at ITCH 2019: Improving Usability, Safety and Patient Outcomes with Health Information Technology Victoria, BC, Canada Feb 14, 2019 Cogilex R&D Inc. Mei Chen & Michel Decary
  2. Chen & Decary, ITCH 2019 Achievement, excitement, hype, & fear Google’s AlphaGo has defeated human Go master Self-driving cars are as safe as human drivers AI defeated human doctors in contest to diagnose tumors AI will replace doctors, especially radiologists Machine has surpassed human intelligence AI promises a new paradigm for healthcare & it will revolutionize the industry
  3. Chen & Decary, ITCH 2019 A reality check In general ● 60-85% all big data strategies have failed due to a combination of challenges ( Walker, 2017, Digital Journal). In healthcare ● Rapid AI development in the industry; ● Many success stories, but mostly related small-scale pilots or research projects; ● Some heavily invested projects have failed; ● Early hype around AI promised miracle cures and delivered few results (Hyde, 2018, Forbes).
  4. Chen & Decary, ITCH 2019 Challenges for using AI in Healthcare • Inadequate understanding about what a particular type of AI technology can or can’t do • Lack of good implementation strategies • Incompatibility with legacy technologies and data • Shortage of trained workforce • Pre-existing corporate biases
  5. Chen & Decary, ITCH 2019 Objectives of the workshop Understand the real potential of AI for healthcare and gain insights about how to invest wisely in AI . ● Role of AI in healthcare ● Types of AI technologies useful for healthcare What is it? How does it work? What is the context of its proper use? ● Insights about how to use AI to support best medical decision making and clinical practice Exemplar AI applications in healthcare Next-gen AI-powered EHR systems
  6. Chen & Decary, ITCH 2019 Artificial intelligence(AI)–A subfield of computer science ● AI is machine that simulates aspect of learning or any other feature of human intelligence (John McCarthy, 1956). ● The theory and development of computer systems able to perform tasks normally requiring human intelligence. ● Being able to pass the Turing test
  7. Chen & Decary, ITCH 2019 AI Subfields: Source: Kumar GN, 2018
  8. Chen & Decary, ITCH 2019 AI in healthcare: Augmented Intelligence AI as a powerful tool and partner * Man + machine = enhanced human capabilities (AMA, 2018) AI can help human • Unlock the power of big data and gain insight into patients • Support evidence-based decision making, improving quality, safety, and efficiency • Coordinate care and foster communication • Improve patient experience and outcomes • Deliver value and reduce costs • Improve health system performance & optimization
  9. Chen & Decary, ITCH 2019 Human-machine Partnership in Healthcare AI-powered Automation Improving Effectiveness AI-powered Automation Man Machine Improving effectiveness ● Quality ○ Experience ○ Outcomes ● Safety ○ Ways to ensure patient safety ● Efficiency ○ Usability ○ Productivity ● Access to care Controlling costs AI-powered automation • Medical robotics o Surgical robots o Rehabilitation robots o Smart pills • Machine learning o Supervised learning o Unsupervised learning o Reinforcement learning • Natural language processing o Statistical vs. rule-based NLP • AI voice technology o Clinical voice documentation o Voice nursing assistants
  10. Chen & Decary, ITCH 2019 10 Promising AI Applications in Health Care Source: Harvard Business Review, Kalis, Collier, Fu, 2018 E H R
  11. Chen & Decary, ITCH 2019 Types of AI technologies for Healthcare ● Medical robotics Surgical robots, rehabilitation robots, smart pills, senior’s robotic companion ● Machine learning/Deep learning Supervised learning-->unsupervised learning-->reinforcement learning ● Natural language processing (NLP) Statistical vs. rule-based NLP ● AI voice technology Medical voice documentation; AI nursing assistants (voicebots)
  12. Chen & Decary, ITCH 2019 Arthur Samuel, 1950 The field of study that gives computers the ability to learn without being explicitly programed. Google dictionary Machine learning is a program or a system that builds or trains a predictive model from input data. The system then uses a learned model to make useful predictions from new, never-before-seen data drawn from the same distribution as the one used to train that model. What is machine learning (ML)?
  13. Chen & Decary, ITCH 2019 Supervised learning Unsupervised learning Semi-supervised learning Reinforcement learning ML can be applied to all data types: images, audios, text, and videos Machine learning algorithms: ● Given input (a data set); ● known outcomes (labeled); ● Look for patterns correlated outcomes with input to make prediction
  14. Chen & Decary, ITCH 2019 Machine learning example Cat Source: Unspecified
  15. Chen & Decary, ITCH 2019 Identifying the underlying structure/patterns of data and trying to map input variables into discrete categories Most AI successes are achieved through supervised learning Data-driven clinical decision support: ● Diagnostic analysis using medical imaging (e.g., CT-scan, x-rays, MRI results) ● Making medical diagnosis based on the symptoms ● Designing and selecting treatments based on biomarkers or other attributes ● Identifying at-risk groups based on health-related factors and social determinants Supervised learning: Classification problem
  16. Chen & Decary, ITCH 2019 Benign Malignant Uncertain Clump thickness, uniformity of cell size, shape Classification problem: Identifying a disease Source: Unspecified
  17. Chen & Decary, ITCH 2019 Classification problem: Classifying disease severity Source: Lily Peng, Google AI Healthcare
  18. Chen & Decary, ITCH 2019 Classification problem: Identifying different types of diseases CheXpert A large dataset of chest X-rays and competition for automated chest x-ray interpretation --Launched in 2019 by Stanford university Image Source: Unspecified
  19. Chen & Decary, ITCH 2019 Supervised learning: Regression problem Predicting results within a continuous output. Predictive analytics in healthcare: ● Predicting inpatient mortality and long length of stay based on EHR Big Data (Google and partners, 2018) ● Predicting the number of ER patients during a specific period, needed staff and beds based on past data ● Predicting patients’ survival rates based on health conditions, age, and other characteristics
  20. Chen & Decary, ITCH 2019 Unsupervised learning: Clustering
  21. Chen & Decary, ITCH 2019 Unsupervised learning: Anomaly detection
  22. Chen & Decary, ITCH 2019 Unsupervised learning: Pattern identification in face recognition Image source: Unspecified
  23. Chen & Decary, ITCH 2019 Unsupervised learning: Pattern recognition in genomics
  24. Chen & Decary, ITCH 2019 Genomics for treatment design Image source: Unspecified
  25. Chen & Decary, ITCH 2019 Monitoring tumor response to treatment Beyond diagnostics: Machine learning on medical images for real-world clinical research Image source: Unspecified
  26. Chen & Decary, ITCH 2019 Natural language processing (NLP) Natural language processing (NLP) deals with building computational algorithms to automatically analyze and represent human Language, particular text. Statistical NLP vs. Rule-based approach
  27. Chen & Decary, ITCH 2019 Statistical NLP: Machine learning Successes in NLP tasks: • Text-to-speech, • Speech-to-text (voice dictation) • Machine translation • Classification of text • Question answering (QA) Image source: Unspecified
  28. Chen & Decary, ITCH 2019 Image source:
  29. Chen & Decary, ITCH 2019 Steps Involved In rule-based NLP: 1. Morphological Processing: 1. Syntax Analyzing: 1. Semantic Analysis: 1. Pragmatic Analysis: Rule-based NLP Uses: ● Text Mining (entities, relationships) ● Classification ● Text summary ● Machine Translation ● Question and Answering ● Text creation ● Semantic search Semantic search is the transition “from being an information engine to a knowledge engine (Google, 2012)
  30. Chen & Decary, ITCH 2019 A cognitive-based semantic approach to NLP: • Using semantic rules in conjunction with a world model and cognitive frameworks to semantically analyze, rank, select, and extract web content from trusted sources; • Analyzing health information in relation to user’s goals, tasks and the information needs in specific situations; • Using knowledge models and rule-based NLP analysis as a base for creating patient-centered digital health technologies (e.g., smart medical search engine, medical voice chatbots) Cogilex’s rule-based NLP
  31. Chen & Decary, ITCH 2019 Key features of Cogilex’s semantic search technology 1. Identifying selfcare information related to: ● Disease entity > 25,000 ● Symptom entity > 4,500 ● Injury and accident entity > 1,500 ● Medical procedure entity > 9,500 ● Drug entity > 8,000 ● Other health related object classes 3. Using machine learning on big data to extract relationships among: ● Symptoms ● Tests ● Treatment modalities ● Drugs ● Dietary plans ● Etc.2. Classifying health information and generating knowledge maps to guide user’s search
  32. Chen & Decary, ITCH 2019 Seenso medical search engine: Analyzing web content from trusted sources and providing knowledge maps to guide user's search
  33. Chen & Decary, ITCH 2019 NLP applications in Healthcare ● IBM’s Watson Health ● Amazon Comprehend Medical ● Google Deepmind health ● United Health Group Inc.’s Optum ● MetaMap (NLM) ● cTakes (Mayo clinical text analysis and knowledge extraction system) ● Linguamatics ● CLAMP (Clinical Language Annotation, Modeling, and Processing) Toolkit Quotes
  34. Chen & Decary, ITCH 2019 IBM Watson Cognitive Computing The goal of cognitive analytics is to generate new insights using artificial intelligence (AI) and machine learning algorithms that can understand, reason and learn. Image source: Unspecified
  35. Chen & Decary, ITCH 2019 Amazon Comprehend Medical ● Support for entity extraction and entity traits on a vast vocabulary of medical terms: anatomy, conditions, procedures, medications, abbreviations, etc. ● An entity extraction API (detect_entities) trained on these categories and their subtypes. ● A Protected Health Information extraction API (detect_phi) able to locate contact details, medical record numbers, etc.
  36. Chen & Decary, ITCH 2019Image source: Unspecified
  37. Chen & Decary, ITCH 2019 ● Medical dictation for professionals: Nuance Dragon Medical Practice ● Voice interfaces for EHRs: an essential step to humanizing the EHRs ● AI Assistants for consumers AI Voice technology Mycroft: An open source AI voice assistant for Linux- based operating systems IBM Watson and NVIDIA AI Platforms
  38. Chen & Decary, ITCH 2019 Medical dictation for professionals: Nuance Dragon Medical Practice Nuance® Dragon® Medical Practice Edition 4 accurately translates the doctor's voice into a rich, detailed clinical narrative that feeds directly into the EHR. Improve documentation, boost efficiency, increase physician satisfaction, and eliminate transcription costs.
  39. Chen & Decary, ITCH 2019 General AI voice assistants AI Assistants for consumers Text-based medical chatbots Your.MDSensely Mycroft (open source) Orbita Voice IBM Watson Nuancen Conversa ● AI Assistants like Alexa, Siri, Cortana, and Google Assistant do general question-answer matching and do not perform tasks directly related to patient care. ● Health chatbots such as Babylon, Ada, and Buoy rely on text-based and mostly close-ended communications
  40. Chen & Decary, ITCH 2019 Big data in EHR: Quantitative Data Vital signs, diagnoses, diagnosis codes, laboratory results, medication Qualitative Data (80%) Clinical notes, medication order notes, discharge instructions Challenges -Many data types, many user types, large data size and high complex -Processing such data and generating knowledge is moving beyond unassisted human capacity EHR systems–the backbone of digital health transformation
  41. Chen & Decary, ITCH 2019 Barriers in using EHRs: For patients ● Complexity of EHR systems ● Patients’ lack of basic health literacy ● Patients’ preference to discuss their health issues with care providers in person (76% responders, Heath, 2018) ● Perceived limited use of EHR portals by patients (59% responders) (Heath, 2018) ● Physicians want EHRs to be designed to facilitate digital and mobile patient engagement For healthcare professionals ● AMA demands EHR overhaul, calls them 'poorly designed and implemented’, ● Studies indicates that typing and clicking consume more than half the workday for doctors, contributing to physician burnout ● The majority physicians think EHRs need a complete overhaul
  42. Chen & Decary, ITCH 2019 Next-gen EHRs: AI-powered EHR (1 of 2) From “systems of records” to “systems of intelligence” and “systems of engagement”: ● Better clinical decision support: ○ Diagnostic analytics using medical imaging; ○ Predictive analytics using big data; ○ Routine integration of medical imaging with other clinical data for triage and critical care monitoring, diagnostic interpretation, and treatment modification ○ Personalized treatment design: precision medicine and behavior modification ● Smarter search algorithms ● Full integrated NLP capacity for narrative health data (e.g., critical summary of patient info, physician’s notes);
  43. Chen & Decary, ITCH 2019 Next-gen EHRs: AI-powered EHR (2 of 2) ● Integrated voice technology -AI voice assistant for health professionals (e.g., clinical dictation, voice interface, question asking and answering) -AI voice assistants for productive patient engagement (e.g.,, AI nursing assistants/chatbots) ● Integrating data information from multiple sources (including mobile data) ● Population health monitoring and management (epidemiology, social determinants of healthcare) ● Mechanism for safety keeping -Preventing medication errors -Preventing harmful drug interaction -Preventing complications of treatment to pre-existing conditions ● Content and tools for productive patient engagement
  44. Chen & Decary, ITCH 2019 Benefits of machine learning for healthcare Deep learning has produced remarkable results for complex real-world problems that involve big data. It has the potential to provide data-driven, evidence-based clinical intelligence for advancing both biomedical research and service delivery across the full spectrum of healthcare. Koski (2018): • A new paradigm to derive insights on biological, diagnostic and therapeutic processes and behaviors from data • Accelerate the process of digesting and interpreting vast quantities of complex, diverse information • Enable new data-driven presentation, diagnostic, treatment, and management options
  45. Chen & Decary, ITCH 2019 Machine learning is not an all-purpose solution For tasks that require common-sense solutions or domain-specific expertise, and situations that are outside of the ML training dataset, machine learning is less applicable. ML weaknesses: ● Identifies superficial features and patterns, but lacks the understanding of meanings and concepts; ● Identifies correlations but not causal relations; ● Lacks common sense reasoning, general intelligence, and domain knowledge integration; ● Lacks explainability and it is hard to fix certain identified problems; ● Needs big data, machine learning models are as good as the training data (biases, noises, errors often exist in real-world data); ● Difficulty to generalize the finding beyond its training dataset. Limitations of machine learning for healthcare
  46. Chen & Decary, ITCH 2019 Key requirements for AI success in healthcare ● Understanding what a particular type of AI technology can or can’t do ● Specifying the context of its proper use ● Developing an AI strategy that will bring real value to your organization and being able to implement it ● Establishing performance standards: ○ Evaluation criteria ○ Performance measure ○ Pilot-implementations and validation ● Ensuring privacy, security, and ethics
  47. Chen & Decary, ITCH 2019 Medical imaging ● Mayo Clinic neuroradiologists are using AI to find genetic markers in MRI scans. ● Stanford researchers have developed an AI algorithm that can diagnose up to 14 types of medical conditions and is able to diagnose pneumonia from medical images. ● Memorial Sloan Kettering Cancer Center is collaborating with an imaging company to improve the diagnosis of prostate cancer. ● University of Warwick is using an AI system to analyze chest X-rays and spot patients who should receive immediate care ● Google’s DeepMind, is working to develop a commercialized deep learning CDS tool that can identify more than 50 different eye diseases – and provide treatment recommendations for each one. Examples of AI for healthcare
  48. Chen & Decary, ITCH 2019 Machine learning, voice technology and EHR integration ● Epic, Cerner Allscripts and others are building EHRs that feature automation analytics, voice dictation, and tools for patients ● IBM Watson health ● Apple mobile devices, data and EHR integration ● UK NHS Medical diagnosis and services using Babylon mobile app ● Google’s EHR analysis to forecast patient outcomes ● Canada Mackenzie Health EHR implementation that includes clinical voice documentation ● Mayo clinical text analysis and knowledge extraction system, voice integration in HER Examples of AI for healthcare
  49. Chen & Decary, ITCH 2019 AI platforms & services These platforms typically provide functionality for: • -Natural language processing, • -Image recognition, • -Question-answer matching, • -Voice recognition, • -Predictive analytics, etc. 1. Amazon web service 2. Google cloud 3. Microsoft Azure 5. IBM Watson 4. MonkeyLearn 6. NVIDIA Platform 7. Nuance Platform 8. OpenAI 9. SAS
  50. Chen & Decary, ITCH 2019 Discussion 1. Give an example of the best AI integration in healthcare 2. What are the priorities and challenges for AI in healthcare? 3. AI development and integration strategies: ○ Given the high complexity and costs involved in developing the next-gen EHRs, should each country build a national AI-powered EHR system? ○ Should the next-gen EHRs be built with incremental improvements or a complete overhaul? ○ What are the keys to interoperability and data exchange? ○ How can AI transform healthcare? To support the existing clinical workflows or new ways of practicing medicine? 4. You thoughts on the benefits, costs, and feasibility of developing AI in healthcare.
  51. Chen & Decary, ITCH 2019 Key References 1. Artificial intelligence in healthcare: past, present and future 2. Artificial intelligence, bias and clinical safety 3. AI in healthcare - not so fast? Study outlines challenges, dangers for machine learning 4. Artificial Intelligence and Machine Learning Workshop 5. AMA AI Policy 6. 3 charts show where artificial intelligence is making an impact in healthcare right now 7. AI and machine learning: What cuts hype from reality? Note: All references, including the unspecified image sources are to be completed