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Artificial Intelligence (AI)

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Artificial Intelligence (AI) is a broad term for digital technology that can perform tasks that require "intelligence", such as reasoning, making decisions, learning from mistakes, communicating, and problem solving.

Examples of AI Tools​

  • 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 a text-to-image generator - however some, (such as anatomical), 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 summarise the paper and extract key information such as formulas or statistical tests. 
  • 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. 
  • 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. 

AI in healthcare

Thinking Critically about AI in Healthcare Jessica Morley, May 2023
Annotated bibliography on key issues around AI in healthcare.
Principles for artificial intelligence (AI) and its application in healthcare BMA, October 2024 (22 page report)
​AI knowledge repository NHS England 2025
The NHS AI Lab : NHS England
NHS Strategy
  • AI in healthcare: navigating the noise | NHS Confederation September 2024
  • AI and the NHS: is it the silver bullet that will improve the health service’s productivity? Jessica Morley, Nuffield Trust Guest Blog 2024
  • Preparing the NHS for the AI Era: A Digital Health Record for Every Citizen Tony Blair Institute, 2024
  • AI for healthcare (Imperial College London)
  • AI in health care: what do the public and NHS staff think? The Health Foundation, 2024
  • Priorities for an AI in health care strategy. The Health Foundation, 2024
  • Understanding healthcare workers’ confidence in AI. NHS AI Lab & Health Education England, 2022
  • Developing healthcare workers’ confidence in AI. NHS AI Lab & Health Education England, 2022
Patient Safety and Information
  • ​​Patient Safety and Artificial Intelligence: Opportunities and Challenges for Care Delivery. Institute for Healthcare Improvement, 2024
  • Artificial Intelligence in Health Care: Implications for Patient and Workforce Safety. Institute for Healthcare Improvement, 2024
    This IHI Innovation Report presents a detailed review of three primary applications of generative artificial intelligence (genAI) in health care, including benefits and potential patient safety risks and unintended consequences resulting from genAI tools.
  • Balancing the risks and benefits of AI in the production of health information. Patient Information Forum, 2024
    As the landscape evolves, health information producers are investigating the risks and benefits of using AI in their everyday work. Developed in collaboration with PIF’s AI working group, this position statement aims to help members understand the AI landscape and how to manage it.

Understanding AI

  • Transformed by AI: How generative artificial intelligence could affect work in the UK – and how to manage it | IPPR March 2024 (44 page report)​​

Definitions

  • An Algorithm is a set of instructions or rules that a computer (including smartphones) uses to complete a task.
  • Generative AI is a type of AI that generates text, images, audio, video, or other media based on user prompts using machine learning. Chatbots are a common example of generative AI.
  • Hallucinations refer to responses from large language models (LLMs) such as ChatGPT that seem plausible but are inaccurate. An example would be a citation for a book that doesn't exist.
  • Large Language Models (LLMs) are a foundation model, which is a type of machine learning model trained on vast amounts of data. LLMs are trained on massive amounts of text to carry out language-related tasks. Google Translate and ChatGPT are both examples of LLMs.
  • Machine Learning is a type of AI that uses algorithms to 'learn' without all of it's instructions being explicitly programmed. Examples include LLMs (see above), virtual assistants like Alexa, and facial recognition.

University AI Guides

Sheffield Hallam University - ​Making the most of Generative AI
Leeds University - Generative AI guidance for taught students
Sheffield University - Generative AI - Guidance and resources to help you to get the most out of Generative AI within and alongside your studies.

AI Governance and Ethics

  • How to use AI and personal data appropriately and lawfully : Information Commissioner's Office 2022
  • Responsible AI Toolkit - GOV.UK 
  • Navigating the European Union Artificial Intelligence Act for Healthcare, npj Digital Medicine, August 2024
  • Information Governance Guidance: Artificial Intelligence : NHS England 2023

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