UHN Virtual Library: Generative AI at UHN

Visit UHN AI Hub Collaborative Centre to learn about:

• Gen AI Education
• The “signed-in version” Copilot Chat: UHN's approved GenAI 
• UHN Policies, Guidelines and Standards
• AI Governance at UHN
• Education and Learning

Copilot Chat, a Gen AI chat, is enabled and available to all of TeamUHN. Copilot Chat is endorsed and supported by UHN Digital, when used in a protected mode (requires signing in with the UHN credentials).

If you are not logged in with your UHN credentials (not in a protected mode), anything entered in the Copilot chat is NOT properly secured.

Even in the protected mode, for data safety, the best practice is to avoid using any Private or Confidential information, including Personal Health Information, employee information, research and education confidential data, corporate confidential information, or intellectual property. 

Accessing Copilot in a protected mode

• Microsoft 365 Desktop Icon
  Log in with your UHN credentials if prompted 
  

• Copilot Button in Teams 
  

• Copilot Chat Website
  Log in with your UHN credentials if prompted  
• Edge Browser
  • Sign into Edge browser by clicking on your Profile image/initials (top right corner) using your UHN credentials.  
  • Press the Copilot icon in the upper right corner of Edge to open Copilot in the Edge sidebar. The green shield icon at the top of the Copilot window confirm Copilot Chat is in a protected mode.

To maintain data security, look for the green shield icon  to verify Copilot Chat is in a protected mode.

Our library resources are made available through license agreements with many different vendors and publishers, each of whom set out different terms and conditions for using their content. UHN Library & Information Services is working with our vendors and publishers to secure the necessary rights for using licensed content in protected Gen AI tools, such as CoPilot Chat. At this time, many of our legal agreements restrict the use of licensed content in Gen AI tools. Before using library-licensed content (i.e. journal articles, eBooks, or other electronic materials found in our collection) in any Gen AI tool, please contact UHNLibraries@uhn.ca for guidance. 

To ensure the responsible use of Gen AI tools Goverment of Canada developed the “FASTER” principles:

Source: Government of Canada - Guide on the use of generative artificial intelligence

• Fair: ensure that content from these tools does not include or amplify biases and that it complies with human rights, accessibility, and procedural and substantive fairness obligations; engage with affected stakeholders before deployment
• Accountable: take responsibility for the content generated by these tools and the impacts of their use. This includes making sure generated content is accurate, legal, ethical, and compliant with the terms of use; establish monitoring and oversight mechanisms
• Secure: ensure that the infrastructure and tools are appropriate for the security classification of the information and that privacy and personal information are protected; assess and manage cyber security risks and robustness when deploying a system
• Transparent: identify content that has been produced using Gen AI; notify users that they are interacting with an AI tool; provide information on institutional policies, appropriate use, training data and the model when deploying these tools; document decisions and be able to provide explanations if tools are used to support decision-making
• Educated: learn about the strengths, limitations and responsible use of the tools; learn how to create effective prompts and to identify potential weaknesses in the outputs
• Relevant: make sure the use of Gen AI tools supports user and organizational needs and contributes to better outcomes for clients; consider the environmental impacts when choosing to use a tool; identify appropriate tools for the task; AI tools aren’t the best choice in every situation

Social and environmental impacts of developing Gen AI.

Source: Mohammad Hosseini, Peng Gao, Carolina Vivas-Valencia, A social-environmental impact perspective of generative artificial intelligence, Environmental Science and Ecotechnology, Volume 23, 2025, 100520, ISSN 2666-4984, https://doi.org/10.1016/j.ese.2024.100520.(https://www.sciencedirect.com/science/article/pii/S2666498424001340).

• Understand the Tool: Familiarize yourself with the capabilities and limitations of the AI tool. This includes understanding the type of tasks it can perform, the data it was trained on, and its ability to understand and generate content.
• Clear Instructions: When interacting with the AI, provide clear and concise instructions. The more specific you are with your request, the better the AI can generate the desired output.
• Iterative Process: Using AI tools is often an iterative process. If the initial output isn’t what you expected, refine your instructions and try again.
• Review Outputs: Always review the outputs generated by the AI. While AI can generate useful and creative content, it’s crucial to ensure the information is accurate and appropriate for your needs.
• Ethical Use: Be mindful of ethical considerations when using AI. This includes respecting copyright laws, avoiding generating harmful or offensive content, and considering the privacy implications of the data you’re working with.
• Continuous Learning: AI tools are continually evolving and improving. Stay updated with the latest advancements and updates to the tool to make the most of its capabilities.

Reminders when using any GenAI tool

• Always verify and validate the accuracy of returned results.
• Do not use any AI assistant for any type of clinical care related work, including note taking to put into a chart, generating summaries of patient information, or other. UHN has not validated the use of these tools for medical applications and teams are working on controlled pilots for such use cases.
• Never put Private or Confidential information, including patient data (PHI - Personal Health Information) employee information, research and education confidential data, corporate confidential information, or intellectual property in any AI tool. .

"Generative artificial intelligence (GAI) tools can enhance the quality and efficiency of medical research, but their improper use may result in plagiarism, academic fraud and unreliable findings. Transparent reporting of GAI use is essential, yet existing guidelines from journals and institutions are inconsistent, with no standardised principles." ‡  

If using GenAI in your medical research, you should be aware of the GAMER statement, that includes a checklist noting which aspects of GenAI should be reported in your research manuscript or proposal. 

‡Luo X, Tham YC, Giuffrè M, et al. Reporting guideline for the use of Generative Artificial intelligence tools in MEdical Research: the GAMER Statement. BMJ Evidence-Based Medicine Published Online First: 13 May 2025. doi: 10.1136/bmjebm-2025-113825 

Link to the GAMER Checklist. 

Reporting Guides/Checklists for Studies Including AI Processes 

You should also be aware of the reporting requirements for specific study types, such as TRIPOD-AI for prediction model evaluation, or CONSORT-AI for randomized controlled trials, and many more. 

For a full list of reporting guides that include Generative and other types AI, see the EQUATOR site.

• Persona — Who is asking for the information? Who do you want the AI to be?
• Task — What do you need Copilot on Edge to do for you?
• Context — Any additional information Copilot on Edge could use to generate a more specific response
• Output Format — How do you want Copilot on Edge to format its output (in bullet points, tables, images in a specific style, etc.)?

Prompt examples:

• What might international learners want to know?
  See the Copilot answer...

• As a health care professional, what might international learners want to know?
  See the Copilot answer...

• As a health care professional, what might international learners want to know about working in a hospital in Canada?
  See the Copilot answer...

• As a health care professional, what might international learners want to know about working in a hospital in Canada? Please provide the top 12 best answers.
  See the Copilot answer...

Algorithm: A set of rules or instructions given to an AI, neural network, or other machines to help it learn on its own.

Artificial Intelligence (AI) : The simulation of human intelligence processes by machines, especially computer systems.

Computer Vision: The ability of artificially intelligent systems to “see” like humans, interpreting and understanding visual input on the computer.

Data Mining: The process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.

Deep Learning: A subset of ML that makes the computation of multi-layer neural networks feasible. It is particularly useful for image and speech recognition tasks.

EHR (Electronic Health Records): Digital records of health information. They contain all the information you’d find in a paper chart — and a lot more.

Healthcare Analytics : The branch of analysis that refers to the use of data-driven findings in healthcare settings to enable healthcare professionals to make informed decisions.

IoMT (Internet of Medical Things) : A connected infrastructure of medical devices, software applications, and health systems and services.

Machine Learning (ML) : A type of AI that allows a system to learn from data rather than through explicit programming.

mHealth (Mobile Health) : Practice of medicine and public health supported by mobile devices.

Natural Language Processing (NLP) : The ability of a computer program to understand human language as it is spoken or written.

Neural Network: A series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates.

Predictive Analytics: The use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data.

Precision Medicine: An approach to patient care that allows doctors to select treatments that are most likely to help patients based on a genetic understanding of their disease.

Prompting: Gen AI prompting refers to the process of providing specific instructions or inputs to a generative artificial intelligence model to produce desired outputs. These prompts can be in the form of questions, statements, or any textual input that guides the AI to generate relevant and coherent responses, content, or solutions.

Reinforcement Learning: A type of machine learning where an agent learns to behave in an environment, by performing certain actions and observing the results.

Robotics: A field of engineering focused on the design and manufacturing of robots. Robots are often used to perform tasks that are difficult for humans to perform or perform consistently.

Supervised Learning: A type of machine learning where the AI learns from labeled training data and makes predictions based on that data.

Telehealth: The use of digital information and communication technologies, such as computers and mobile devices, to access health care services remotely and manage your health care.

Telemedicine: The use of technology to provide healthcare remotely.

Unsupervised Learning: A type of machine learning where AI learns from test data that is not labeled and responds to new situations.