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Part 1: Document Description
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Citation |
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Title: |
Implementation of artificial intelligence in the 2025 medical parasitology course at Hallym University |
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Identification Number: |
doi:10.7910/DVN/WBQAOR |
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Distributor: |
Harvard Dataverse |
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Date of Distribution: |
2026-02-26 |
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Version: |
1 |
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Bibliographic Citation: |
Eun Hee Ha, 2026, "Implementation of artificial intelligence in the 2025 medical parasitology course at Hallym University", https://doi.org/10.7910/DVN/WBQAOR, Harvard Dataverse, V1 |
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Citation |
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Title: |
Implementation of artificial intelligence in the 2025 medical parasitology course at Hallym University |
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Identification Number: |
doi:10.7910/DVN/WBQAOR |
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Authoring Entity: |
Eun Hee Ha (College of Medicine, Hallym University) |
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Distributor: |
Harvard Dataverse |
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Access Authority: |
Eun Hee Ha |
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Depositor: |
Cho, A Ra |
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Date of Deposit: |
2026-02-26 |
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Holdings Information: |
https://doi.org/10.7910/DVN/WBQAOR |
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Study Scope |
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Keywords: |
Medicine, Health and Life Sciences |
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Abstract: |
This correspondence describes my experience as a third-year medical student participating in an AI-Implemented Medical Parasitology (AIMP) course from October 27 to December 15, 2025, at Hallym University in South Korea. |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Notes: |
<a href="http://creativecommons.org/publicdomain/zero/1.0">CC0 1.0</a> |
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Other Study Description Materials |
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Supplement 1. The syllabus for the AIMP course at Hallym University provided to students before the start of class.pdf |
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Notes: |
application/pdf |
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Supplement 2. The image dataset used for deep learning training on malaria parasite detection was selected from the dataset used by Reddy et al..zip |
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Notes: |
application/zip |
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Supplement 3. The author’s answer to the AIMP final exam prompt on malaria parasite detection.pdf |
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Notes: |
application/pdf |
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Supplement 4. TensorFlow Lite model for deep learning training on malaria parasite detection.zip |
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Notes: |
application/zip |