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Cross-Sectional Evaluation of Medical Disinformation Safeguards in Consumer-Facing Large Language Model Platforms

Year: 2026

Type: article

Abstract: Unlabelled: This cross-sectional evaluation of six consumer-facing large language model platforms found significant heterogeneity in safeguard performance against the generation of health disinformation, with Claude and ChatGPT demonstrating complete resistance across all prompt types, while Copilot, Meta AI, Grok, and Gemini exhibited substantial vulnerabilities.

Source: JMIR Infodemiology

DOI: 10.2196/89831

Authors: Natansh D Modi, Cyril A Alex, Abdulhalim A Awaty, Bradley D Menz, Stephen Bacchi, Kacper T Gradon, Jessica M Logan, Andrew Rowland, Lisa Kalisch Ellett, Ross A McKinnon, Michael D Wiese, Michael J Sorich, Ashley M Hopkins

Institutions: University of South Australia, Flinders University, The University of Adelaide, University College London

Language: English

Topic: Misinformation and Its Impacts

Subfield: Sociology and Political Science

Field: Social Sciences

Domain: Social Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $1,500

Funders: Johns Hopkins University, Medical Research Council, National Health and Medical Research Council

Awards: Precision Medicine and Cancer Immunotherapy: A Multifactorial Paradigm, Optimising Breast Cancer Treatment: Unravelling the Influence of Concomitant Medicines and Patient Subgroups through Machine Learning and Big Data

OpenAlex ID: W7154997560

FWCI: 61.99

Cites: 8

Cited by: 5

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