TY  - JOUR
AU  - Burke, Harry B
AU  - Hoang, Albert
AU  - Lopreiato, Joseph O
AU  - King, Heidi
AU  - Hemmer, Paul
AU  - Montgomery, Michael
AU  - Gagarin, Viktoria
PY  - 2024
DA  - 2024/7/25
TI  - Assessing the Ability of a Large Language Model to Score Free-Text Medical Student Clinical Notes: Quantitative Study
JO  - JMIR Med Educ
SP  - e56342
VL  - 10
KW  - medical education
KW  - generative artificial intelligence
KW  - natural language processing
KW  - ChatGPT
KW  - generative pretrained transformer
KW  - standardized patients
KW  - clinical notes
KW  - free-text notes
KW  - history and physical examination
KW  - large language model
KW  - LLM
KW  - medical student
KW  - medical students
KW  - clinical information
KW  - artificial intelligence
KW  - AI
KW  - patients
KW  - patient
KW  - medicine
AB  - Background: Teaching medical students the skills required to acquire, interpret, apply, and communicate clinical information is an integral part of medical education. A crucial aspect of this process involves providing students with feedback regarding the quality of their free-text clinical notes. Objective: The goal of this study was to assess the ability of ChatGPT 3.5, a large language model, to score medical students’ free-text history and physical notes. Methods: This is a single-institution, retrospective study. Standardized patients learned a prespecified clinical case and, acting as the patient, interacted with medical students. Each student wrote a free-text history and physical note of their interaction. The students’ notes were scored independently by the standardized patients and ChatGPT using a prespecified scoring rubric that consisted of 85 case elements. The measure of accuracy was percent correct. Results: The study population consisted of 168 first-year medical students. There was a total of 14,280 scores. The ChatGPT incorrect scoring rate was 1.0%, and the standardized patient incorrect scoring rate was 7.2%. The ChatGPT error rate was 86%, lower than the standardized patient error rate. The ChatGPT mean incorrect scoring rate of 12 (SD 11) was significantly lower than the standardized patient mean incorrect scoring rate of 85 (SD 74; P=.002). Conclusions: ChatGPT demonstrated a significantly lower error rate compared to standardized patients. This is the first study to assess the ability of a generative pretrained transformer (GPT) program to score medical students’ standardized patient-based free-text clinical notes. It is expected that, in the near future, large language models will provide real-time feedback to practicing physicians regarding their free-text notes. GPT artificial intelligence programs represent an important advance in medical education and medical practice. 
SN  - 2369-3762
UR  - https://mededu.jmir.org/2024/1/e56342
UR  - https://doi.org/10.2196/56342
DO  - 10.2196/56342
ID  - info:doi/10.2196/56342
ER  -