Accepted for/Published in: JMIR Formative Research
Date Submitted: Dec 16, 2024
Date Accepted: Aug 11, 2025
Representation of Medical Concepts in Emoji: Cross-Sectional Analysis Using MeSH to Identify Gaps and Opportunities
ABSTRACT
Background:
Emoji are a universal visual language widely used in digital communication, yet their representation of medical concepts remains limited. The introduction of medical emoji like the anatomical heart and lungs highlights their potential for healthcare communication, but significant gaps persist.
Objective:
To systematically analyze the representation of medical concepts in emoji by mapping Medical Subject Headings (MeSH) to Unicode emoji, identifying gaps in medical emoji representation, and proposing areas for future emoji development.
Methods:
A cross-sectional study was conducted using the sentence transformer model. Digital resources, including the MeSH thesaurus and Unicode emoji set version 15.0, were utilized. Embeddings for 2,077 MeSH terms and 3,055 emojis were generated, and cosine similarity scores were calculated to evaluate the semantic alignment between MeSH terms and emoji descriptions. A threshold of 0.7 was set to indicate a strong semantic match.
Results:
The analysis revealed significant variations in emoji representation across medical categories. "Geographicals" had the highest match rate (33.33%), whereas "Anatomy" showed only 7.94% matches, with 13 of 163 terms exceeding the similarity threshold. Categories such as "Disciplines and Occupations," "Information Science," and "Psychiatry and Psychology" had no matches (0%), highlighting notable gaps. The findings underscore substantial disparities in medical emoji representation, particularly for internal organs, mental health, and specialized disciplines. Limited availability of representative emoji may hinder effective healthcare communication, especially in digital health contexts. This study emphasizes the potential of artificial intelligence to design emojis that address these gaps and improve inclusivity.
Conclusions:
Significant gaps in medical emoji representation across various domains were identified. Future efforts should prioritize underrepresented medical categories and leverage AI-driven approaches for emoji development to enhance healthcare communication and accessibility. Clinical Trial: Not applicable
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