Ethical Challenges in AI for Clinical Research

Date: February 11, 2025 Dr. Ross Prager Guest Skeptic: Dr. Ross Prager is an Intensivist at the London Health Sciences Centre and an adjunct professor at Western University. His expertise in critical care medicine is complemented by his research interests in critical care ultrasound and evidence-based knowledge translation. This is another SGEM Xtra. On today’s episode, we’re diving into a fascinating and evolving topic—how artificial intelligence (AI) shapes clinical research. AI has the potential to streamline many aspects of medical research, from study design to statistical analysis and even manuscript preparation. But as always, we need to approach these innovations skeptically. There are a lot of promises being laid on the shoulders of AI, and increasingly, it can be difficult to separate the hype from reality. I certainly believe that AI will change what clinical research looks like in the next decade, but at its core, it will be the synergy between researchers and technology that drives innovation, not either in isolation. The easiest way to reflect on how AI might be used in clinical research is to think about the research lifecycle. Layered on top of this are themes like collaboration/team efficiency, security and privacy, and other general administrative efficiencies (accounting + Meeting scheduling + email management). Study inception and design Protocol Generation Ethics application Study Facilitation and Recruitment Data Extraction Data Analysis Manuscript writing Manuscript submission Knowledge Mobilization  Eleven Questions on Artifical Intelligence and Clinical Research Listen to the SGEM Podcast to hear Dr. Prager's responses to my eleven questions.  1. Designing a Study with AI – Formulating a PICO Question: Every good clinical study starts with a clear and well-defined research question. AI tools are now being used to help formulate the PICO (Population, Intervention, Control, Outcome). How can AI assist researchers in this first critical step? 2. Identifying Potential Study Participants from EMRs: One of the biggest challenges in research is identifying eligible patients. Traditionally, this has been done manually and has been a very time-intensive process (think medical students). How can AI help streamline this? 3. Determining the Most Important Patient-Oriented Outcome: Research should prioritize outcomes that matter to patients. How can AI help determine the most clinically meaningful and patient-centred outcomes for a study? In other words, can AI help us find the POO? 4. Estimating Effect Size and Sample Size Calculations: To conduct a well-powered study, researchers need to estimate the expected effect size and determine the required sample size. Can AI assist with these calculations? 5. AI for Statistical Analysis and Data Visualization: Once data is collected, the next step is analysis. How can AI assist with statistics and visualizing complex data? 6. AI-Assisted Manuscript Writing and Editing: Writing a research paper is a time-consuming process, especially for non-native English speakers. A friend of mine is a clinical researcher and editor for a major journal. They talk about knowing some brilliant researchers who cannot write/communicate well. Can AI help these people and improve the clarity and readability of their scientific manuscripts? 7. Verifying Citation Accuracy: We will be talking about the issue of inaccurate citations in the medical literature with Dr. Nick Peoples. His research reported that citations are not correct up to 25% of the time (reference). Concerns have been raised about AI hallucinated citations. We want to make things better, not worse, by using AI. How can AI be used to ensure accuracy and prevent misinformation in referencing? 8. AI in Systematic Reviews and Meta-Analyses: Another form of clinical research is performing systematic reviews and meta-analyses.