Use this presentation to: - learn about the historical roots of AI - learn about major events in the AI timeline - get an overview of some of the ways that AI is being used now in healthcare to facilitate provider-patient communication, mine medical records, assess patients, predict illness, suggest treatments, enable patient monitoring This presentation is updated for early 2024 and addresses AI's use in the creation of dis/misinformation and deepfakes, as well as the bias inherent in AI, brought on by the data sets used to train it.

1. Artificial Intelligence
&
Healthcare
Kimberley R. Barker, MLIS
Librarian for Belonging & Community Engagement
Health Sciences Library, University of Virginia

4. Learning Objectives
• Learn to define “Artificial Intelligence”
• Learn about the history of AI
• Learn the difference between AI and machine learning
• Learn about AI industry leaders
• Learn about popular current uses of machine learning in daily life
• Learn about current healthcare applications for AI
• Learn about barriers to AI
• Learn about bias and discrimination inherent in AI

5. AI: definition
•“…the theory and development of computer
systems able to perform tasks that normally
require human intelligence, such as visual
perception, speech recognition, decision-
making, and translation between languages.”

6. AI: a brief history
•An ancient idea
•Talos & Galatea

7. AI: a brief history, 2
• 1206- Al-Jazari creates a programmable orchestra of
mechanical human beings
• 1580- Rabbai Judah Loew Ben Bezalel (allegedly) creates
the Golem
• 1642- Blaise Pascal invents the first digital calculating
machine
• 1726- Jonathon Swift publishes Gulliver’s Travels à the
Engine:“a Project for improving speculative Knowledge by
practical and mechanical Operations ”

8. AI: a brief history 3
• 1818- Mary Shelley publishes Frankenstein; or The Modern Prometheus-
speculates on the ethics of creating life
• 1863- Samuel Butler speculates that machines will one day become
conscious and supplant humanity
• 1941- Konrad Zuse builds first working program-controlled computer
• 1945- Game theory is introduced in Theory of Games and Economic
Behavior; integral to development of modern AI
• 1950- Alan Turing introduces the idea of the Turing Test
• 1950- Isaac Asimov publishes The Three Laws of Robotics

9. AI: a brief history 4
• 1951- first working AI programs written; checkers and chess
• 1955- Arthur Samuels builds a program which learns how to play checkers
• 1956- Dartmouth College Summer AI Conference organized (term
“artificial intelligence” is coined)
• 1959- Jonathon McCarthy and Marvin Minsky found the MIT AI Lab
• Late 1950’s-early 1960’s- Margaret Masterson and colleagues design
semantic nets for machine translation
• 1960’s- Ray Solomonoff lays foundation of mathematical theory of AI

10. AI: a brief history, 5
• 1963- ANALOGY (written by Thomas Evans) demonstrates that computers
can solve the same analogy questions that are given on IQ tests
• 1965- ELIZA- interactive program that carries on a dialogue in English, on any
topic- the first chatbot
• 1965- DENDRAI, 10-year effort to to deduce the molecular structure of
organic compounds using scientific instrument data. First expert system
• 1966- Ross Quillian demonstrates semantic nets
• 1969- Shakey the Robot demonstrated animal locomotion, perception and
problem-solving

11. AI: a brief history 6
• Early 1970’s- Jane Robinson establishes a Natural Language
Processing Center
• 1973- Assembly Robotics Group builds Freddy Robot; capable of
using visual perception to locate and assemble models
• 1975- Marvin Minsky publishes “Frames”; brings together ideas
about schemas and semantic links
• 1979-the Stanford Cart; first computer-controlled automated
vehicle. Successfully navigated a room full of chairs
• 1986- robot cars from the University of Munich drove up to 55 mph
on empty streets

12. AI: a brief history 7
• 1986- Barbara Grosz and Candace Sidner create the first computation model
of discourse, establishing the field of research.
• 1997- Deep Blue chess machine defeats the (then) world chess champion,
Garry Kasparov.
• 1998- Furby released (first successful attempt at producing a type of A.I to
reach domestic market)
• 1998- Tim Berners-Lee publishes Semantic Web Road Map
• Late 1990’s- Web crawlers and other AI-based information extraction
programs become essential in widespread use of the Internet

13. AI: a brief history 8
• 2000- Nomad robot explores remote regions of Antarctica looking for
meteorite samples
• 2002- Roomba released (autonomous vacuum)
• 2004- DARPA Grand Challenge (prize money for autonomous vehicles)
• 2004- “Spirit” and “Opportunity” autonomously navigate Mars
• 2005- Recommendation technology based on tracking web activity brings
AI to marketing.
• 2005- Blue Brain- project to simulate the brain at molecular detail
• 2009- Google self-driving car

14. AI: a brief history 9
• 2010- Microsoft Kinect (machine learning for human motion capture)
• 2011- Watson defeats Jeopardy! champions
• 2011-2014- Siri, Google Now, Cortana (natural language; recommendations;
perform actions)
• 2013- NEIL (Never Ending Image Learner) released at Carnegie Mellon
University; constantly compared and analyzed relationships between
different images
• 2015- Hawking, Musk, Wozniak and 3,000 researchers in AI and robotics sign
open letter calling for ban on research of autonomous weapons

15. AI: a brief history 10
• 2016- June 16th, OpenAI publishes research on generative models
• 2017- Asilomar Conference on Beneficial AI; discussed AI ethics and
strategies for bringing about beneficial AI while avoiding risk from artificial
general intelligence.
• 2022- November 30th, OpenAI launches ChatGPT
• March 21, 2023 – Google launched Bard, its ChatGPT alternative

16. AI: a brief history, 11
• March 31, 2023 – Italy banned ChatGPT for collecting personal data
and lacking age verification during registration for a system that can
produce harmful content (rescinded in April, after meeting Italy’s
demands)
• May 16, 2023 – OpenAI CEO Sam Altman appears in a Senate
subcommittee hearing on the Oversight of AI, where he discusses
the need for AI regulation that doesn’t slow innovation.
• 2023- Summer- UVA Taskforce on AI releases its report

17. AI in Pop Culture
• C-3PO
• Skynet
• Baymax
• WOTAN (Doctor Who)
• Omnius (Dune)
• Cylons
• Transformers
• The Matrix

18. Neural Networks- crucial to development
•“A Neural Network is a computer system
designed to work by classifying
information in the same way a human brain
does. It can be taught to recognize, for
example, images, and classify them
according to elements they contain.”

19. Artistic interpretation of a neural network

20. Let’s talk about Chatgpt…
• What is it?
• Chat Generative Pre-Trained Transformer is a Large Language Model (LLM)
machine learning chatbot, created by AINOW and released in November 2022
• Why all the excitement?
• Chatgpt is the first chatbot of its kind to be able to write and CONVERSE
convincingly in English.
• How is it being used in the sciences?
• Write essays & talks
• Summarize literature
• Improve papers
• Identify research gaps
• Write computer code
• Perform statistical analyses

21. AI vs. Machine Learning
• AI is the broad category;
machine learning is one application of AI
• “AI is basically the intelligence –
how we make machines intelligent,
while machine learning is the
implementation of the computer methods
that support it.”
For examples, if the category
was pasta, tortellini would be
one type of pasta.

22. Machine Learning- definition
• “Machine learning is an application of artificial intelligence (AI) that
provides systems the ability to automatically learn and improve from
experience without being explicitly programmed. Machine learning
focuses on the development of computer programs that can access
data and use it learn for themselves.”
• *pattern recognition
• ML is really “start of the art” of AI; true AI isn’t a reality yet

23. Examples of Machine Learning
• Virtual Personal Assistants- Siri, Alexis, Cortana
• Traffic predictions
• Surveillance systems
• Computer vision à how Pinterest knows
which pins to recommend
• Facial recognition
• Chatbots- the Facebook kerfluffle
• Shopping recommendations

24. Examples of Machine Learning
• Purchase predictions
• Video games
• Self-driving cars
• Fraud detection
• News generation
• Pandora & Spotify

25. Industries affected by AI
Cybersecurity
Manufacturing
Transportation
Agriculture
Banking
Education
Healthcare
Marketing
Business
Defense

26. According to pwc’s research, AI’s
estimated potential contribution to
the global economy by 2030 will be
$15.7 trillion.

27. The Interactive Data
Explorer from pwc:
https://www.pwc.com/gx/en/i
ssues/data-and-
analytics/publications/artificial
-intelligence-
study.html#explorer

28. AI Industry Leaders

29. Why AI in healthcare?
•Save time and improve efficiency
•Shortage of clinicians
•Improve patient outcomes
•Strengthen security

30. Why AI now?
•The perfect storm
•Tons of data (big data)
•Robust algorithms
•Processing power

31. Barriers to use of AI in healthcare
•Dirty data (data management has to happen first)
•Silo’d data
•Lack of infrastructure/data management plan
•DMP should be predicated on International Data
Corporation (IDC) Third Platform Principles, which
are anchored by 4 areas:
•Big Data & Analytics
•Cloud
•Mobile
•Social

32. AI & Healthcare- Types (1)
• Robotic process automation (RPA): use of AI in computer
programs to automate administrative and clinical workflows;
improve patient experience
• Natural language processing (NLP): use of ML to understand
human language, verbal or written, and interpret
documentation, notes, reports, and published research

33. AI & Healthcare- Types (2)
• Machine learning (ML): training algorithms using data sets,
such as health records, to create models capable of
performing such tasks as categorizing information or
predicting outcomes.
• Deep learning: subset of machine learning that involves
greater volumes of data, training times, and layers of ML
algorithms to produce neural networks capable of more
complex tasks.

34. AI & Healthcare- Uses
• Predictive analytics/modeling
• Pattern recognition
• Disease detection
• Precision medicine
• Patient self-monitoring (builds on the “quantified self”)
• Scheduling

35. Predictive Analytics/Modelling
• Increase the accuracy of diagnoses
• Improve preventive medicine and public health
• Enhance personalized care
• Accurately predict insurance costs
• Streamline research and development with prediction models
• Guide drug development to deliver medications that meet public need
• Better patient outcomes

36. Pattern Recognitionàdisease detection
• Dermatology
• Skin cancer detection
• Radiology
• Arterys- AI assistant (1st FDA approval)
• Cardiology
• Heart sound recordings

37. Patient Monitoring
• CoMET
• “uses continuous monitoring and computer algorithms to create
a visual portrait of a patient’s risk of experiencing a serious
event over the next 12 hours.”
• Created by UVA cardiologist Dr. Randall Moorman

38. Patient Self-Monitoring, cont’d
•DeafAI- AI-based digital humans as sign language
interpreters
•Extended Visual Assistant (EVA)
•Voice-controlled eyewear for the visually impaired
•Uses machine learning to recognize objects, text, signs, etc, and verbally
describes what it sees

39. Patient Self-Monitoring
• Chronic and acute conditions; post-surgery
• PeerWell- AI for total joint replacement
• Patients begin using app before surgery. Patients receive customized
daily lessons and tasks which require them to input their results directly
into the app. Machine learning algorithm adjusts pre- and post-surgery
instructions based on patient input.
• AI apps for more common ailments
• Diabetes management
• Palliative care
• Congenital heart disease

40. Scheduling
• Hyro
• “Through a HIPAA-compliant API integrated with the health
system’s EMR, the AI assistant sources the patient’s records,
retrieves upcoming appointment information, and reschedules
end-to-end with zero human intervention.”

41. Other Uses
• Search huge amounts of data incredibly fast
• Elicit.org -
• Elicit can find relevant papers without perfect keyword
match, summarize takeaways from the paper specific to your
question, and extract key information from the papers.
(Elicit is built by Ought, a non-profit machine learning
research lab with a team of eight people distributed across
the Bay Area, Austin, New York, and Oristà
• LitSense - LitSense lets you search for sentences in more
than 30 million biomedical publications (from NLM)

42. The Dark Side of AI
•Discrimination/bias
•Deep fakes
• Doctored photos, videos, audio used to trick
people into giving money, or to spread political
dissension
•Spreading dis/misinformation online
• Using algorithmic micro-targeting on social media

43. AI and Discrimination
• AI is only as unbiased as:
• the people who program it (mostly men)
• Are they acting on conscious or unconscious bias?
• Are they considering diverse points-of-view?
• the data on which its trained (not curated)
• Deep neural networks for images most often trained on ImageNet
• Natural language processing algorithms trained on data sets scraped
from GoogleImages, GoogleNews, and Wikipedia
• the systems & institutions into which AI is deployed

44. AI & Ethics
• UNESCO
• Recommendation on the Ethics of Artificial Intelligence
• A comprehensive international framework to shape development and
use of AI
• Adopted in 2021
• Its four core values include:
• Respect, protection, and promotion of Human Rights and
Fundamental Freedoms and Human Dignity
• Living in peaceful, just, and interconnected societies
• Ensuring diversity & inclusiveness
• Environment and ecosystem flourishing

45. There’s a lot to learn about AI & Healthcare
• StanfordOnline’s Artificial Intelligence in Healthcare Program
• MIT xPro’s Artificial Intelligence in Healthcare: Fundamentals and
Applications
• Coursera
• AI for Good Specialization
• Advisory group to the MD Program Curriculum Committee at UVA
SOM- Dr. Andrew Parsons

46. Questions?
Please reach out: krb3k@virginia.edu

47. Resources
• Timeline of Artificial Intelligence
https://en.wikipedia.org/wiki/Timeline_of_artificial_intelligence
• History of Machine Learning
https://www.estory.io/timeline/view/JlYn6L/445/History_of_Machine_Learning
• “What Is The Difference Between Artificial Intelligence And Machine
Learning?” https://bit.ly/2jHOxFA
• Artificial intelligence in healthcare: transforming the practice of medicine-
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8285156/

48. Resources
• “Neural Network | Human Brain versus computer”
https://techbuf.com/human-brain-neural-network/
• ShieldAI- drones for combat situations https://www.shield.ai/
• Wordsmith- https://automatedinsights.com/wordsmith
• AI’s role in healthcare starts small, gets bigger”
https://bit.ly/2F93JZu
• “How AI is transforming healthcare and solving problems in 2017”
https://bit.ly/2qWvugp
• “Google, Fitbit, startups storm into healthcare AI”
https://bit.ly/2ryvJgn

49. Resources
• “Artificial intelligence messenger bot, Baymax”
https://www.medicaldesignandoutsourcing.com/artificial-intelligence-
messenger-bot/
• “’Big Hero 6': The Science Behind Baymax, Disney's Big, Soft Robot”
https://www.nbcnews.com/tech/gadgets/big-hero-6-science-behind-
baymax-disneys-big-soft-robot-n240241
• “No, Facebook Did Not Panic and Shut Down an AI Program That Was
Getting Dangerously Smart” https://gizmodo.com/no-facebook-did-not-
panic-and-shut-down-an-ai-program-1797414922
• “Google Self-Driving Cars Have Learned How to Interpret Cyclists' Hand
Signals” http://fortune.com/2016/07/06/google-self-driving-cars-cyclist/

50. Resources
• “Predictive analytics in health care using machine learning tools and techniques”
https://ieeexplore.ieee.org/document/8250771/
• “How artificial intelligence is revolutionizing the patient experience in healthcare”
https://www.telusinternational.com/articles/ai-patient-experience-healthcare/
• “'It Is Crazy!' The Promise and Potential Peril of ChatGPT”
https://www.medpagetoday.com/opinion/patientcenteredmedicalhome/102557
• Creating Artificial Intelligence 'In Full Color’ https://www.nursing.virginia.edu/news/ai-
ecosystem-williams-moorman/
•
• “These ER Docs Invented a Real Star Trek Tricorder”
https://www.nbcnews.com/mach/technology/these-er-docs-invented-real-star-trek-
tricorder-n755631
• “What Companies Are Winning The Race For Artificial Intelligence?”
https://www.forbes.com/sites/quora/2017/02/24/what-companies-are-winning-the-race-for-
artificial-intelligence/#34820637f5cd

51. Resources
• “Just a Few of the Amazing Things AI Is Doing in Healthcare”
https://singularityhub.com/2018/03/29/just-a-few-of-the-amazing-things-ai-is-
doing-in-healthcare/#sm.00000ffrfb4hgpe2xxzxbtpckn6ws
• “Artificial intelligence powers digital medicine”
https://www.nature.com/articles/s41746-017-0012-2
• “Man against machine: AI is better than dermatologists at diagnosing skin
cancer” https://www.eurekalert.org/pub_releases/2018-05/esfm-
mam052418.php
• "Contributed: Top 10 Use Cases for AI in Healthcare”
https://www.mobihealthnews.com/news/contributed-top-10-use-cases-ai-
healthcare
• Can Artificial Intelligence detect Melanoma?
https://www.mskcc.org/news/can-artificial-intelligence-detect-melanoma

52. Resources
• AI can be sexist and racist — it’s time to make it fair-
https://www.nature.com/articles/d41586-018-05707-8
• AINOW 2019 Report- https://ainowinstitute.org/AI_Now_2019_Report.pdf
• How AI-Enabled RPM Can Improve Healthcare Delivery-
https://www.americantelemed.org/blog/how-ai-enabled-rpm-can-improve-healthcare-
delivery/
• How Good Is That AI-Penned Radiology Report?- https://hms.harvard.edu/news/how-good-ai-
penned-radiology-report
• Pattern Recognition Power: Three Reasons AI Will Improve Clinical Care-
https://www.forbes.com/sites/forbestechcouncil/2022/03/15/pattern-recognition-power-
three-reasons-ai-will-improve-clinical-care/?sh=2125b3865e32

53. Resources
• Arterys Resources https://www.arterys.com/resources-library
• “The future is now? Zipline http://www.flyzipline.com/
• AITopics https://aitopics.org/search
• Nonhuman “Authors” and Implications for the Integrity of Scientific Publication and
Medical Knowledge https://jamanetwork.com/journals/jama/fullarticle/2801170
• Worldwide Artificial Intelligence Spending Guide
• https://www.idc.com/getdoc.jsp?containerId=IDC_P33198
• UNESCO Artificial Intelligence- https://www.unesco.org/en/artificial-intelligence