Representation of Medical Concepts in Emojis Using Medical Subject Headings to Identify Gaps and Opportunities: Cross-Sectional Analysis
Bibliographic record
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".