This document provides an overview of artificial intelligence in medicine. It defines key concepts like artificial intelligence, machine learning, and deep learning. It discusses barriers to adoption of AI in healthcare as well as potential hazards. It provides examples of AI applications in areas like radiology, clinical decision support, and personalized medicine. It also addresses ethical concerns and discusses how AI may impact medical education and the healthcare workforce.

1. THE PATIENT
GUIDE TO
INTELLIGENCE-
BASED MEDICINE
ARLEN MEYERS, MD, MBA
EMERITUS PROFESSOR, UNIVERSITY OF COLORADO
SCHOOL OF MEDICINE
PRESIDENT AND CEO , SOCIETY OF PHYSICIAN
ENTREPRENEURS
CHIEF MEDICAL OFFICER, CYBERIONIX

2. HTTPS://YOUTU.
BE/MBFVPTEOS_
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3. HYPE OR
HOPE?

7. WHAT YOU
WILL
LEARN
The basic concepts of artificial intelligence in medicine
The barriers to dissemination and implementation
The hazards of using AI in medicine
Learn how AI is being used in medicine to improve quality,
access, your experience, doctor experience and business
processes at lower costs.

8. PLEASE DISCUSS
Google maps, Alexa,
customer service chat bots,
some mobile medical apps
and Smart TVs use artificial
intelligence.
What has been your
experience using these?
Concerns?

9. WHAT IS DIGITAL HEALTH?
THE APPLICATION OF
INFORMATION AND
COMMUNICATION
TECHNOLOGIES (ICTS) TO
EXCHANGE MEDICAL
INFORMATION FOR VARIOUS
INTENDED USES
• Telehealth
• Electronic medical records
• Big data and analytics
• Remote patient monitoring
• Patient reported outcomes
Virtual and augmented reality
Blockchain
Artificial intelligence
Mobile medical apps
Digital therapeutics

10. WHAT IS ARTIFICIAL INTELLIGENCE?
• There is no universal definition of artificial intelligence (AI). AI is generally
considered to be a discipline of computer science that is aimed at developing
machines and systems that can carry out tasks considered to require human
intelligence. Machine learning and deep learning are two subsets of AI. In
recent years, with the development of new neural networks techniques and
hardware, AI is usually perceived as a synonym for “deep supervised
machine learning”.

11. WHAT IS MACHINE LEARNING
• Machine learning uses examples of input and expected output (so called
“structured data” or “training data”), in order to continually improve and make
decisions without being programmed how to do so in a step-by-step
sequence of instructions. This approach mimics actual biological cognition: a
child learns to recognize objects (such as cups) from examples of the same
objects (such as various kinds of cups). Today application of machine
learning are widespread including email spam filtering, machine translation,
voice, text and image recognition.

12. WHAT IS DEEP LEARNING?
• Deep learning has evolved from machine learning. Deep learning uses a
plurality of AI algorithms (so called “artificial neural networks”) to recognize
patterns, hence being able to group and classify unlabeled data.

13. BARRIERS TO AI DISSEMINATION AND
IMPLEMENTATION
• There are four basic categories of barriers:
• 1) technical
• 2) human factors
• 3) environmental, including legal, regulatory, ethical, political, societal
and economic determinants and
• 4) business model barriers to entry.

14. HAZARDS AND LANDMINES
• Trust
• The Black Box problem: transparency
• Bias
• Regulatory, economic, ethical and societal issues

15. TRUST STANDARDS
• Known as ANSI/CTA-2090, "The Use of Artificial Intelligence in Health
Care: Trustworthiness" – considers what the association says are the
three key areas relating to how trust is created and maintained across
stakeholders.

16. HUMAN TRUST
• Human trust is concerned with developing "humanistic factors that
affect the creation and maintenance of trust between the developer
and users," according to CTA. "Specifically, human trust is built upon
human interaction, the ability to easily explain, user experience and
levels of autonomy of the AI solution."

17. TECHNICAL TRUST
• Technical trust is focused on design and training of AI and machine
learning systems, ensuring they "deliver results as expected."
Additionally, it considers data quality and integrity – such as
algorithmic bias, security, privacy, source and access.

18. REGULATORY TRUST
• Regulatory trust, meanwhile, is "gained through compliance by
industry based upon clear laws and regulations," said CTA, whether
that's from accreditation boards, regulatory agencies, federal and
state laws or international standardization frameworks.

20. PLEASE DISCUSS
What are your concerns
about the ethical use of AI
in medicine?
Suppose you were treated
by a doctor who used AI to
make a diagnosis which
turned out to be wrong.
What would you do?

21. USE CASES IN MEDICINE
Radiology and imaging Clinical decision support Personalized medicine Robotic process
automation
Patient education,
experience, engagement
and behavior change
enablement

22. VOCALISCHECK
• AI-based vocal biomarker company Vocalis Health received a CE
mark for its Covid-19 'voice check' tool. VocalisCheck is a software-
only product that collects a single voice sample from users simply by
counting from 50 to 70. The recording is transformed to a picture
(spectrogram) containing 512 features which are analysed by AI
machine learning. The tool was recently validated in a large clinical
study demonstrating an accuracy sensitivity and specificity above
80%, even among asymptomatic cohorts

23. IMAGE DETECTION OF SKIN CANCER
• AI can be taught to flag possible skin cancers on photos taken with
smartphone cameras—and the images can be ordinary “people shots”
rather than closeups of suspicious lesions. Using lesion classification
by experienced dermatologists as the ground truth, the researchers
found their system achieved sensitivity and specificity of right around
90% each when tasked with separating suspicious lesions from
benign skin discolorations and busy backgrounds.

24. PLEASE DISCUSS
• Will the use of artificial intelligence in medicine eliminate
doctors or other healthcare professionals?
• Why or why not? Would you want it to?
• How will it impact the future of medical education and
workforce needs?
• Will AI improve inequality or make it worse?

25. 7 LESSONS
• #1 AI investment has weathered Covid disruption
• #2 AI is in a process of democratization
• #3 New initiatives have high-level endorsement from C-Suite
• #4 Customer service will remain one of the key applications
• #5 Some use AI use cases are finding homes on other hype
cycles
• #6 Gartner recommends a focus on narrow use cases over AGI
• #7 The AI hype cycle is still relatively immature

26. THANK YOU