Dr. John Westhoff has launched the Independent Data Exploration and Analysis (IDEA) Project at the University of Nevada, Reno School of Medicine (UNR Med). This yearlong course is designed to teach first-year medical students how to understand and evaluate health data. A key feature of the program is the use of artificial intelligence (AI) tools, which helps students get hands-on experience with real-world data.
Initially, students used AI platforms like ChatGPT and Claude, but now they work with TrialMind. This platform was created by Keiji AI and is custom-made for students. It simplifies literature reviews and assists with coding and statistical analyses. Notably, UNR Med is the first medical school to integrate TrialMind into its curriculum.
TrialMind acts more like a research mentor than a shortcut tool. It helps students with tasks like literature reviews while encouraging them to describe study designs in simple terms and convert those ideas into statistical code. This lowers the technical barriers for students, allowing them to focus more on reasoning and scientific questioning.
“We identified the need for a stronger understanding of clinical research among our students,” Dr. Westhoff explains. “Most won’t become researchers, but they must read and interpret scientific literature accurately. Knowing how to spot flawed methods or recognize the influence of study design is essential for practicing evidence-based medicine.”
Each year, students form small groups and work through various milestones while developing their case studies with TrialMind. Now in its third year, the IDEA Project has inspired students to publish their research on significant topics. For instance:
- “Trends and disparities in firearm-related mortality among U.S. children and young adults, 1999-2020”
- “Geographical trends in cerebrovascular disease mortality in the United States, 1999-2020”
- “Adults 65 years and older not immune to the opioid epidemic”
Joseph Tran, currently an M.D./Ph.D. student, noted that his participation in the IDEA Project transformed his outlook on research. “It highlighted that you can create meaningful, publishable research by asking the right questions and applying clear methods,” he stated. “This has helped me identify important clinical questions.”
Tran also brings a background in computer science from Stanford, where he learned statistical programming languages like Python and R. His goal is to combine these skills with healthcare innovations. “Using AI tools in research allows us to focus on what truly impacts patient care rather than getting lost in complex statistical details,” he adds.
This innovative approach highlights the importance of understanding research methodologies in the medical field. It not only prepares students for future challenges in healthcare but also equips them with the skills to analyze and interpret data effectively, fostering a generation of doctors who are informed and capable of evidence-based practice.
For more insights into the significance of AI in research and healthcare, visit sources like the [National Institutes of Health](https://www.nih.gov) or the [American Medical Association](https://www.ama-assn.org).
