ArticleJMIR formative research2025
Representation of Medical Concepts in Emojis Using Medical Subject Headings to Identify Gaps and Opportunities: Cross-Sectional Analysis.
Article in JMIR formative research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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 emojis, such as the anatomical heart and lungs, highlights their potential for health care communication, but significant gaps persist. Objective: This study aims to systematically analyze the representation of medical concepts in emoji by mapping Medical Subject Headings (MeSH) to Unicode emojis, 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 (Unicode Consortium), were used. Embeddings for 2077 MeSH terms and 3055 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 health care 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 artificial intelligence-driven approaches for emoji development to enhance health care communication and accessibility.
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