AI Primer for Everyone

Date

Jun 03 2022

Time

EDT
9:00 am - 5:00 pm

The ABAIM AI Primer for Everyone is a one-day virtual course designed for healthcare executives, industry partners, nurses, administrators, respiratory therapists, radiology techs, trainees, students, patients, and all stakeholders to gain a working knowledge of basic AI principles. This class is an ideal first step for anyone interested in AI.

This interactive, discussion-driven course will answer all of the following questions and more:

  • How can your organization deploy AI?
  • Which technologies will benefit your patients and boost your workflow?
  • How do you prepare yourself to make use of AI?
  • What are the differences between various emerging AI technologies?

Learning objectives:

  • How to set up an AI strategy and/or an AI center at your organization
  • How to evaluate different AI technologies
  • How to communicate with data scientists, vendors, and colleagues about AI
  • How to reduce bias and approach AI from an ethical framework
  • Become conversant in AI technologies
  • Learn to make decisions that will positively impact your patients and your organization

Register Now

Healthcare or industry executive – $250
Nurses/administrators/trainees – $100
Students and patients – $50
Healthcare Leaders Registration
CEO / Physician / Student / Etc
Radiology, Cardiology, etc.
This will be used to access course material on your ABAIM account
Please select the option that most closely describes you

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Hourly Schedule

Schedule - Times Listed in EDT

9:00 - 10:15
Orientation to the ABAIM Artificial Intelligence Primer
Faculty and Attendee Introductions and Welcome
10:15 - 10:30
Short Break
10:30 - 11:30
I. Introduction to Artificial Intelligence
- Basic Concepts of Artificial Intelligence (Modules 1.1-1.5) - History of Artificial Intelligence (Module 2.1) - History of Artificial Intelligence in Medicine (Module 3.1)
11:30 - 12:30
Lunch Session (TBA)
12:30 - 1:30
II. Data Science and Artificial Intelligence in the Current Era
- Healthcare Data and Databases (Modules 4.1-4.4) </br>- Machine and Deep Learning (Part I)(Modules 5.1-5.17)
1:30 - 1:45
Short Break
1:45 - 3:00
II. Data Science and Artificial Intelligence in the Current Era (continued)
- Other Key Concepts in Artificial Intelligence (Modules 6.1-6.4)
3:00 - 3:30
Long Break
3:30 - 5:00
II. Data Science and Artificial Intelligence in the Current Era (continued) </br>III. The Current Era of Artificial Intelligence in Medicine (continued)</br>III. The Current Era of Artificial Intelligence in Medicine (continued)</br>IV. The Future of Artificial Intelligence and Application in Medicine
- Clinician Cognition and AI in Medicine (Modules 7.1-7.6)</br>- Artificial Intelligence in Subspecialties (Modules 8.1-8.5)</br>- Key Concepts of the Future of AI (Modules 10.1-10.2)</br>- The Future of Artificial Intelligence in Medicine (Module 11.1)
5:00 - 6:00
Final Q&A and Networking

Speakers

  • Alfonso Limon
    Alfonso Limon
    Data Science Consultant

    Highly effective technical director with proven accomplishments in a variety of engineering and medical applications combining mathematics, physics, and machine learning. Primarily focused on delivering state-of-the-art solutions impacting competitive business advantage– over a decade of experience building teams, designing complex data-driven projects, leading timeline-driven research programs, building milestone consensus for investors, and pitching technical goals to venture capitalists.

  • Anthony Chang
    Anthony Chang
    Chair, ABAIM

    After his cardiology training at the Children’s Hospital of Philadelphia with his research interest in mathematics and chaos theory in biomedicine, Dr. Anthony Chang was an attending cardiologist in the cardiovascular intensive care unit of Boston Children’s Hospital and an assistant professor at Harvard Medical School. Throughout his career as a pediatric cardiac intensive care physician, he has been interested in applications of biomedical data to decision-making. He completed his Masters of Science (MS) in Data Science with a sub-specialization in artificial intelligence from Stanford School of Medicine. He is also a computer scientist-in-residence at Chapman University. He is currently the Chief Intelligence and Innovation Officer and Medical Director of the Heart Failure Program at Children’s Hospital of Orange County.

    He is the founder and medical director of the Medical Intelligence and Innovation Institute (MI3) that is supported by the Sharon Disney Lund Foundation. The institute, founded in 2015, is dedicated to the introduction and implementation of artificial intelligence in medicine and was the first institute of its kind in a hospital. He intends to build a clinician-computer scientist interface with a nascent society (the Medical Intelligence Society) and is the editor-in-chief of Intelligence-based Medicine, the accompanying journal for his book, Intelligence-Based Medicine: Artificial Intelligence and Human Cognition in Clinical Medicine and Healthcare. He is the organizing chair for Artificial Intelligence in Medicine (AIMed) meetings around the world, the largest and most comprehensive clinician-led meetings that focus on applications of artificial intelligence in medicine and the dean of the nascent American Board of Artificial Intelligence in Medicine (ABAIM).

  • Flora Wan
    Flora Wan
    Neuroinformatics Lead & Research Coordinator, Holland Bloorview Kids Rehabilitation Hospital

    Dual technical and project coordinator role leading a multi-site research project focused on collecting and analyzing data on children with cerebral palsy at paediatric rehabilitation hospitals across Ontario. Passionate about the possibilities in applying data science, AI and machine learning to develop healthcare solutions that can improve the quality of life for children with disabilities.

  • Howard Lei
    Howard Lei
    Sr. Data Scientist, CHOC Children's

    A demonstrated history of working in Machine Learning and software development. Skills: Python, Spark, Keras, Tensorflow, Pytorch, deep learning, speech processing, statistical algorithms, Docker, Kubernetes, GCP, AWS, SQL, C/C++. Ph.D. in Electrical Engineering and Computer Science from UC Berkeley. US Citizen.

  • Ioannis Kakadiaris
    Ioannis Kakadiaris
    Data Science Lead

    Award-Winning Inventor, Researcher, Mentor, Leader, & Expert Witness with over 23 years’ experience delivering creative, stakeholder-informed research coupled with value-driven AI, Computer Vision and Biometrics solutions. Founded and Directed a successful Research Center showcasing the ability to drive innovation and affect commercialization through careful strategic planning. Offers a broad perspective coupled with an incisive ability to discern the essence of complicated issues and develop pragmatic strategies that attain measurable results. Leader and team builder executing on the principal that bold, disruptive and impactful research originates from inspired, focused teams. Dr. Kakadiaris’ research has been covered by numerous national media outlets, including ABC News, and CBS News, Discovery Channel, National Public Radio, and NBC News.

  • May D. Wang
    May D. Wang
    Secretary, ABAIM

    Dr. May Dongmei Wang is Wallace H. Coulter Distinguished Faculty Fellow and full professor in Biomedical Engineering Dept. and School of Electrical and Computer Engineering at Georgia Institute of Technology (GT) and Emory University (EU). She is Director of Biomedical Big Data Initiative, Georgia Distinguished Cancer Scholar, Petit Institute Faculty Fellow, Kavli Fellow, AIMBE Fellow, IAMBE Fellow, IEEE Fellow, and Board of Directors in American Board of AI in Medicine. Dr. Wang received BEng from Tsinghua University China, and MS with PhD degrees from Georgia Tech. Her research is in Biomedical Big Data with AI-Driven Intelligent Reality (IR) for predictive, personalized, and precision health (pHealth). During 20+ years academic professorship and ~4 years industrial research, she published 270+ articles in referred journals and conference proceedings and delivered 260+ invited and keynote lectures. She is a recipient of Georgia Tech (GT) Outstanding Faculty Mentor Award and Emory University MilliPub Award (for a high-impact paper that is cited over 1,000 times). She organized IEEE Healthcare Summit on Integrating BHI and AI to Combat Pandemics, and IEEE-JBHI Special Issue on AI-driven Informatics, Sensing, Imaging and Big Data Analytics for Fighting the COVID-19 Pandemic.

    Dr. Wang is the Senior Editor for IEEE Journal of Biomedical & Health Informatics (J-BHI, Impact Factor 7.02), an Associate Editor for IEEE Transactions for BME, and IEEE Reviews for BME, a panelist for NIH CDMA Study Section, NSF Smart and Connect Health, Brain Canada, and multiple European countries. She was Emerging Area Editor for Proceedings of National Academy of Sciences (PNAS), 2014-2015 IEEE Engineering in Medicine and Biology Society (EMBS) Distinguished Lecturer, and was elected EMBS Vice Presidents twice. Dr. Wang has dedicated to growing Biomedical and Health Informatics (BHI) community and is chairing IEEE Biomedical and Health Informatics Technical Community and ACM Special Interest Group in Bioinformatics. She serves in IEEE Future Directions Committee and International Academy of Medical and Biological Engineering (IAMBE) Executive Committee.

    At Georgia Tech, Dr. Wang is in 2022 President Leading Women Program and 2021 Provost Emerging Leaders Program. She was 2018-2021 Carol Ann and David Flanagan Distinguished Faculty Fellow, 2015-2017 GT Biomedical Informatics Program Co-Director in Atlanta Clinical and Translational Science Institute (ACTSI). Before 2016, Dr. Wang was Director of Bioinformatics and Biocomputing Core in NIH/NCI-sponsored U54 Center for Cancer Nanotechnology Excellence, and Co-Director of GT Center of Bio-Imaging Mass Spectrometry for over 10 years. Dr. Wang’s research has been supported by NIH, NSF, CDC, VA, Georgia Research Alliance, Georgia Cancer Coalition, Shriners’ Hospitals for Children, Children’s Health Care of Atlanta, Enduring Heart Foundation, Coulter Foundation, Microsoft Research, HP, UCB, and Amazon.

  • Michael Finley
    Michael Finley
    Private Medical Educator
  • Orest Boyko
    Orest Boyko
    Co-Chair, ABAIM

    Orest B. Boyko MD PhD is an American Board of Radiology board certified radiologist with an additional ABR certificate of added qualifications in Neuroradiology.

    Dr Boyko has been an Associate Professor since 2007 at the University of Southern California in Los Angeles, California, and currently is part of the USC Bridge Institute housed in the USC Michelson Center for Convergent Bioscience on the main campus of USC.

    As a radiologist, Dr Boyko as been working in the AI research community as a physician consultant since December 2013 in multi-modal mining in healthcare in the research lab of Dr Tanveer Sayeda-Mahmood in the IBM Almaden Research Center 650 Harry Road, San Jose, CA 95120 which has developed natural language processing applications to the electronic health record to assist in clinical decision support. Dr Boyko has also been since its inception involved the currently concluding IBM Healthcare Initiative in Radiology called the IBM Medical Sieve Radiology Grand Challenge in initial cutting edge research development of a radiology and cardiology cognitive assistant.

    Recently, Dr Boyko accepted an invitation to chair the newly created Guerbet Digital Solutions AI Advisory Board and Guerbet initiate to create an AI community in conjunction with Applied Radiology.

    Dr Boyko is active in advancing learning initiatives for the field of AI and machine learning.

    Dr Boyko has co-authored over 100 papers and book chapters in the field of radiology, neuroradiology and MRI.

  • Robert Hoyt
    Robert Hoyt
    Clinical Lead

    Robert Hoyt MD received his undergraduate education at the University of Virginia and his medical education at Virginia Commonwealth University. While still on active duty in the US Navy he became involved with multiple implementations of health information technology. As a result of this experience and additional education at Stanford University, he created a Medical (Health) Informatics program at the University of West Florida in 2004. He noted early on that there were no current and practical textbooks on the subject, so he launched Health Informatics: Practical Guide in 2007. The seventh edition was published in 2018 and he just released his latest textbook Introduction to Biomedical Data Science in late 2019.

    Dr. Hoyt has also been involved with clinical and informatics research over the past decade and he has published and lectured extensively in these areas. He is a reviewer for multiple medical and informatics journals He became board certified in Clinical Informatics in 2015 and a Fellow of the American Medical Informatics Association in 2019.

    He is also involved in multiple community-oriented projects. He and his wife are a Big Couple for a local teenager, and both are Take Stock in Children mentors in the public-school system. They have supported two college scholarships annually for many years. Bob is the President of the Robert E Mitchell Foundation that supports Vietnam era prisoner of war research in Pensacola.

  • Sean Yee
    Sean Yee
    Research Administrator, Providence Health & Services
  • Tim McLerran
    Tim McLerran
    Co-Founder, Medical Intelligence One

    My mission is to care for patients based on their own deeply informative data, with wisdom derived from a partnership between human and machine intelligence, trained on data from billions of other humans.

    As a scientist, I have helped to develop and implement automated methods to measure thousands of molecules in thousands of blood samples using mass spectrometry. These thousands of molecules represent an imprint of the rich hierarchy of physiologic patterns. If the genome sets the stage, then the molecular composition of blood and other bio-specimens shines a spotlight on the actors.

    As a physician, I have seen that we treat people based on population averages, often trying many unnecessary and sometimes even harmful therapies on those who have access to care, while our system’s inefficiencies leave many others without any access to care.

    As a human, I know we can do better. This will take several key steps:

    • Build the technologies and infrastructure to:
    – Measure what matters in terms of health and disease
    – Aggregate data from enough people to leverage AI for clinical purposes

    • Build the policies and value networks necessary to:
    – Align incentives of payers and patients with the goal of long-term health

    The current state of healthcare screams for disruption. AI and big data are moving quickly to disrupt. I am taking a deep dive into the fray.

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