Try AI: Experiment Wisely!
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Elicit.orgUses language models to help support different parts of the research workflow, like refining your question, find papers, summarize key findings, and extract data.
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Semantic ScholarA free, AI-powered research tool for scientific literature, based at the Allen Institute for AI.
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PerplexityAn AI-chat-based conversational search engine that can help you find information by providing suggestions and sources in response to your questions.
Don't Trust, Verify!
It's crucial to always verify information from AI tools with external sources as they can make up or misrepresent information.
Read more about AI hallucinations.
Need Help?
Library staff may be able to recommend additional resources and help to find you answers to your AI questions.
Contact your library site or email and we'll be happy to assist you!
AI in Medicine Ebooks
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Ethics of Medical AI (2024)
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An overview of the ethical implications of AI-related technologies in medicine. This book explores how these technologies transform practices, relationships and environments in the clinical field. -
Internet of Medicine for Smart Healthcare (2025)
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This book provides in-depth explanations and discussions of the latest applications of Artificial Intelligence (AI), machine learning and the Internet of Medicine. -
Future Care (2023)
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Future care explores the upswell of virtual care, the evolving role of sensors, and the impact of artificial intelligence in medicine and health care through cutting-edge science, big idea projections, and patient stories. -
The Digital Reconstruction of Healthcare (2021)
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The complex challenges facing healthcare require innovative solutions that can make patient care more effective, easily available, and affordable. One such solution is the digital reconstruction of medicine that transitions much of patient care from hospitals, clinics, and offices to a variety of virtual settings. -
The Impact of Algorithmic Technologies on Healthcare (2025)
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Provides an in-depth account of how technologies such as artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT) are reshaping healthcare, transitioning from traditional diagnostic and treatment approaches to data-driven solutions that improve predictive accuracy and patient outcomes. -
Empowering Nurses with Technology (2025)
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Covers the basics of healthcare technology, including electronic health records (EHRs), telehealth, and mobile health applications, as well as practical advice for implementing technology in nursing workflows. -
Cybersecurity in Healthcare Applications (2025)
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Examines how artificial intelligence can be leveraged to detect and mitigate cyber threats in healthcare systems and environments.
Recently Published AI Research
The following feed is a PubMed search using the following MeSH headings:
- Artificial Intelligence
- Machine Learning
- Natural Language Processing
- Medicine
- Health Services Research
- Delivery of Health Care
- Medical Informatics
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No AHS data, business information, or sensitive information should ever be used or fed into any third-party AI tool.
Learn about the best practices when using third-party AI tools and AHS' use of Microsoft Copilot.
AI Technologies
What is AI?
While the field of AI has been around for a while, the recent developments in deep learning and neural networks have accelerated our ability to leverage vast amount of data to build better models. These models in turn, became better at things like problem solving and pattern recognition and began to set new benchmarks for natural language abilities. Many companies are now exploring what else this technology can offer to our lives, whether that be in work or at home. In libraries and for researchers, AI technologies hold a lot of promise to support our abilities to find, generate, analyze, apply, and spread knowledge. This guide presents some videos, tools, and tips to help you learn about some of these emerging innovations.
Recommended Reads - Basics of AI
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What is AI? A simple guide to help you understand AIIn this beginner's guide from the BBC, you'll venture beyond chatbots to discover various types of AI - and see how these new digital tools are already playing a part in our lives.
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What is artificial intelligence (AI)?A short primer on AI from IBM that touches on the different types of AI (weak vs strong), deep learning, a history, and some key AI applications.
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Elements of AIThe Elements of AI is a series of free online courses created by MinnaLearn and the University of Helsinki. You'll learn what AI is and what can (and can’t) be done with AI. The courses combine theory with practical exercises and can be completed at your own pace.
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Microsoft's AI Skills Navigator - AI FluencyA learning path that is designed to provide a comprehensive understanding of AI. It starts with foundational concepts and provides progresses to cover various aspects of AI (generative AI, responsible AI, using Copilot, etc)
Tips for Using AI Tech
Do's and Don'ts of Using the Tech:
- Be mindful of how your data is being used. Prior to using a tool, familiarize yourself with the terms of use and how the company is using the data that you are providing it with. Don't provide these tools with any sensitive information and no AHS data or business information should ever be used/fed to any third-party AI tool. Learn more about AHS' use of Microsoft Copilot.
- Don't use your AHS credentials to create accounts on these third-party sites. Keep your AHS email and password private, secure, and unique, and don't use them in part or whole when signing-up for outside accounts.
- Verify, verify, verify! Don't blindly trust the information that is being generated and always verify the output against other evidence-based sources. Even when citations and references look correct, they can be entirely made up.
- Not all tools are created equal and not all AI tools are built for research. General-purpose AI tools like Copilot or ChatGPT are great for brainstorming, but they can fall short when it comes to finding published research—you may end up spending extra time fact-checking (see above). Research-specific tools (like the ones in this guide) are designed to search academic sources directly, giving you more accurate and reliable results.
Recommended Reads
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Using Large Language Models with CareAllen Institute for AI (Ai2) researchers outline some of the common risks of current text-based large language model (LLM) systems written for the everyday user and highlight some important questions to ask ourselves when using this tech (adapted):
1. Am I asking the kind of questions appropriate for the type of tool, given its limitations (language model & data source) and risks of application?
2. Am I ok with my prompts being stored and shared with others? Is there any private information in my prompts?
3. Have I checked the accuracy of the output? Does the output contain information that I didn’t ask for?
4. Am I asking the kind of questions where giving credit would be important, and if so, am I be able to identify the authors of the model’s output so that I can credit them?
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Evaluating Chat-GPT Generated ContentSome clear guidelines for evaluating generated content for educators that has broad applicability for anyone using AI tools. Read more to learn about each step:
1. Establish a Clear Purpose
2. Assess the Accuracy
3. Evaluate Relevance
4. Examine the Quality
5. Integrate Human Judgment
6. Address Ethical Considerations
Tools for Researchers
How can I effectively use AI to…
- brainstorm or refine research questions?
- assist with research tasks like screening or data extraction?
- inform decision making with reliable sources?
- help me stay up to date on the latest research?
- find relationships between papers?
AI tools aren't one-size-fits-all. While general-purpose apps like ChatGPT or Microsoft Copilot are great for content generation, they may not meet the needs of in-depth research. Many new tools are now designed specifically for information retrieval and to support different parts of the research workflow, including more specialized functions like abstract screening and data extraction, and are trained on academic sources. Performance can vary widely depending on your input, the tool’s data sources, the size and focus of its training data, and your specific use case—so it’s worth experimenting to find what works best for you.
As a researcher, you might find the following AI tools a useful starting point to enhance or complement your existing workflows. While we don't endorse any specific tool, we're happy to explore your use cases together and encourage thoughtful, critical exploration.
| Consider for... | Tool | AI Features | Sources | License Model | Select Publications |
|---|---|---|---|---|---|
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Quick clinical summaries of peer-reviewed literature
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Open Evidence |
Natural language queries |
JAMA, NEJM, Elsevier journal content | Free for healthcare providers. Sign up required. | |
| Crafting better research questions, evidence synthesis, research insights | Undermind.ai | Semantic search, LLM-based summarization of literature, Q&A |
Semantic Scholar (~220M) | Free with paid tiers. Sign up required. | 1, 2, 3 |
| Questions that require recent, unpublished sources/grey-literature | Perplexity | LLM-based Q&A, web search | Open internet search (real-time). Can include items like academic journals, internet sources, preprints, books, technical reports, and conference proceedings. | Free with paid tiers | 1 |
| Claims and evidence tables (consensus meter), alongside insights into evidence gaps | Consensus | Natural language queries, LLM-based summarization of literature | Semantic Scholar (~220M) | Free with paid tiers | 1 |
| Data extraction tables, prompts for inclusion/exclusion criteria, screening | Elicit | Natural language queries, LLM-based summarization of literature | Semantic Scholar (~220M), OpenAlex (~256M), ClinicalTrials.gov (~460K) | Free with paid tiers. Sign up required. | 1 |
| Trend analysis, research field analytics and other deep insights | Ai2 Scholar QA | Semantic search, LLM-based Q&A, Deep, step-wise search execution | Semantic Scholar (~220M) + ArXiV papers | Free with paid tiers | 1 |
| Interactive exploration of research networks and trends based on seed paper, staying up- to-date on new publications | Research Rabbit | Network visualization, recommendations | Semantic Scholar (~220M), CrossRef | Free with paid tiers. Sign up required. | 1 |
Recommended Reads
- Automation of Systematic Reviews with Large Language Models. medRxiv. 2025 June 13
- Answering real-world clinical questions using large language model, retrieval-augmented generation, and agentic systems. Digit Health. 2025 Jun 9
- Using artificial intelligence for systematic review: the example of elicit. BMC Med Res Methodol. 2025 Mar 18.
- Artificial intelligence in systematic reviews: promising when appropriately used. BMJ Open. 2023 Jul 7.
- Are ChatGPT and large language models “the answer” to bringing us closer to systematic review automation? Syst Rev. 2023 Apr 29.
Recorded Webinars
Artificial Intelligence (AI) methods in evidence synthesis: Cochrane Learning Live webinar series
In this webinar series, we will explore the role of AI in evidence synthesis, examine how it can complement traditional methods and provide a platform for experts to discuss the opportunities, challenges, and risks involved. This series targets those with foundational knowledge of systematic reviews who want to stay updated on AI developments in evidence synthesis.
In this webinar series, we will explore the role of AI in evidence synthesis, examine how it can complement traditional methods and provide a platform for experts to discuss the opportunities, challenges, and risks involved. This series targets those with foundational knowledge of systematic reviews who want to stay updated on AI developments in evidence synthesis.
- Recommendations and guidance on responsible AI in evidence synthesis [June 2025]
- How effectively do large language models and AI-based automation tools assist in writing and summarizing evidence syntheses? [May 2025]
- (How well) can large language models and AI-based automation tools assist in Risk of Bias Assessment? [April 2025]
- Opportunities and challenges for data extraction with a large language model [March 2025]
- Could large language models and/or AI-based automation tools assist the screening process? [February 2025]
- (How) can AI-based automation tools assist with systematic searching? [January 2025]
What is on the horizon for health care?
As we strive to improve healthcare outcomes, boost efficiency, and reduce costs, AI has the potential to play an important role in revolutionizing health care delivery. However, whether we are automating administrative tasks or enhancing the accuracy of diagnostics with AI technologies it will be important to approach these emerging technologies with caution. It will be necessary to fully investigate the key risks involved and address critical privacy and security concerns, as well as establish robust ethical frameworks and explore requirements for regulatory oversight.
Recommended Reads
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A practical framework for appropriate implementation and review of artificial intelligence (FAIR-AI) in healthcare (Aug 2025)FAIR-AI provides a practical template for health systems to adopt a process for the rigorous evaluation and monitoring of AI solutions. The prescriptive framework guided by explicit criteria is intentionally designed for health systems to use at the speed and scale required in real-world settings. This framework will enable institutions to carefully balance the desire to adopt innovative solutions while maintaining the highest standards for patient and care team safety.
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Toolkit for Implementers of Artificial Intelligence in Health Care (Version 2.0 Sep 23)This Canada Health Infoway toolkit provides an overview of the issues related to the implementation and uses of AI solutions in health care and provides strategic and operational guidance. It is divided into six modules:
1. An Introduction to AI in Health Care
2. Understanding Key Risks of AI in Health Care
3. Emerging Regulation of AI
4. Identifying Strategic Opportunities and Investments in AI
5. Change Management for AI Adoption in the Health Care Sector
6. AI Governance: Structures, Roles & Responsibilities
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HIMSS Artificial Intelligence (AI) and Emerging Technologies Toolkit for Healthcare OrganizationsThis toolkit includes an AI Instructional Slide Deck ("Artificial Intelligence: A Primer for Nurses"), a technology Heat Map, and Definitions of Emerging Technologies. Each one of these tools has a unique practical application by healthcare leaders.
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Canada's Drug Agency - Evidence on Digital Health BundleYou’ll find the most recent reports and resources on topics related to digital health. Bundle topics: Artificial Intelligence, Virtual Health, Wearables, Remote Monitoring and more! Read the 2025 Watch List: Artificial Intelligence in Health Care for some updated trends in healthcare.
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WHO: Ethics and Governance of Artificial Intelligence for HealthWHO reiterates the importance of applying ethical principles and appropriate governance when designing, developing, and deploying AI for health. The 6 core principles identified by WHO are: (1) protect autonomy; (2) promote human well-being, human safety, and the public interest; (3) ensure transparency, explainability, and intelligibility; (4) foster responsibility and accountability; (5) ensure inclusiveness and equity; (6) promote AI that is responsive and sustainable.
Additional Resources
Healthcare Management Forum (HMF) is the official peer-reviewed journal of the Canadian College of Health Leaders.
Research collection on Artificial Intelligence in Canadian Healthcare:
- Achieving success through ethical introduction of artificial intelligence in the healthcare ecosystem - Jun. 2025
- The far-reaching impact of robotic-assisted surgery on healthcare systems - Apr. 2025
- Considerations for emergency department virtual triage - Nov. 2024
- Artificial intelligence governance framework for healthcare - Oct. 2024
- Unlocking the potential: Responsibly embracing artificial intelligence to advance the use of health data and analytics at the Canadian Institute for Health Information - Oct. 2024
- Artificial intelligence research in Canadian hospitals: The development of metropolitan competencies - Aug 2024
- and more...
NEJM AI Latest Publications
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