Artificial Intelligence (AI) is the use of digital technology to create systems capable of performing tasks commonly thought to require human intelligence. It has the potential to make a significant difference in health and care settings through its ability to analyse large quantities of complex information.
This guide was created for the purposes of supporting and informing UHBW staff and students about the uses of Artificial Intelligence in healthcare, to share the pros and cons of artificial intelligence, and to sign post useful AI tools. However, Artificial Intelligence is developing and growing constantly and so this guide may quickly become outdated.
UHBW Library and Knowledge Service does not support or endorse any of the tools mentioned in this guide.
A clear need for our healthcare workforce is to continually adapt to meet the needs of the society it serves. Health Education England (HEE) commissioned the University of Manchester to perform a learning needs analysis and develop a framework outlining the skills and capabilities to ensure our health and care professionals can work in a digitally enhanced environment.
View Artificial Intelligence (AI) and Digital Healthcare Technologies Capability framework.
Artificial Intelligence refers to a broad field of science encompassing not only computer science but also psychology, philosophy, linguistics and other areas. AI is concerned with getting computers to do tasks that would normally require human intelligence.
It encompasses:
As you start to use AI it's a good idea to become familiar with common industry terms and concepts. We've listed here some common AI terms you may come across, and provided links to some more in-depth glossaries:
Please be aware that some definitions may not be universally agreed, may change at in the near future, or may be interlinked with other terms.
Common Artificial Intelligence Terms:
AI systems can have bias embedded in them, which can manifest through various pathways including biased training datasets or biased decisions made by humans in the design of algorithms.
A wide-ranging field of research that deals with large datasets. The field has grown rapidly as computer systems become more capable of storing and analysing vast amounts of data. A key challenge in big data is being able to generate useful insights from this data without compromising the privacy of the people to whom the data relates
A software application that has been designed to mimic human conversation, allowing it to talk to users via text or speech. Previously used mostly as virtual assistants in customer service, chatbots are becoming increasingly powerful and can now answer questions across a variety of topics as well as generating other forms of text (see generative AI).
Pictures and video that are deliberately altered to generate misinformation and disinformation. Advances in generative AI have lowered the barrier for the production of deepfakes.
A subset of machine learning that uses artificial neural networks to recognise patterns in data and provide a suitable output, for example, a prediction. Deep learning is suitable for complex learning tasks, and has improved AI capabilities in tasks such as voice and image recognition, object detection and autonomous driving.
An AI model that generates text, images, audio, video or other media in response to user prompts. It uses machine learning techniques to create new data that has similar characteristics to the data it was trained on. Generative AI applications include chatbots, photo and video filters, and virtual assistants.
Large language models, such as ChatGPT, are unable to identify if the phrases they generate make sense or are accurate. This can sometimes lead to inaccurate results, also known as ‘hallucination’ effects, where large language models generate plausible sounding but inaccurate text. Hallucinations can also result from biases in training datasets or the model’s lack of access to up-to-date information.
A type of foundation model that is trained on vast amounts of text to carry out natural language processing tasks. During training phases, large language models learn parameters from factors such as the model size and training datasets. Parameters are then used by large language models to infer new content. Whilst there is no universally agreed figure for how large training datasets need to be, the biggest large language models (frontier AI) have been trained on billions or even trillions of bits of data.
A type of AI that allows a system to learn and improve from examples without all its instructions being explicitly programmed. Machine learning systems learn by finding patterns in training datasets. They then create a model (with algorithms) encompassing their findings. This model is then typically applied to new data to make predictions or provide other useful outputs, such as translating text.
This focuses on programming computer systems to understand and generate human speech and text. Algorithms look for linguistic patterns in how sentences and paragraphs are constructed and how words, context and structure work together to create meaning. Applications include speech-to-text converters, online tools that summarise text, chatbots, speech recognition and translations.
There is great potential to use AI tools to support you with your work and education but it is important to remember that there is a big difference between human and artificial intelligence. There is a limit to what AI tools can do although it is not always clear at first glance.
While AI tools have the potential to boost productivity inform decision-making, they can also generate false or biased information and contribute to carbon emissions.
You can find more examples of the pro and cons of AI from Leeds University and the University of Wolverhampton.
AI tools are software applications or platforms that utilise artificial intelligence to perform specific tasks or solve particular problems. This list compiled by East Cheshire NHS Trust Library & Knowledge Service will give you an overview of some of the most well known AI tools currently available:
ChatGPT is a large language model (LLM) chatbot developed by OpenAI. GPT = Generative Pre-Trained Transformer. It is trained on a massive dataset of text and code and can generate human-like text in response to a wide range of prompts and questions. It is not connected to the internet and can make stuff up to fill gaps in its ‘knowledge’.
MS Copilot (previously Bing AI) also uses GPT-4 (a LLM) to answer questions in a conversational way. It will reference its sources which include: The internet, it’s own knowledge base and conversation history. Copilot can generate images using Dall-E 3, a text-to-image generator created by OpenAI. Images generated, such as anatomical images, may not necessarily be accurate.
Gemini (previously Google Bard) is a more powerful and versatile LLM than GPT. It will answer questions in a similar way but it is trained on a dataset that includes both text and code (GPT is trained on text only). It also has access to real-time internet while GPT does not.
Elicit is a little different and uses machine learning and natural language processing (NLP) to help you find relevant research papers. It will then summarises the paper and extract key information such as formulas or statistical tests. It is a good alternative to using a clinical database (such as Medline) if you want to find papers quickly.
Consensus also uses machine learning to identify research papers that will answer a specific research question. Based on the result of the papers it will provide a ‘consensus’ answer to your research question. It only searches for published research via Semantic Scholar so it is more reliable that the GPT tools. There is currently a 6 month lag of data due to indexing - this is good as there is indexing.
Humata uses NLP to analyse text in a document/research paper. You can upload a PDF and ask it questions such as ‘what are the results of this trial’ or was NNT used? It will also generate new writing (rewords) based on existing documents.
Many Universities and academic institutions have guidance for their staff and students on the use of artificial intelligence in education. If you're a student or otherwise affiliated with an academic institution we recommend checking in with them. You can find links to guidance produced from our two local universities below:
University of Bristol:
Artificial intelligence tools like ChatGPT, Claude and Bing Copilot are revolutionising how we work and study. In this resource we aim to show you how to use AI well and ethically. That includes knowing how AI works, what its limits currently are, when you can and can’t use AI to study, how to write good AI prompts, how you can use it as a virtual tutor and which tools might be useful to you.
University of the West of England:
Generative AI (artificial Intelligence) is a rapidly evolving area that offers many possibilities for enhancing your learning and research. However, there are also concerns around possible misuse and other negative impacts. This guidance will support your use of AI at the University and has been developed from UWE Bristol’s principles for using generative artificial intelligence within learning, teaching and assessment.
As set out in the Long Term Plan, the NHS is aiming to become the first national health system in the world to digitise and make use of AI and machine learning technologies to help clinicians interpret scans part of the NHS routine. Below you'll find a selection of NHS publications and websites about artificial intelligence:
The NHS AI Lab: The NHS AI Lab creates an environment for collaboration and co-creation by bringing together programmes that address the barriers to developing and deploying AI systems in health and care. Explore guidance, case studies, reports and blog posts to find out about challenges faced by individuals and organisations working with AI in health and care and discover their solutions and best practice.
Artificial Intelligence: How to get it right: 'How to get it right' sets out the foundational policy work that was done to develop the plans for the NHS AI Lab. It outlines where in the system AI technologies can be used and the policy work that is, and will need to be done, to ensure the use of AI is safe, effective and ethical (published October 2019).
Artificial Intelligence and Information Governance: This guidance from NHSE focuses on the IG implications of using AI in health and care settings for patients/service users, those working in the health and care sector, and for information governance professionals.
Horizon Scanning: Find out more about the artificial intelligence (AI) roadmap and understanding healthcare workers’ confidence in AI.
Our colleagues from across the NHS have published current awareness bulletins on Artificial Intelligence in healthcare, which you can view here. If you need help accessing any of the articles listed in these documents, please do contact us at library@uhbw.nhs.uk.
Artificial Intelligence Update produced by East Cheshire NHS Trust:
The aim of the publication is to bring together a range of recently published research and guidance that will help make evidence-based decisions. Available online.
Digital Health Digest produced by The King's Fund:
A twice-monthly update from The King's Fund Library rounding up the latest news on developments in digital health and technology. Please subscribe to view.
The WT&E Knowledge Management Service produce a number of bulletins to keep you up to date with the latest publications and research on different themes relating to workforce, education and training. This includes a technology update, a weekly email that covers the three areas of the Topol review (Genomics, Artificial Intelligence and Digital Medicine).
An introduction to artificial intelligence for healthcare professionals: This free online course from NHSE elfh describes artificial intelligence (AI) and AI technologies that relate to healthcare. It also explores high-level concepts in machine learning (ML) and provides a responsible AI approach.
Have you undertaken any eLearning about Artificial Intelligence that you would recommend to your colleagues? Let us know about it and we can list it here.
Generative AI has a low barrier to entry. If you're wondering how to use it, our guide to creating an effective prompt and the further literature below describe how you can create an effective prompt and get the best results from generative AI tools.
Also check out this prompting process guide from Newcastle University which details how to use your prompt and how to evaluate the generated output:
Further Reading:
Giray, L. (2023) Prompt Engineering with ChatGPT: A Guide for Academic Writers. Annals of Biological Engineering [online]. 54, pp. 2629-2633.
Heston, T. F. and Khun, C. (2023) Prompt Engineering in Medical Education. International Medical Education [online]. 2(3), pp. 198-205.
Lingard, L. (2023) Writing with ChatGPT: An Illustration of its Capacity, Limitations and Implications for Academic Writers. Perspectives on Medical Education [online]. 12(1), pp. 261-270.
Mesko, B. (2023) Prompt Engineering as an Important Engineering Skill for Medical Professionals: Tutorial. Journal of Medical Education [online]. 25, no pagination.
Newcastle University (2025) Getting Started with Prompts.