Skip to main content
Analysis

Proposing the EPICS Standard: The Ethical and Environmental Costs of AI for Graphic Medicine and Health Humanities

Abstract

For Graphic Medicine and Health Humanities to negotiate the use of AI, whether it be for research, scriptwriting, marketing, or even art production, the costs must be clearly recognized. Certainly, AI offers convenience, speed, versatility, and, to a degree, access for those who could not readily utilize the comics medium for their medical narrative or instruction. It must be asked, though, for a field or format that formally requires only a writing implement and stick figures, why is greater ease sought? For that matter, whose labor is being exploited in order to provide such frictionless product? A myriad number of uncompensated artists have had their work scraped in order to make these AI tools functional, and the effect runs largely counter to the core missions of Graphic Medicine, namely giving a voice to the voiceless and honoring nihil de nobis, sine nobis: “nothing about us without us.” Not only does AI work silence the original artists and storytellers, but it also homogenizes through its output, privileging heteronormative, ageist, sexist, and discriminatory representations – definitionally health injustice. Adding to the injury of lost cultural and regional specificity is the actual damage done ecologically by the requisite data centers, particularly to areas already subject to environmental racism. Short of a zero-tolerance approach being employed against AI for Graphic Medicine, the EPICS standard, including needs-based utilization, artist opt-in and reparation, content vetting, and Green AI platforms, must be enforced by the community.

Keywords

artificial intelligence, ethics, health injustice, environmental racism, Sarah Andersen, cishet baseline, heteronormativity, art theft

How to Cite

Lewis, A. D., (2026) “Proposing the EPICS Standard: The Ethical and Environmental Costs of AI for Graphic Medicine and Health Humanities”, Graphic Medicine Review 6(1). doi: https://doi.org/10.7191/gmr.1271

Downloads

Download GMR1271

242

Views

40

Downloads

Share

Author

Downloads

Issue

Publication details

Licence

Creative Commons Attribution 4.0

Identifiers

Peer Review

This article has been peer reviewed.

Acknowledgements

A version of this paper was presented at the Health Humanities Consortium Conference on 10 Feb. 2026 in Indianapolis, Indiana. Any AI images presented were already generated by their credited sources and did not originate in the composition of this paper.

File Checksums (MD5)

  • GMR1271: 2cf4abaec46353b2a2fb66d9d37486f2