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ChatGPT Interactive Medical Simulations for Early Clinical Education: Case Study
BACKGROUND: The transition to clinical clerkships can be difficult for medical students, as it requires the synthesis and application of preclinical information into diagnostic and therapeutic decisions. ChatGPT—a generative language model with many medical applications due to its creativity, memory...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10674152/ https://www.ncbi.nlm.nih.gov/pubmed/37948112 http://dx.doi.org/10.2196/49877 |
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author | Scherr, Riley Halaseh, Faris F Spina, Aidin Andalib, Saman Rivera, Ronald |
author_facet | Scherr, Riley Halaseh, Faris F Spina, Aidin Andalib, Saman Rivera, Ronald |
author_sort | Scherr, Riley |
collection | PubMed |
description | BACKGROUND: The transition to clinical clerkships can be difficult for medical students, as it requires the synthesis and application of preclinical information into diagnostic and therapeutic decisions. ChatGPT—a generative language model with many medical applications due to its creativity, memory, and accuracy—can help students in this transition. OBJECTIVE: This paper models ChatGPT 3.5’s ability to perform interactive clinical simulations and shows this tool’s benefit to medical education. METHODS: Simulation starting prompts were refined using ChatGPT 3.5 in Google Chrome. Starting prompts were selected based on assessment format, stepwise progression of simulation events and questions, free-response question type, responsiveness to user inputs, postscenario feedback, and medical accuracy of the feedback. The chosen scenarios were advanced cardiac life support and medical intensive care (for sepsis and pneumonia). RESULTS: Two starting prompts were chosen. Prompt 1 was developed through 3 test simulations and used successfully in 2 simulations. Prompt 2 was developed through 10 additional test simulations and used successfully in 1 simulation. CONCLUSIONS: ChatGPT is capable of creating simulations for early clinical education. These simulations let students practice novel parts of the clinical curriculum, such as forming independent diagnostic and therapeutic impressions over an entire patient encounter. Furthermore, the simulations can adapt to user inputs in a way that replicates real life more accurately than premade question bank clinical vignettes. Finally, ChatGPT can create potentially unlimited free simulations with specific feedback, which increases access for medical students with lower socioeconomic status and underresourced medical schools. However, no tool is perfect, and ChatGPT is no exception; there are concerns about simulation accuracy and replicability that need to be addressed to further optimize ChatGPT’s performance as an educational resource. |
format | Online Article Text |
id | pubmed-10674152 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-106741522023-11-10 ChatGPT Interactive Medical Simulations for Early Clinical Education: Case Study Scherr, Riley Halaseh, Faris F Spina, Aidin Andalib, Saman Rivera, Ronald JMIR Med Educ Original Paper BACKGROUND: The transition to clinical clerkships can be difficult for medical students, as it requires the synthesis and application of preclinical information into diagnostic and therapeutic decisions. ChatGPT—a generative language model with many medical applications due to its creativity, memory, and accuracy—can help students in this transition. OBJECTIVE: This paper models ChatGPT 3.5’s ability to perform interactive clinical simulations and shows this tool’s benefit to medical education. METHODS: Simulation starting prompts were refined using ChatGPT 3.5 in Google Chrome. Starting prompts were selected based on assessment format, stepwise progression of simulation events and questions, free-response question type, responsiveness to user inputs, postscenario feedback, and medical accuracy of the feedback. The chosen scenarios were advanced cardiac life support and medical intensive care (for sepsis and pneumonia). RESULTS: Two starting prompts were chosen. Prompt 1 was developed through 3 test simulations and used successfully in 2 simulations. Prompt 2 was developed through 10 additional test simulations and used successfully in 1 simulation. CONCLUSIONS: ChatGPT is capable of creating simulations for early clinical education. These simulations let students practice novel parts of the clinical curriculum, such as forming independent diagnostic and therapeutic impressions over an entire patient encounter. Furthermore, the simulations can adapt to user inputs in a way that replicates real life more accurately than premade question bank clinical vignettes. Finally, ChatGPT can create potentially unlimited free simulations with specific feedback, which increases access for medical students with lower socioeconomic status and underresourced medical schools. However, no tool is perfect, and ChatGPT is no exception; there are concerns about simulation accuracy and replicability that need to be addressed to further optimize ChatGPT’s performance as an educational resource. JMIR Publications 2023-11-10 /pmc/articles/PMC10674152/ /pubmed/37948112 http://dx.doi.org/10.2196/49877 Text en ©Riley Scherr, Faris F Halaseh, Aidin Spina, Saman Andalib, Ronald Rivera. Originally published in JMIR Medical Education (https://mededu.jmir.org), 10.11.2023. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Education, is properly cited. The complete bibliographic information, a link to the original publication on https://mededu.jmir.org/, as well as this copyright and license information must be included. |
spellingShingle | Original Paper Scherr, Riley Halaseh, Faris F Spina, Aidin Andalib, Saman Rivera, Ronald ChatGPT Interactive Medical Simulations for Early Clinical Education: Case Study |
title | ChatGPT Interactive Medical Simulations for Early Clinical Education: Case Study |
title_full | ChatGPT Interactive Medical Simulations for Early Clinical Education: Case Study |
title_fullStr | ChatGPT Interactive Medical Simulations for Early Clinical Education: Case Study |
title_full_unstemmed | ChatGPT Interactive Medical Simulations for Early Clinical Education: Case Study |
title_short | ChatGPT Interactive Medical Simulations for Early Clinical Education: Case Study |
title_sort | chatgpt interactive medical simulations for early clinical education: case study |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10674152/ https://www.ncbi.nlm.nih.gov/pubmed/37948112 http://dx.doi.org/10.2196/49877 |
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