Low-Resource Research Examples
When working with undergraduate students in a physician-scientist program, I often look for research approaches that can be conducted without large samples or major funding. I plan to collect notable examples as I encounter them.
A Common Pattern
As I collect these examples, a common feature becomes apparent: they concern things that everyone seems to assume are true, but for which no one has made the effort to organize and present the available evidence.
Examples
- Huh KY, Song I. “Analyzing Collaborations in Clinical Trials in Korea Using Association Rule Mining.” Translational and Clinical Pharmacology. 2024 Dec;32(4):177–186. https://doi.org/10.12793/tcp.2024.32.e17 This study analyzes networks among clinical-trial institutions.
- Cho SI, Lee JM, Park HJ, Suh J, Lee RW. “Healthcare Crisis in Korea and Its Impact on Medical Research: A PubMed Analysis (2022–2024).” Journal of Korean Medical Science. 2025;40(9). doi:10.3346/jkms.2025.40.e112 This study analyzes how the conflict between the medical profession and the government affected Korea’s PubMed-indexed research output.
- Ahn S. “Large Language Model Usage Guidelines in Korean Medical Journals: A Survey Using Human-Artificial Intelligence Collaboration.” Journal of Yeungnam Medical Science. 2025;42.14. https://doi.org/10.12701/jyms.2024.00794 This study examines whether submission guidelines for Korean medical journals include rules related to AI, and, if so, what those rules say.
- McCoy, J. P., & Ullman, T. D. (2018). “A Minimal Turing Test.” Journal of Experimental Social Psychology, 79, 1–8. https://doi.org/10.1016/j.jesp.2018.05.007 The scenario asks: “If an android and a human can each say only one word to prove that they are human and thereby avoid death, what word should they say?” Candidate words are collected, and then many people are asked about pairs of words so that the information can be aggregated. Combining this approach with large language models may make it possible to compare differences in perception.