Artificial Intelligence in the Academic Health Sciences

This guide provides information and resources on the applications of Artificial Intelligence (AI) in the academic health sciences. This includes the use of AI in teaching, research, and publishing. The primary types of AI used in these areas are Generative AI, including AI chatbots, and Deep Learning technology. For definitions of key terms used in the discussion of AI, see the glossary.

Generative AI is an emerging topic in academic health sciences research. It is important to understand some of the limitations in using AI for this purpose. Here are a few examples of those limitations:

• Bias: The data used to train Generative AI models can contain bias that is then reflected or perpetuated in the outputs.
• Data & User Privacy: Data may be collected and used in ways that are not transparent or disclosed to the user.
• Plagiarism & Copyright: The use of AI in research projects raises issues of authorship, in addition to intellectual property right concerns of publishers and creators of the content contained in the training data.
• Currency/Content: Generative AI is limited to the resources it has access to and how up to date that information is. Sources behind paywalls or firewalls are generally not accessible, thus impacting the quality of answers given by the AI model.
• Reproducibility & Consistency: The same search prompt can yield differing results, depending on the user and results may be irrelevant or inconsistent even when the same prompt is used.
• Inaccuracy: Generative AI works on predicting what the user is looking for. This can lead to inaccurate answers and data hallucinations, including invented references and citations. 
• Environmental Impact: running large scale generative AI models can consume a significant amount of energy. According to the UN Environment Programme "AI-related infrastructure may soon consume six times more water than Denmark, a country of 6 million."