ABAIM Introductory Course


Dec 02 - 03 2022


9:00 am - 5:00 pm

The ABAIM AI in Medicine Introductory Course is a comprehensive, two-day course on basic concepts in artificial intelligence in clinical medicine and healthcare designed by a team of clinician-data scientists as well as clinicians with an AI focus and data scientists who are involved in healthcare. This course is for all who are interested in having an overall review of concepts and applications in artificial intelligence in clinical medicine and healthcare.

About the course:

  • The format is “flipped” in that much of the review will be faculty discussing (not presenting) slides
  • In addition, the attendees are encouraged to ask questions throughout the sessions
  • There will also be a “pre” and “post” review course assessment with half the number of questions (50 questions) as the certification assessment (110 questions).

This course as well as independent study will prepare any attendee for the ABAIM AI in Medicine Assessment and Certification (110 questions in 2 hours).

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Hourly Schedule

Day 1

9:00 - 10:15
Orientation to the ABAIM Artificial Intelligence in Medicine review cours I. Introduction to Artificial Intelligence
-Faculty and attendee introductions
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)
11:30 - 12:30
LUNCH SESSION (Data Science Project Workflow and Tips)
12:30 - 1:30
I. Introduction to Artificial Intelligence (Continued)
- History of Artificial Intelligence (Module 2.1), - History of Artificial Intelligence in Medicine (Module 3.1)
1:30 - 1:45
Short Break
1:45 - 3:00
II. Data Science and Artificial Intelligence in the Current Era
- Healthcare Data and Databases (Modules 4.1-4.4), - Machine and Deep Learning (Part I)(Modules 5.1-5.8)
3:00 - 3:30
Long Break
3:30 - 5:00
II. Data Science and Artificial Intelligence in the Current Era (continued)
- Machine and Deep Learning (Part II)(Modules 5.9-5.12)
5:00 - 6:00

Day 2

10:00 - 11:15
Review of Day 1, II. Data Science and Artificial Intelligence in the Current Era
- Machine and Deep Learning (Part III)(Modules 5.13-5.17) - Assessment of Model Performance - Issues with Machine and Deep Learning
11:15 - 11:30
Short Break
11:30 - 12:30
II. Data Science and Artificial Intelligence in the Current Era (continued)
12:30 - 1:30
Lunch Session (Ethics in AI in Healthcare)
1:30 - 2:30
II. Data Science and Artificial Intelligence in the Current Era (continued)
- Other Key Concepts in Artificial Intelligence (Modules 6.1-6.4)
2:30 - 2:45
Short Break
2:45 - 4:00
II. Data Science and Artificial Intelligence in the Current Era (continued), III. The Current Era of Artificial Intelligence in Medicine (continued), IV. The Future of Artificial Intelligence and Application in Medicine
- Clinician Cognition and AI in Medicine (Modules 7.1-7.6), - Artificial Intelligence in Subspecialties (Modules 8.1-8.5), - Key Concepts of the Future of AI (Modules 10.1-10.2), - The Future of Artificial Intelligence in Medicine (Module 11.1)


  • 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).

  • Candace Makeda Moore
    Candace Makeda Moore
    Research Software Engineer, Netherlands eScience Center

    A medical doctor and computer programmer, passionate about using her skills in data analytics to improve patient outcomes. As a programmer who worked for years in clinical medicine, I have great insight into the real problems to solve. Finding new insights that drive innovation to solve problems motivates me.
    My personal research includes programming in Python and R for texture analysis, radiomics, and various machine learning and statistical analysis of algorithms. Some programs I have made available on my GitHub (https://github.com/drcandacemakedamoore) . I have used my skills as a digital scultpor/3D printer to make medical imaging based illustrations (such as those on this profile) and educational toys for ill children and those who care for them. Some of my own work is showcased at cmhm.info

  • 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.

  • 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.

  • Scott Campbell
    Scott Campbell
    Founder, Zero Hour Medical; President, San Francisco Emergency Physician's Association; Advisor, American Board of Ai in Medicine

    Dr. Scott Campbell earned a BA in Political Science from the University of Michigan where he was a member of the varsity baseball team. He received his MsPH from the UCLA School of Public Health with a dual concentration in behavioral science and health education, and his MD from Columbia University College of Physicians and Surgeons. He began his medical career in the specialty of internal medicine, transplant and cardiopulmonary critical care at California Pacific Medical Center in San Francisco before transitioning to emergency medicine where he has been an attending emergency physician for The Permanente Medical Group of Northern California for over thirty years with the majority of that practice at the Kaiser San Francisco Medical Center.

    Scott has served a broad range of local, state, national and public-private health policy planning efforts in the United States, the United Kingdom and Canada focusing on focusing on patient throughput optimization and creative capacity carve-outs across multiple care delivery continuums. On behalf of Mayor Gavin Newsom, he led innovative teams to address emergency department overcrowding in San Francisco with solutions such as the SF McMillan Sobering, Medical Respite and Dore Psychiatric Centers, creation of a 311 call system, and updated 911 call triage processes as well as partnering with McKinsey & Co. as chairman of the SF Ambulance Diversion Task Force.

    Extensive emergency medicine leadership, analytic and epidemic modeling experience in the world-wide Covid-19 pandemic while serving as president of the San Francisco Emergency Physician’s Association inspired Scott to form the consulting service, Zero Hour Medical, blending his passion for Ai and machine learning with problem solving in emergency medicine, disaster preparedness and acute care situational awareness for payers, providers and enterprise leadership.

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