What could we make of AI in plastic surgery education.
Abstract
To explore the possibilities of artificial intelligence (AI) text-to-picture system, DALL·E 2 was used to generated clinical photographs for medical and plastic surgery education. Generic English text was used to guide AI in three categories: subcutaneous tumor, wound and skin tumor. The most clinically accurate images were chosen for the article or for further editing. AI-generated images with variating clinical accuracy in different categories. The most accurate images were the soft-tissue tumors and the least accurate wounds. This study showed that AI text-to-picture system might be worthy tool for medical education.
추출된 의학 개체 (NER)
| 유형 | 영어 표현 | 한국어 / 풀이 | UMLS CUI | 출처 | 등장 |
|---|---|---|---|---|---|
| 해부 | subcutaneous
|
피하조직 | dict | 1 | |
| 합병증 | wound
|
scispacy | 1 | ||
| 합병증 | wounds
|
scispacy | 1 | ||
| 질환 | tumor
|
C0027651
Neoplasms
|
scispacy | 1 | |
| 질환 | tumors
|
C0027651
Neoplasms
|
scispacy | 1 | |
| 질환 | subcutaneous tumor
|
scispacy | 1 | ||
| 질환 | skin tumor
|
scispacy | 1 | ||
| 질환 | soft-tissue tumors
|
scispacy | 1 |
MeSH Terms
Humans; Artificial Intelligence; Surgery, Plastic; Plastic Surgery Procedures; Skin Neoplasms; Soft Tissue Neoplasms
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