Large language models improve clinical decision making of medical students through patient simulation and structured feedback: a randomized controlled trial
Emilia Brügge, Sarah Ricchizzi, Malin Arenbeck, Marius Niklas Keller, Lina Schur, Walter Stummer, Markus Holling, Max Hao Lu, Dogus Darici · 2024
Read Large language models improve clinical decision making of medical students through patient simulation and structured feedback: a randomized controlled trial by Emilia Brügge, Sarah Ricchizzi, Malin Arenbeck, Marius Niklas Keller, Lina Schur, Walter Stummer, Markus Holling, Max Hao Lu, Dogus Darici (2024) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-11605890.txt.
Description
Emilia Bruggae and co-authors run a trial in a 2024 study on AI patients for medical training. Sound clinical choices start with a good history talk with the patient. Students usually practice that talk on real or acted patients, which limits how much practice each student gets.
Prompts were built so a large language model could play patients with varied symptoms and give structured feedback after each talk. Students were split at random, with neither side told who got what. One group practiced with the AI patients. The trial tests whether that practice improves real decision skills. If it does, schools gain a cheap way to add practice hours.
Catalog record
| CREATOR | Emilia Brügge, Sarah Ricchizzi, Malin Arenbeck, Marius Niklas Keller, Lina Schur, Walter Stummer, Markus Holling, Max Hao Lu, Dogus Darici |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 41 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |