Siobhan O'Connor

Artificial intelligence (AI) is now widely accessible and already being used by healthcare researchers throughout various stages in the research process, such as assisting with systematic reviews, supporting data collection, facilitating data analysis and drafting manuscripts for publication.1 The most common AI tools used are forms of generative AI such as ChatGPT, Claude and Gemini. Generative AI is a type of AI that can generate human-like text, audio, videos, code and images based on text-based prompts inputted by a human user. Generative AI is trained on large amounts of data, and the outputs are sophisticated and can be indistinguishable from a response from a skilled human.2 In this article, we outline several AI applications that can be used in healthcare research, examining their benefits, limitations and outline best practices for maintaining research integrity and ethical standards.There are several reasons why healthcare researchers may choose to use some form of AI in the research process, for example—to make it more efficient, save time and reduce costs. While AI tools have become increasingly sophisticated, this progress may slow as high-quality training data becomes scarcer.3 The effectiveness of any AI tool will also depend on users’ digital literacy and subscription access. The requirement for a paid subscription to access the optimal capabilities of many AI tools means that inequitable access is likely to continue; researchers based in high-income countries will be able to access and use AI tools for healthcare research while those in low-resource settings may not.4 It is worth noting that accessible AI research tools may help to address existing disparities by helping healthcare researchers from low-income and middle-income countries with limited resources access to specific methodological and domain expertise. …

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