ACCA Version of AI & Healthcare: An Overview for the Curious

AI & Healthcare:
An Overview for the Curious
Kimberley R. Barker, MLIS
Librarian for Belonging & Community Engagement
Claude Moore Health Sciences Library
University of Virginia

LAND ACKNOWLEDGMENT
I respectfully acknowledge that
the University of Virginia
inhabits the unceded, traditional, and
current territory of the
Monacan Indian Nation.
https://www.monacannation.com/

LABOR ACKNOWLEDGEMENT
We must acknowledge that the University of Virginia-
its construction, growth, and development- was
made possible through the coerced labor of enslaved
Africans and African Americans. We are all indebted
to their sacrifice. We recognize that the legacies of
slavery are still present today and that racism
continues to shape our laws, cultures, and
institutions.
Credit: Meggan Cashwell, Ph.D. Adapted from Terah ‘TJ’ Stewart and the Mid-American Arts Alliance.

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.”.”

AI: An extremely brief history

AI: an extremely brief history (1)
•Ancient world
• Ideas & dreams - Talos & Galatea
•Middle Ages
• Practical applications-Al-Jazari & Rabbi Judah Loew Ben
Bezalel
•Early Modern Period
• Computers & literature- Pascal (first digital calculating
machine) & Swift (Gulliver’s Travels à the Engine:“a Project
for improving speculative Knowledge by practical and
mechanical Operations ”)

•1818
•Mary Shelley- Frankenstein
•One of the first modern instances of:
•publicly grappling with the ethics of creating
consciousness
•publicly worrying about what happens if your
creation turns on you

•1940s & 1950s
•Characterized by a blend of the theoretical and the practical
•Zuse builds first working program-controlled computer
•Theory of Games and Economic Behavior published; integral
to development of modern AI
•Turing Test introduced
•Asimov- The Three Laws of Robotics
•Samuels builds a program which learns how to play
checkers
•Dartmouth College Summer AI Conference organized
(term “artificial intelligence” is coined)
•MIT AI Lab founded

• 1960s- 1970s
• Lots of practical application; some (more) theory
• First chatbot (called ELIZA) created
• First computer-controlled vehicle (Stanford Cart)
• 1980s-1990s
• Began to truly see theory brought to fruition in terms of how we
think of AI currently
• Deep Blue beats Kasparov
• Web crawlers and other AI-based information extraction
programs become essential in widespread use of the Internet

• 2000s
• Characterized by increasingly sophisticated practical applications
• 2004- “Spirit” and “Opportunity” autonomously navigate Mars
• 2005- Recommendation technology based on tracking web activity
brings AI to marketing
• 2011- Watson defeats Jeopardy! champions
• 2011-2014- Siri, Google Now, Cortana (natural language;
recommendations; perform actions)
• 2022- November 30th, OpenAI launches ChatGPT
• 2024- January 9, Rabbit R1 launched

• 2000s, cont’d
• Worry about AI (manifests as ethical and legal hearings) gains more traction
• 2017- Asilomar Conference on Beneficial AI; discussed AI ethics and strategies for
bringing about beneficial AI while avoiding risk from artificial general intelligence.
• 2021- WHO Ethics and governance of artificial intelligence for health
• 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.
• Dec 2023- UN releases “Governing AI for Humanity”

What is “state of the art” AI?
Machine learning!

AI
vs.
Machine Learning
• AI is the broad category;
machine learning is one application of AI
• “The way I think of it is: AI is the science and
machine learning the algorithms that make
the machines smarter.The enabler for AI is
machine learning.”
-- Nidhi Chapel
For example, turmeric (ML) is a spice, but not
all spices (AI) are turmeric (ML).

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
*Large Language Models (LLMs)
Forms of “learning” are state- of- the- art of AI;
machine independent thought isn’t a reality yet

Why is AI pursued in healthcare?
•Save time & money
•Improve efficiency
•Shortage of workers
• Improve patient outcomes
• Strengthen security

Why is AI possible in healthcare now?
•The perfect storm
•Big data
•Robust algorithms
•Processing power

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

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

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.

AI & Healthcare- Uses
• Predictive analytics/modeling
• Pattern recognition
• Disease detection
• Precision medicine
• Patient self-monitoring
• Scheduling

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

Medical Tasks at AMII from the
Univ Alberta AI Medical Informatics Group
• “… actively engaged with many teams of medical
researchers, exploring ways to use patient data to produce
classifiers that can make accurate predictions about future
patients.These projects involve using various information
about a patient with the goal of predicting some relevant
property of that patient.We seek ways to "learn" these
predictors, from historical data, often augmented with other
prior biological data (such as metabolic or signaling
pathways).”

Pattern Recognition for disease detection
• Dermatology
• Skin cancer detection
• Radiology
• Arterys- AI assistant (1st FDA approval)
• Cardiology
• Heart sound recordings

Patient Monitoring & Self-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.”
• 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 illnesses/needs
• Diabetes management
• Palliative care
• Congenital heart disease

Scheduling & Searching
• 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.”
• 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)

AI & Cancer, 1
• Precision medicine
• Prediction
• Odds of developing & survival
• Diagnosis
• Ability to distinguish levels of aggression à triage, specific therapies, etc
• Treatment
• Decision treatment models
• Research
• Drug development

AI & Cancer, 2
• AI algorithm from Royal Marsden NHS foundation trust, the Institute
of Cancer Research, London, and Imperial College London
• “The model accurately graded the risk – or how aggressive a tumour is likely to be - of 82% of
the tumours analysed, while only 44% were correctly graded using a biopsy. The model also
accurately predicted the disease type of 84% of the sarcomas tested – meaning it can
effectively differentiate between leiomyosarcoma and liposarcoma - compared with
radiologists who were not able to diagnose 35% of the cases. - “AI better than biopsy at
assessing some cancers, study finds”
• “The results showed the AI model could identify each nodule’s risk of cancer with an
AUC of 0.87. The performance improved on the Brock score, a test currently used in
clinic, which scored 0.67. The model also performed comparably with the Herder
score – another test – which had an AUC of 0.83.”
- “New artificial intelligence tool can accurately identify cancer”
https://www.theguardian.com/society/2023/apr/30/artificial-intelligence-tool-identify-
cancer-ai

AI & Cancer, 3
• iStar (Inferring Super-Resolution Tissue Architecture)
• Imaging technique developed at UPenn’s Perelman SOM
• “…allow doctors and researchers to see cancer cells that might otherwise have been
virtually invisible. This tool can be used to determine whether safe margins were
achieved through cancer surgeries and automatically provide annotation for
microscopic images...”
• “…has the ability to automatically detect critical anti-tumor immune formations called
“tertiary lymphoid structures,” whose presence correlates with a patient’s likely
survival and favorable response to immunotherapy…”
-New AI Tool Brings Precision Pathology for Cancer and Beyond Into Quicker, Sharper Focus
Read the Brief Communication:
Inferring super-resolution tissue architecture by integrating spatial transcriptomics with histology
https://www.nature.com/articles/s41587-023-02019-9

• the data on which it’s trained
• the people who program it
• the systems & institutions into which AI is deployed
AI: only as unbiased as:

• Deep neural networks for images
most often trained on ImageNet
AI can be sexist and racist — it’s time to
make it fair. Nature. July 2018
AI: only as unbiased as the
data on which it’s trained:

AI can be sexist and racist — it’s time to make it fair. Nature. July 2018

• “Language models are more capable than ever, but also more
biased”
• “… new data shows that larger models are also more
capable of reflecting biases from their training data. A 280
billion parameter model developed in 2021 shows a 29%
increase in elicited toxicity over a 117 million parameter
model considered the state of the art as of 2018.”
- AI can be sexist and racist — it’s time to make it fair. Nature. July 2018
AI: only as unbiased as the data on which its
trained:

AI: only as unbiased as the data on which its
trained:
• Natural language processing algorithms trained on data sets scraped
from GoogleImages, GoogleNews, and Wikipedia
• Biased medical “artifacts”
• “…even when trained with data sets of thousands of images, the
AI model exhibited a pattern of underdiagnosis in underserved
and racial and ethnic minority groups”. – Ferryman, et al.

AI: only as unbiased as the people who program it
• “…the people building AI systems are not representative of the
people those systems are meant to serve. ”
• People working in AI in North America are predominantly:
• Male
• White
• Heterosexual
• Cisgender
Stanford’s Artificial Intelligence Index Report 2021, Chapter 6: Diversity in AI

AI & Bias
• “Female graduates of AI PhD programs in North America have accounted for
less than 18% of all PhD graduates on average…”
• “…in 2019, among new U.S. resident AI PhD graduates, 45% were white, while
22.4% were Asian, 3.2% were Hispanic, and 2.4% were African American.”
• “The percentage of white (non-Hispanic) new computing PhDs has changed
little over the last 10 years, accounting for 62.7% on average.”
• Membership survey by Queer in AI in 2020
• “…almost half the respondents said they view the lack of inclusiveness in the
field as an obstacle they have faced…”
• “More than 40% of members surveyed said they have experienced
discrimination or harassment as a queer person at work or school.”
Stanford’s Artificial Intelligence Index Report 2021, Chapter 6:
Diversity in AI

AI: only as unbiased as the systems & institutions
into which it is deployed (1):
• Healthcare
• the systems & institutions into which it is deployed
• “70% of physicians showed some level of implicit bias against Black people
and Hispanics/Latinos.
51% of physicians had moderate-to-strong levels of bias against
Hispanics/Latinos/Latinas.
42% of physicians had moderate-to-strong levels of bias against Black people.
- TABLE 1- Summary of Studies Included in the Systematic Review of Implicit
Racial/Ethnic Bias Among Health Care Professionals
- from “Implicit Racial/Ethnic Bias Among Health Care
Professionals and Its Influence on Health Care Outcomes:
A Systematic Review”

AI: only as unbiased as the systems & institutions
into which it is deployed (2):
“Journalists and commentators pinned the blame for racial bias on
Optum's algorithm. In reality, technology wasn't the problem. At issue
were the doctors who failed to provide sufficient medical care to the
Black patients in the first place. Meaning, the data was faulty because
humans failed to provide equitable care.
Artificial intelligence and algorithmic approaches can only be as accurate,
reliable and helpful as the data they're given. If the human inputs are
unreliable, the data will be, as well.”
- “AI could help remove bias from medical research and data”
Robert Pearl, October 06, 2021

AI: only as unbiased as the systems &
institutions into which it is deployed (3):
• Social Media
• “Automated Ableism: An Exploration of Explicit Disability Biases in Sentiment and
Toxicity Analysis Models”
• “We analyze sentiment analysis and toxicity detection models to detect the
presence of explicit bias against people with disability (PWD). We employ the
bias identification framework of Perturbation Sensitivity Analysis to examine
conversations related to PWD on social media platforms, specifically Twitter
and Reddit, in order to gain insight into how disability bias is disseminated in
real-world social settings.”

AI & Bias: What can we do to avoid it? (1)
• Question current data sets and demand better (more diverse) ones
• Normalize the use of equity tools
• Algorithmic Impact Assessments
• Ada Lovelace Institute series on AIAs in healthcare
• “This user guide describes the recommended algorithmic impact
assessment (AIA) process for teams seeking access to imaging data from
the proposed National Medical Imaging Platform (NMIP), for one of three
reasons:
1.To conduct research that uses NMIP imaging data.
2.To train a new medical product that uses NMIP imaging data.
3.To test an existing medical product on NMIP imaging data.”

• Normalize the use of equity tools (cont’d)
• AI Now
• REPORT: Advancing Racial Equity Through Technology Policy
• Aequitas
• “…an open-source bias audit toolkit for machine learning developers, analysts,
and policymakers to audit machine learning models for discrimination and bias,
and make informed and equitable decisions around developing and deploying
predictive risk-assessment tools.”
• Support Accessibility Standards
• AI version of Web Content Accessibility Guidelines (WCAG)
• Artificial Intelligence (AI) and Accessibility Research Symposium 2023

• Amplify BIPOC researchers and their work
• Timnit Gebru, , Ph.D.
• Meredith Broussard, Ph.D.
• Nicol Turner Lee, Ph.D.
• Renee Cummings , Ph.D.
• Mutale Nkonde , Ph.D.
• Fay Cobb Payton, Ph.D.
• Joy Buolamwini, Ph.D.
Why are Black women becoming the hidden figures in AI?
*Why are Black women becoming the hidden figures in AI?
https://thefulcrum.us/media-technology/black-women-in-ai
• Question the data sets on which AI tools are trained
• Advocate for AI practices that don’t worsen health disparities
• Educate whomever you can about the bad AND good related to AI

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
• Artificial Intelligence in Health Care Online Short Course
• Coursera
• AI for Good Specialization
• What’s happening at your institution? Should you be involved?

Common Questions
• Will AI replace jobs?
• No
• Jobs will be replaced by someone using AI.
• Should I be afraid of AI?
• No
• You should be wary of those who are already in power and want to use AI in the
way that they’ve used every other tool.

“We are an interdisciplinary and globally distributed AI
research institute rooted in the belief that AI is not
inevitable, its harms are preventable, and when its
production and deployment include diverse perspectives
and deliberate processes it can be beneficial. Our research
reflects our lived experiences and centers our
communities.”
-DAIR - https://www.dairinstitute.org/about/
Final thought

This Photo by Unknown Author is licensed under CC BY-
NC

Beyond AI Exposure: Which Tasks are Cost-
Effective to Automate with Computer Vision?
“Looking at computer vision, where the cost estimates for AI systems are
more developed, we find that most systems are cost effective to deploy
when single systems can be used across entire sectors or the whole
economy. Conversely 77% of vision tasks are not economical to automate
if a system can only be used at the firm-level. This contrast makes it clear
that the cost-effectiveness of AI models will likely play an important role
in the proliferation of the technology.”

The pendulum is always swinging
• Homemade vs. purchased from a store
• Handmade vs. mass-produced

ACCA Version of AI & Healthcare: An Overview for the Curious

Thank you!
Please get in touch if you have questions or
would just like to chat about AI & healthcare:
Kimberley R. Barker, MLIS
krb3k@virginia.edu
All resources used to create this presentation are on the following slides.

Resources- General
• 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
• Worldwide Artificial Intelligence Spending Guide
https://www.idc.com/getdoc.jsp?containerId=IDC_P33198

Resources: General
• “Neural Network | Human Brain versus computer” https://techbuf.com/human-brain-
neural-network/
• What is the AI ‘State of the Art’?- https://medium.com/60-leaders/what-is-the-ai-state-
of-the-art-b60227856cf2
• AITopics https://aitopics.org/search
• Rabbit R1- https://www.rabbit.tech/
• Beyond AI Exposure: Which Tasks are Cost-Effective to Automate with Computer
Vision?- https://futuretech-site.s3.us-east-2.amazonaws.com/2024-01-
18+Beyond_AI_Exposure.pdf

Resources- Healthcare
• Artificial intelligence in healthcare: transforming the practice of medicine-
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8285156/
• 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
• Overview of artificial intelligence in medicine-
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6691444/
• Development and Evaluation of a Smartphone-Based Chatbot Coach to Facilitate a
Balanced Lifestyle in Individuals With Headaches (BalanceUP App): Randomized
Controlled Trial. Sandra Ulrich, Andreas R Gantenbein, Viktor Zuber, Agnes Von WylJ
Med Internet Res. 2024 Jan 24; 26 e50132

• Artificial intelligence, chatbots and ChatGPT in healthcare—narrative review of
historical evolution, current application, and change management approach to
increase adoption- https://jmai.amegroups.org/article/view/8271/html
• Nonhuman “Authors” and Implications for the Integrity of Scientific Publication and
Medical Knowledge https://jamanetwork.com/journals/jama/fullarticle/2801170
• “Google, Fitbit, startups storm into healthcare AI” https://bit.ly/2ryvJgn
• “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

• “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/
• Logic-based mechanistic machine learning on high-content images reveals how drugs
differentially regulate cardiac fibroblasts
https://www.pnas.org/doi/10.1073/pnas.2303513121

• “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

• 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

Resources: Healthcare
• Artificial Intelligence in Radiology, Nat Rev Cancer. 2018 Aug; 18(8): 500–510.
doi: 10.1038/s41568-018-0016-5
• Medical Tasks at AMII from the Univ Alberta AI Medical Informatics Group-
https://docs.google.com/document/d/e/2PACX-
1vT__IJx7MIQLjNVPk7alrO7eKDHnBOT9PZCit63XopEzH89qsqkR3Tppe_DD1yu
U5nFKpiV-L2pdQO7/pub
• Medicine’s Lessons for AI Regulation-
https://www.nejm.org/doi/full/10.1056/NEJMp2309872

Resources- Bias
• Timnit Gebru
• Black in AI- https://blackinai.github.io/#/
• DAIR- https://www.dair-institute.org/
• “We’re in a diversity crisis”: cofounder of Black in AI on what’s poisoning algorithms
in our lives- bit.ly/3OIcNGv
• Meredith Broussard
• More Than a Glitch: Confronting Race, Gender, and Ability Bias in Tech
• Artificial Unintelligence: How Computers Misunderstand the World
• Nicol Turner Lee, Director – Center for Technology Innovation Brookings Institution
• Renee Cummings, WEF Data Equity Council & AI Governance Alliance
• Mutale Nkonde, Founding CEO, AI for the People
• Fay Cobb Payton, Visiting Scholar & Special Advisor on Inclusive Innovation, Rutgers
University
• Joy Buolamwini, Founder Algorithmic Justice League
• Unmasking AI

Resources- Bias
• Black women in AI: Building a more inclusive and equitable future-
https://www.brookings.edu/events/black-women-in-ai-building-a-more-inclusive-and-
equitable-future/
• AI for the People- https://aiforthepeopleus.org/
• Making AI more explainable to protect the public from individual and community harms-
Written statement to the U.S. Senate AI Insight Forum on Transparency, Explainability,
Intellectual Property, & Copyright- Nicol Turner Lee, November 29, 2023
• Considering Biased Data as Informative Artifacts in AI-Assisted Health Care-
https://www.nejm.org/doi/full/10.1056/NEJMra2214964

Resources- Bias
• Aequitas Bias & Fairness Audit Toolkit- http://aequitas.dssg.io/
• Trained AI models exhibit learned disability bias, IST researchers say- bit.ly/47LnezS
• Algorithmic impact assessment: user guide-
https://www.adalovelaceinstitute.org/resource/aia-user-guide/
• Automated Ableism: An Exploration of Explicit Disability Biases in Sentiment and
Toxicity Analysis Models Pranav Narayanan Venkit Mukund Srinath Shomir Wilson
https://trustnlpworkshop.github.io/papers/5.pdf
• WC3 WAI Artificial Intelligence (AI) and Accessibility Research Symposium 2023-
https://www.w3.org/WAI/research/ai2023/

Resources- Bias
• “Implicit Racial/Ethnic Bias Among Health Care Professionals and Its Influence on
Health Care Outcomes: A Systematic Review”- doi: 10.2105/AJPH.2015.302903
• Seyyed-Kalantari L, Zhang H, McDermott MBA, Chen IY, Ghassemi M. Underdiagnosis
bias of artificial intelligence algorithms applied to chest radiographs in under-served
patient populations. Nat Med 2021;27:2176-2182.
https://www.nature.com/articles/s41591-021-01595-0
• AI can be sexist and racist — it’s time to make it fair-
https://www.nature.com/articles/d41586-018-05707-8
• The AI Equity Lab: Identifying and Mitigating Online Biases-
https://www.brookings.edu/wp-content/uploads/2023/11/FINAL_AI-Equity-
Lab_December-4.2023.pdf

Resources: Reports & Regulations
• UN
• Governing AI for Humanity-
https://www.un.org/sites/un2.un.org/files/ai_advisory_body_interim_report.pdf
• WHO
• Regulatory considerations on artificial intelligence for health-
https://iris.who.int/handle/10665/373421
• Ethics & Governance of Artificial Intelligence for Health-
https://www.who.int/publications/i/item/9789240029200
• EU
• EU AI Act: first regulation on artificial intelligence- bit.ly/3HEsAlI
• The Center for Open Data Enterprise (CODE). (2019). Sharing And Utilizing
Health Data for A.I. Applications: Roundtable Report. U.S. Department of
Health and Human Services. https://www.hhs.gov/sites/default/files/sharing-
and-utilizing-health-data-for-ai-applications.pdf

Resources: Reports & Regulations
• THE AI INDEX REPORT: Measuring trends in Artificial Intelligence -
https://aiindex.stanford.edu/report/
• UNESCO Artificial Intelligence- https://www.unesco.org/en/artificial-intelligence
• AI Now
• Advancing Racial Equity Through Technology Policy-
https://ainowinstitute.org/publication/advancing-racial-equity-through-technology-
policy
• Algorithmic Impact Assessments Report: A Practical Framework for Public Agency
Accountability- https://ainowinstitute.org/publication/algorithmic-impact-assessments-
report-2
• Schwartz, R., Vassilev, A., Greene, K., Perine, L., Burt, A., & Hall, P. (2022). Towards a
Standard for Identifying and Managing Bias in Artificial Intelligence. U.S. Department of
Commerce, National Institute of Standards and
Technology. https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.1270.pdf

Resources: Academic
• Artificial Intelligence in Medicine-
• Journal of Medical Artificial Intelligence- https://jmai.amegroups.org/
• NEJM’s article series “AI in Medicine” - https://www.nejm.org/ai-in-medicine
• Artificial Intelligence at U of A- https://www.ualberta.ca/research/our-research/artificial-
intelligence.html
• Journal of Artificial Intelligence Research- https://www.jair.org/index.php/jair

Resources: Cancer
• Hollon TC, Pandian B, Adapa AR, et al. Near real-time intraoperative brain tumor
diagnosis using stimulated Raman histology and deep neural networks. Nat Med.
2020;26(1):52–58. doi: 10.1038/s41591-019-0715-9
• Mori Y, Kudo SE. Detecting colorectal polyps via machine learning. Nat Biomed Eng.
2018;2(10):713–714. doi: 10.1038/s41551-018-0308-9
• Matsuo K, Machida H, Shoupe D, et al. Ovarian conservation and overall survival in
young women with early-stage low-grade endometrial cancer. Obstet Gynecol.
2016;128(4):761. doi: 10.1097/AOG.0000000000001647
• Liu B, He H, Luo H, Zhang T, Jiang J. Artificial intelligence and big data facilitated
targeted drug discovery. Stroke Vasc Neurol. 2019;4(4):206–213. doi: 10.1136/svn-2019-
000290
• A CT-based radiomics classification model for the prediction of histological type and
tumour grade in retroperitoneal sarcoma (RADSARC-R): a retrospective multicohort
analysis. The Lancet: Oncology. 2023; 24(11): 1277-1286. DOI: https://doi.org/10.1016/S1470-
2045(23)00462-X

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