Summary of Large Language Models For Medical Osce Assessment: a Novel Approach to Transcript Analysis, by Ameer Hamza Shakur et al.
Large Language Models for Medical OSCE Assessment: A Novel Approach to Transcript Analysis
by Ameer Hamza Shakur, Michael J. Holcomb, David Hein, Shinyoung Kang, Thomas O. Dalton, Krystle K. Campbell, Daniel J. Scott, Andrew R. Jamieson
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The study explores the use of Large Language Models (LLMs) to assess communication skills related to medical student OSCE examinations. Researchers analyzed 2,027 video-recorded OSCE exams from the University of Texas Southwestern Medical Center, focusing on summarizing patients’ medical history. They transcribed speech audio using Whisper-v3 and evaluated various LLM-based approaches for grading students. Results showed that frontier LLM models like GPT-4 achieved high alignment with human graders (Cohen’s kappa agreement: 0.88), demonstrating potential for augmenting the current grading process. Open-source models also showed promising results, suggesting cost-effective deployment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study looks at using special computers called Large Language Models to help grade medical students’ exams. These exams are like a test where students show they can communicate with patients. The researchers took 2,027 videos of these exams and used the computer’s speech recognition tool to see what the students said. They then tried different ways to use the computers to help grade the exams. They found that some computers were very good at it, just like a human would be. This could make grading easier and less expensive. |
Keywords
» Artificial intelligence » Alignment » Gpt
