DeepSeek for healthcare: do no harm?

AI and Ethics 2026 Open access

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Authors: James Anibal, Steven Bedrick, Hang Nguyen, Jasmine Gunkel, Hannah Huth, Tram Le, Samantha Salvi Cruz, Lindsey Hazen, Bradford J. Wood
DOI: 10.1007/s43681-025-00842-1
Cited by: 2
References: 37

Abstract

Accessibility and cost remain barriers to the adoption of healthcare technology and will determine the impact of breakthroughs like generative AI. However, despite recent advancements in these areas, AI models may still contain biases and be prone to misuse by governments or other power structures with an interest in influencing public opinion. This report examines the potential effects of these "pro-state" biases on the delivery of healthcare. DeepSeek is used as a case study to illustrate the healthcare risks that may arise from unknown or biased post-training methods and other forms of AI knowledge editing. Supplementary Information: The online version contains supplementary material available at 10.1007/s43681-025-00842-1.

Fields of Study & Subjects

Artificial Intelligence in Healthcare and Education Artificial Intelligence Applications Machine Learning in Healthcare

Publication details

Journal / source
AI and Ethics
Publisher
Springer Science and Business Media LLC
Volume
6
Issue
1
Pages
94–94
Publication date
8 January 2026
Language
EN