This document summarizes a workshop presentation on AI in healthcare. It begins by discussing the hype around AI and how it has not yet delivered many results. It then outlines some challenges to using AI in healthcare like a lack of understanding of what AI can do, poor implementation strategies, and a shortage of trained workforce. The objectives of the workshop are then stated as understanding AI's real potential and how to invest wisely. Various AI technologies like machine learning, natural language processing, and voice technology are described. Key requirements for successful AI include understanding its limitations and developing a strategy to bring real value.
10 Common Applications of Artificial Intelligence in HealthcareTechtic Solutions
List of 10 Common Applications of Artificial Intelligence that explain how artificial intelligence is used in healthcare and why it is necessary? To read briefly all common applications of artificial intelligence in healthcare then visit at https://www.techtic.com/blog/applications-of-ai-in-healthcare/
Artificial Intelligence In Medical IndustryDataMites
Medical artificial intelligence (AI) mainly uses computer techniques to perform clinical diagnoses and suggest treatments. AI has the capability of detecting meaningful relationships in a data set and has been widely used in many clinical situations to diagnose, treat, and predict the results.
visit : https://datamites.com/artificial-intelligence-course-training-pune/
Patients are about to see a new doctor: artificial intelligence by EntefyEntefy
The health care industry has already seen advanced artificial intelligent systems make an impact in areas like medical diagnosis and patient care. But the long-term big-picture importance of AI in medicine may be something else entirely: a potential fix for the intractable problem of too few doctors and nurses worldwide. And as part of that, a solution to health care’s public enemy number one—paperwork.
Entefy curated a presentation based on our article about the impact of artificial intelligence in medical care. These slides provide a snapshot of how AI is at use in medical care today, the advances and limits of current AI systems, and AI’s potential in patient care. The presentation contains useful data and analysis for anyone interested in the intersection of AI and medical care.
For additional analysis and links to our background sources, read “Patients are about to see a new doctor: artificial intelligence" on our blog at https://blog.entefy.com/view/298/Patients-are-about-to-see-a-new-doctor-artificial-intelligence.
The use of artificial intelligence in healthcare has the potential to assist healthcare providers in many aspects of patient care and administrative processes as well as improve patient outcomes.
AI analyzes data throughout a healthcare system to mine, automate and predict processes. Some of the use cases are :
1. Early Diagnosis of diseases
2. Improved clinical trial processes
3. Mental health apps etc.
10 Common Applications of Artificial Intelligence in HealthcareTechtic Solutions
List of 10 Common Applications of Artificial Intelligence that explain how artificial intelligence is used in healthcare and why it is necessary? To read briefly all common applications of artificial intelligence in healthcare then visit at https://www.techtic.com/blog/applications-of-ai-in-healthcare/
Artificial Intelligence In Medical IndustryDataMites
Medical artificial intelligence (AI) mainly uses computer techniques to perform clinical diagnoses and suggest treatments. AI has the capability of detecting meaningful relationships in a data set and has been widely used in many clinical situations to diagnose, treat, and predict the results.
visit : https://datamites.com/artificial-intelligence-course-training-pune/
Patients are about to see a new doctor: artificial intelligence by EntefyEntefy
The health care industry has already seen advanced artificial intelligent systems make an impact in areas like medical diagnosis and patient care. But the long-term big-picture importance of AI in medicine may be something else entirely: a potential fix for the intractable problem of too few doctors and nurses worldwide. And as part of that, a solution to health care’s public enemy number one—paperwork.
Entefy curated a presentation based on our article about the impact of artificial intelligence in medical care. These slides provide a snapshot of how AI is at use in medical care today, the advances and limits of current AI systems, and AI’s potential in patient care. The presentation contains useful data and analysis for anyone interested in the intersection of AI and medical care.
For additional analysis and links to our background sources, read “Patients are about to see a new doctor: artificial intelligence" on our blog at https://blog.entefy.com/view/298/Patients-are-about-to-see-a-new-doctor-artificial-intelligence.
The use of artificial intelligence in healthcare has the potential to assist healthcare providers in many aspects of patient care and administrative processes as well as improve patient outcomes.
AI analyzes data throughout a healthcare system to mine, automate and predict processes. Some of the use cases are :
1. Early Diagnosis of diseases
2. Improved clinical trial processes
3. Mental health apps etc.
From traffic routing to self-driving cars, Alexa to Siri, AI’s reach is extending into all areas of life, including healthcare. Join Kimberley to learn more about how AI is being used now, and will be used in the near future, to facilitate provider-patient communication, mine medical records, assess patients, predict illness, suggest treatments, and so much more.
Artificial Intelligence in Health Care 247 Labs Inc
This presentation was shown at the Artificial Intelligence in Health Care event in Toronto Nov 16 2017. The discussion was to introduce various applications of artificial intelligence and machine learning in the health care field.
5 Powerful Real World Examples Of How AI Is Being Used In HealthcareBernard Marr
Healthcare can be transformed with the innovation and insights of artificial intelligence and machine learning. From robot-assisted surgery to virtual nursing assistants, diagnosing conditions, facilitating workflow and analyzing images, AI and machines can help improve outcomes for patients and lower costs for providers.
Artificial Intelligence (AI) is shaping and reshaping every industry under the sun. The Healthcare industry is not any exception.
In this presentation, I have discussed the basics of AI as well as how it is being used in various branches of the healthcare industry. I presented this topic in my departmental seminar in October 2021 and received appreciation as well as positive feedback in this regard.
artificial intelligence in health care. how it is different from traditional techniques. growth of artificial intelligence. how hospitals are taping artificial intelligence to mange corona virus. pros and cons of artificial intelligence.
Artificial intelligence enters the medical fieldRuchi Jain
In the medical and health field, artificial intelligence can help reduce the cost of ongoing health operations, and can have an impact on the quality of medical care for patients everywhere. By diagnosing diseases earlier, AI can also improve patient outcomes. No matter how you look at it, artificial intelligence has great potential in healthcare.
Healthcare AI will undoubtedly become one of the fastest growing industries in the industry. Although the medical and health artificial intelligence industry was valued at US$ 600 million in 2014 , it is expected to reach a staggering US$ 150 billion by 2026. There are countless AI applications in the healthcare industry, let’s look at some outstanding ones.
Artificial intelligence in health care by Islam salama " Saimo#BoOm "Dr-Islam Salama
A Lecture about basics and concepts of Artificial Intelligence in health care & there applications
محاضرة عامة حول الذكاء الإصطناعي وأساسياته في الرعاية الصحية والطبية وتطبيقاته
Short overview over possibilities and challenges of using artificial intelligence in health care. Presentation from the MultiHelix ThinkTank, May 14 2020.
When it comes to AI use for prediction, diagnosis and treatment of medical conditions, reality is often replaced with a hype. Limitations should be known. A review of AI failures and challenges in healthcare showing why it is not likely for algorithms to replace physicians in the nearest future.
This talk gives an introduction about Healthcare Use cases - The AI ladder and Lifestyle AI at Scale Themes The iterative nature of the workflow and some of the important components to be aware in developing AI health care solutions were being discussed. The different types of algorithms and when machine learning might be more appropriate in deep learning or the other way will also be discussed. Use cases in terms of examples are also shared as part of this presentation .
Generative AI in Healthcare Market - Copy - Copy.pptxGayatriGadhave1
Generative AI holds significant promise in healthcare, there are also challenges related to data privacy, model interpretability, and regulatory compliance that need to be addressed. Ethical considerations and thorough validation processes are crucial to ensure the responsible and safe application of generative AI techniques in healthcare.
From traffic routing to self-driving cars, Alexa to Siri, AI’s reach is extending into all areas of life, including healthcare. Join Kimberley to learn more about how AI is being used now, and will be used in the near future, to facilitate provider-patient communication, mine medical records, assess patients, predict illness, suggest treatments, and so much more.
Artificial Intelligence in Health Care 247 Labs Inc
This presentation was shown at the Artificial Intelligence in Health Care event in Toronto Nov 16 2017. The discussion was to introduce various applications of artificial intelligence and machine learning in the health care field.
5 Powerful Real World Examples Of How AI Is Being Used In HealthcareBernard Marr
Healthcare can be transformed with the innovation and insights of artificial intelligence and machine learning. From robot-assisted surgery to virtual nursing assistants, diagnosing conditions, facilitating workflow and analyzing images, AI and machines can help improve outcomes for patients and lower costs for providers.
Artificial Intelligence (AI) is shaping and reshaping every industry under the sun. The Healthcare industry is not any exception.
In this presentation, I have discussed the basics of AI as well as how it is being used in various branches of the healthcare industry. I presented this topic in my departmental seminar in October 2021 and received appreciation as well as positive feedback in this regard.
artificial intelligence in health care. how it is different from traditional techniques. growth of artificial intelligence. how hospitals are taping artificial intelligence to mange corona virus. pros and cons of artificial intelligence.
Artificial intelligence enters the medical fieldRuchi Jain
In the medical and health field, artificial intelligence can help reduce the cost of ongoing health operations, and can have an impact on the quality of medical care for patients everywhere. By diagnosing diseases earlier, AI can also improve patient outcomes. No matter how you look at it, artificial intelligence has great potential in healthcare.
Healthcare AI will undoubtedly become one of the fastest growing industries in the industry. Although the medical and health artificial intelligence industry was valued at US$ 600 million in 2014 , it is expected to reach a staggering US$ 150 billion by 2026. There are countless AI applications in the healthcare industry, let’s look at some outstanding ones.
Artificial intelligence in health care by Islam salama " Saimo#BoOm "Dr-Islam Salama
A Lecture about basics and concepts of Artificial Intelligence in health care & there applications
محاضرة عامة حول الذكاء الإصطناعي وأساسياته في الرعاية الصحية والطبية وتطبيقاته
Short overview over possibilities and challenges of using artificial intelligence in health care. Presentation from the MultiHelix ThinkTank, May 14 2020.
When it comes to AI use for prediction, diagnosis and treatment of medical conditions, reality is often replaced with a hype. Limitations should be known. A review of AI failures and challenges in healthcare showing why it is not likely for algorithms to replace physicians in the nearest future.
This talk gives an introduction about Healthcare Use cases - The AI ladder and Lifestyle AI at Scale Themes The iterative nature of the workflow and some of the important components to be aware in developing AI health care solutions were being discussed. The different types of algorithms and when machine learning might be more appropriate in deep learning or the other way will also be discussed. Use cases in terms of examples are also shared as part of this presentation .
Generative AI in Healthcare Market - Copy - Copy.pptxGayatriGadhave1
Generative AI holds significant promise in healthcare, there are also challenges related to data privacy, model interpretability, and regulatory compliance that need to be addressed. Ethical considerations and thorough validation processes are crucial to ensure the responsible and safe application of generative AI techniques in healthcare.
Queen's Grand Rounds - Artificial Intelligence at ASTRO 2019 (Nov 14 2019)Ian Pereira
I was asked to help with grand rounds recently. This is the deck I put together detailing my AI experiences at ASTRO 2019.
Thanks to ASTRO and all presenters for addressing this interesting topic.
Looking forward to seeing more at future ASTROs and beyond!
Clinical Trial Design and Artificial Intelligence | Pepgra.comPEPGRA Healthcare
Clinical trials take up the last half of the 10 – 15 year, 1.5 – 2.0 billion USD, cycle of development just for introducing a new drug within a market.
1. AI and its Evolution
2. AI in Clinical Trials
To Continue Reading: https://bit.ly/2W01UDQ
Contact Us:
Website : https://bit.ly/33Fwsye
Email us: sales.cro@pepgra.com
Whatsapp: +91 9884350006
Artificial Intelligence Service in HealthcareAnkit Jain
It is no secret that artificial intelligence is shaping new business landscapes in every industries. As one of emerging convergence technologies, Artificial Intelligence (AI) creates new products and services, finally innovating business models. Especially, it has been noted by industry experts and researchers that healthcare sector has the biggest potential of AI convergence. In fact, major technology companies including Google, Microsoft and IBM have invested in AI in healthcare sector. Thousands of AI startups are active launching innovative services related to healthcare.
This presentation summarizes our research on 40 companies from around the world that are leveraging Artificial Intelligence to improve the Healthcare Industry. They are all well-funded, have highly qualified CEOs & Boards, and are poised to achieve their product development milestones.
The I-Square Ventures proprietary rating algorithm indicates that almost all of these companies will receive more funding, and/or be acquired by larger companies.
HealthXL: How Artificial Intelligence (AI) Can Improve Research & Care Models...Maeve Lyons
Artificial Intelligence in Pharma and Care Delivery Delivering on the Promise.
Earlier this week we published a blog about the state of AI in pharma and care delivery, and we also mentioned we’d be launching an accompanying report. We’re happy share our initial report presentation as part of HealthXL’s Big Data & AI Working Group above.
In the future, HealthXL’s Working Group will go deeper into use cases and discuss other relevant industry topics such as best practices for acquiring quality data, regulatory guidelines for AI solutions, leading academic centers, and much more!
https://healthxl.co/report-artificial-intelligence-pharma-care-delivery-delivering-promise/
AI Innovation in the Pharmaceutical Sector - Accelerating ResearchDaniel Faggella
This is a deck I presented at the OECD's International Symposium on Machine Learning and Artificial Intelligence in Mexico City, October 2019.
The presentation draws from a number of AI executive interviews and in-depth research on AI innovation in pharma R&D, much of which is available on Emerj: https://emerj.com/?s=pharma
Changing Medical profession with Artifical Intelligence what it means to us Dr.T.V.Rao MD
•Artificial Intelligence fast penetrating to every system and modality of human living However the implications of Artificial Intelligence is truly different from other professions we should be more aware of the ongoing matters and chose what is good in Human and health care ?
•Dr.T.V.Rao MD
•Former professor of Microbiology
•Adviser and Member Associate Elsevier research Netherlands
Artificial Intelligence in Pharma and Care Delivery- Delivering on the PromiseJulie Carty
The report focuses on the state of Artificial Intelligence (AI) in pharma and care delivery today
Check out our accompanying blog https://tincture.io/artificial-intelligence-in-pharma-and-care-delivery-8e5bf0a5ae8b
HealthXL Artificial Intelligence Working Group ReportHanna Phelan
In 2017, the promise of Artificial Intelligence (AI) to transform the healthcare industry is as bold as ever, despite growing confusion over the state of progress, adoption, and competition in this rapidly growing field. HealthXL recently commissioned a brief exploration on this topic as part of its Big Data & AI Working Group.
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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
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
https://hbr.org/2018/05/10-promising-ai-applications-in-health-care
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
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
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
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
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.
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?
https://www.healthcareitnews.com/projects/ai-and-machine-learning
Note: All references, including the unspecified image sources are to be
completed