Artificial Intelligence (AI) in Health
What is AI?
Artificial intelligence (AI) is technology that enables computers and digital devices to learn, read, write, talk, see, create, play, analyse, and make recommendations. Generative AI is a type of artificial intelligence that builds on existing material to generate or remix content. For many the most well known one of recent years is ChatGPT. It can answer questions, write songs, poems, essays, and software code. AI has many potential applications and is being used everywhere, and healthcare is not the exception.
Safe and responsible use of AI
Australian Commission on Safety and Quality in Health Care. (2025). AI clinical use guide and AI safety scenarios - interpretation of medical images and ambient scribe. These guides highlight that clinicians are responsibility for the safe and appropriate use of AI tools. They should be aware of how the tools work, deal with personal or sensitive information, and where there may be automation bias and the impacts on patient management.
Department of Health (Victoria) - Artificial Intelligence in Victorian Public Health Services
Safer Care Victoria (Department of Health, State Government of Victoria) has released a health service advisory on Health service use of unregulated Artificial Intelligence (AI). The Australian Medical Association has also made a submission on Safe and Responsible AI in Australia.
The National Health and Medical Research Council (NHMRC) has policies on the use of generative AI in research. Researchers should ensure that the use of generative AI tools does not breach the relevant funder's policies, and that the research output is not flawed by bias or inaccuracy. Generative AI and all of its possibilities are exciting, but it’s still new, and can make mistakes. Generative AI can generate false information (hallucination). You should also check the outputs from generative AI to ensure they aren't breaching copyright, consent, or research integrity.
Australia’s 8 Artificial Intelligence (AI) Ethics Principles
- Human, societal and environmental wellbeing: AI systems should benefit individuals, society and the environment.
- Human-centred values: AI systems should respect human rights, diversity, and the autonomy of individuals.
- Fairness: AI systems should be inclusive and accessible, and should not involve or result in unfair discrimination against individuals, communities or groups.
- Privacy protection and security: AI systems should respect and uphold privacy rights and data protection, and ensure the security of data.
- Reliability and safety: AI systems should reliably operate in accordance with their intended purpose.
- Transparency and explainability: There should be transparency and responsible disclosure so people can understand when they are being significantly impacted by AI, and can find out when an AI system is engaging with them.
- Contestability: When an AI system significantly impacts a person, community, group or environment, there should be a timely process to allow people to challenge the use or outcomes of the AI system.
- Accountability: People responsible for the different phases of the AI system lifecycle should be identifiable and accountable for the outcomes of the AI systems, and human oversight of AI systems should be enabled.
Warnings:
AI can only search open access or open grey literature information, it cannot gain access to closed (behind paywall) journals or paid medication resources or guidelines. This means critical evidence based information can be missed. AI does not really explain how and why it came to the decisions it has. This is known as 'black box reasoning' or 'black-box decision making', there is a risk of loss of knowledge of 'how' decisions are made. There is also possibility of information degeneration, in which in which the data generated by AI ends up polluting the training set of the next generation of AI learning.
You can find more journals from our collection on the subject of artificial intelligence in our publication finder.
Australian Standards
Access to Australian Standards information provided here.
AS ISO/IEC 42001:2023 - Information technology — Artificial intelligence — Management system
AS ISO/IEC 22989 - Information technology - Artificial intelligence - Artificial intelligence concepts and terminology
AS ISO/IEC 23053 - Framework for Artificial Intelligence (AI) Systems Using Machine Learning (ML)
AS ISO/IEC 23894 - Information technology - Artificial intelligence - Guidance on risk management
AS ISO/IEC 24668 - Information technology - Artificial intelligence - Process management framework for big data analytics
AS ISO/IEC 38507 - Information technology - Governance of IT - Governance implications of the use of artificial intelligence by organizations
AI Tools
Text content creation ChatGPT
Chatbot GPT (Generative Pre-trained Transformer) is an AI-powered language model developed by OpenAI. Designed to understand and generate human-like text based on the input they receive. ChatGPT, has been trained on a vast amount of diverse text from the internet and other sources, enabling it to understand and respond to a wide range of topics and questions in a conversational manner. The AI model aims to assist with various tasks, answer queries, generate content, and engage in dialogue across multiple domains.
Trained on large amounts of publicly available data, Gemini can communicate and generate human-like text in response to a wide range of questions. Gemini is a family of multimodal large language models developed by Google DeepMind.
Copilot is a conversational chat interface that lets you search for specific information, generate text such as emails and summaries, and create images based on text prompts you write. There are two types of Copilot tools for staff. One is open to everyone online, and the other is made for work (Work Copilot) and keeps your information secure. Copilot should not be used for any clinical purpose.
PerplexityPerplexity is an AI-powered search engine and chatbot that utilises advanced technologies such as natural language processing (NLP) and machine learning to provide accurate and comprehensive answers to user queries. It is designed to search the web in real-time and offer up-to-date information on various topics. Perplexity is a powerful tool with an intuitive user interface that can help users find information on a wide range of topics.
Image content creation
Generative AI image tools can produce diverse images in a range of mediums, everything from photorealistic oil painting style to anime.
Some examples of generative AI that can create imagery include: Dall.E 3, Midjourney Nightcafe, and Stable Diffusion.
Note: Many of these tools cost money to access premium features. However, you can create a basic account for free or explore the tool with a short-term trial.
Literature citation discovery and mapping tools
Citation mapping tools visualise and analyse the relationships between scholarly publications based on their citations.
Consensus is a search engine that uses language models to find papers and synthesise insights from academic research papers. Material comes from the Semantic Scholar database.
Elicit is a citation mapping tool that uses artificial intelligence to help researchers discover and understand scholarly literature. It allows users to input a seed paper (or papers) and then generates a network map of related papers, highlighting the most important and influential papers in the field.
Inciteful is an online tool that helps you map academic literature. Start from seed paper(s), you can have an overview on the current state of that topic. Then, by adding more seed papers or filters, you can further craft the citation graph and have a focus on your search. If you are writing a paper, you can import the items in your reference list to Inciteful, and the resulting graph should be centered around the paper you are writing. The similar papers section may reveal some papers that you may have missed for inclusion via traditional keywords or citation searches.
Litmaps creates interactive literature maps: collections of articles that make up your research topics. Litmaps helps you visualise the papers as network graphs based on publication years, citations, citing relations, and title similarity. Based on the connections between papers, Litmaps can make suggestions to help you find papers without you coming up with a keyword for searching.
Open Knowledge Maps is an online tool that can generate a knowledge map of a research topic. It shows the main areas in a field with relevant papers and concepts.
Research Rabbit is citation tracking tool that allows users to optimise their searching by using 'collections' and relevant (seed) papers to discover references.
Note: Many of these tools cost money to access premium features. However, you can create a basic account for free or explore the tool with a short-term trial.
Search filters - The links below are a live search in Pubmed.
NOTE: These search filters have not been peer reviewed
Computer-Assisted Diagnosis
Computer-Assisted Diagnosis Imaging
Computer-Assisted Therapy
Precision Medicine
AI in Preventive Medicine
AI General Pubmed Search filter
Terms list
Terms listed sourced from CSIRO library guide.
Further reading
Advancing health care AI through ethics, evidence and equity - American Medical Association (AMA)
Key decision points: 8 decisions points to consider when implementing an AI solution - Health AI Partnership (Duke Health & Microsoft)
6 things to know about AI – News Literacy Project
AI is already being used in healthcare. But not all of it is ‘medical grade’ – CSIRO
Understanding CC licenses and generative AI - by Kat Walsh, Creative Commons
Chen, M., & Decary, M. (2020). Artificial intelligence in healthcare: An essential guide for health leaders. Healthcare Management Forum, 33(1), 10-18. https://doi.org/10.1177/0840470419873123
Coiera, E.W., & Verspoor, K. and Hansen, D.P. (2023). We need to chat about artificial intelligence. Medical Journal of Australia, 219, 98-100. https://doi.org/10.5694/mja2.51992
Crigger, E., Reinbold, K., Hanson, C. et al. (2022). Trustworthy augmented intelligence in health care. Journal of Medical Systems, 46(12). https://doi.org/10.1007/s10916-021-01790-z
Shinners, L., Aggar, C., Stephens, A., & Grace, S. (2023). Healthcare professionals' experiences and perceptions of artificial intelligence in regional and rural health districts in Australia. Australian Journal of Rural Health, 31, 1203–1213. https://doi.org/10.1111/ajr.13045
Zohny, H., McMillan, J., King, M. (2023). Ethics of generative AI. Journal of Medical Ethics, 49, 79-80. https://doi.org/10.1136/jme-2023-108909