Synthetic medical education in dermatology leveraging generative artificial intelligence
Abstract The advent of large language models (LLMs) represents an enormous opportunity to revolutionize medical education. Via “synthetic education,” LLMs can be harnessed to generate novel content for medical education purposes, offering potentially unlimited resources for physicians in training. U...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01650-x |
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| _version_ | 1849729009004314624 |
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| author | Arya S. Rao John Kim Andrew Mu Cameron C. Young Ezra Kalmowitz Michael Senter-Zapata David C. Whitehead Lilit Garibyan Adam B. Landman Marc D. Succi |
| author_facet | Arya S. Rao John Kim Andrew Mu Cameron C. Young Ezra Kalmowitz Michael Senter-Zapata David C. Whitehead Lilit Garibyan Adam B. Landman Marc D. Succi |
| author_sort | Arya S. Rao |
| collection | DOAJ |
| description | Abstract The advent of large language models (LLMs) represents an enormous opportunity to revolutionize medical education. Via “synthetic education,” LLMs can be harnessed to generate novel content for medical education purposes, offering potentially unlimited resources for physicians in training. Utilizing OpenAI’s GPT-4, we generated clinical vignettes and accompanying explanations for 20 skin and soft tissue diseases tested on the United States Medical Licensing Examination. Physician experts gave the vignettes high average scores on a Likert scale in scientific accuracy (4.45/5), comprehensiveness (4.3/5), and overall quality (4.28/5) and low scores for potential clinical harm (1.6/5) and demographic bias (1.52/5). A strong correlation (r = 0.83) was observed between comprehensiveness and overall quality. Vignettes did not incorporate significant demographic diversity. This study underscores the potential of LLMs in enhancing the scalability, accessibility, and customizability of dermatology education materials. Efforts to increase vignettes’ demographic diversity should be incorporated to increase applicability to diverse populations. |
| format | Article |
| id | doaj-art-34360d19dc254cdb80a8e64dea519a60 |
| institution | DOAJ |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-34360d19dc254cdb80a8e64dea519a602025-08-20T03:09:21ZengNature Portfolionpj Digital Medicine2398-63522025-05-01811510.1038/s41746-025-01650-xSynthetic medical education in dermatology leveraging generative artificial intelligenceArya S. Rao0John Kim1Andrew Mu2Cameron C. Young3Ezra Kalmowitz4Michael Senter-Zapata5David C. Whitehead6Lilit Garibyan7Adam B. Landman8Marc D. Succi9Harvard Medical SchoolHarvard Medical SchoolHarvard Medical SchoolHarvard Medical SchoolHarvard Medical SchoolHarvard Medical SchoolHarvard Medical SchoolHarvard Medical SchoolHarvard Medical SchoolHarvard Medical SchoolAbstract The advent of large language models (LLMs) represents an enormous opportunity to revolutionize medical education. Via “synthetic education,” LLMs can be harnessed to generate novel content for medical education purposes, offering potentially unlimited resources for physicians in training. Utilizing OpenAI’s GPT-4, we generated clinical vignettes and accompanying explanations for 20 skin and soft tissue diseases tested on the United States Medical Licensing Examination. Physician experts gave the vignettes high average scores on a Likert scale in scientific accuracy (4.45/5), comprehensiveness (4.3/5), and overall quality (4.28/5) and low scores for potential clinical harm (1.6/5) and demographic bias (1.52/5). A strong correlation (r = 0.83) was observed between comprehensiveness and overall quality. Vignettes did not incorporate significant demographic diversity. This study underscores the potential of LLMs in enhancing the scalability, accessibility, and customizability of dermatology education materials. Efforts to increase vignettes’ demographic diversity should be incorporated to increase applicability to diverse populations.https://doi.org/10.1038/s41746-025-01650-x |
| spellingShingle | Arya S. Rao John Kim Andrew Mu Cameron C. Young Ezra Kalmowitz Michael Senter-Zapata David C. Whitehead Lilit Garibyan Adam B. Landman Marc D. Succi Synthetic medical education in dermatology leveraging generative artificial intelligence npj Digital Medicine |
| title | Synthetic medical education in dermatology leveraging generative artificial intelligence |
| title_full | Synthetic medical education in dermatology leveraging generative artificial intelligence |
| title_fullStr | Synthetic medical education in dermatology leveraging generative artificial intelligence |
| title_full_unstemmed | Synthetic medical education in dermatology leveraging generative artificial intelligence |
| title_short | Synthetic medical education in dermatology leveraging generative artificial intelligence |
| title_sort | synthetic medical education in dermatology leveraging generative artificial intelligence |
| url | https://doi.org/10.1038/s41746-025-01650-x |
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