Artificial Intelligence Bias in Health Communication: Risks and Strategies for Medical Writers

Abstract

Artificial intelligence (AI) is rapidly changing the field of health communication. Medical writers, who are central to making complex medical information understandable and usable, now face both new opportunities and new risks. AI can speed up content creation, improve workflow efficiency, and scale production. At the same time, it introduces concerns related to bias, accuracy, and accountability. This paper focuses on 3 core types of bias that affect AI-generated content: data-driven bias, algorithmic bias, and human bias. These biases often arise from unrepresentative training data, flawed system design, or lack of contextual understanding. Left unchecked, they can lead to misinformation and worsen health disparities. Medical writers play a critical role in mitigating these risks by evaluating AI outputs for accuracy, completeness, and fairness. When guided by clear standards, collaborative practices, and sound editorial judgments, medical writers can help ensure that AI supports ethical, equitable, and effective health communication. This paper offers practical strategies to help medical writers integrate AI tools responsibly without compromising the integrity, ethics, or patient equity of health communication.