Work

Machine unlearning as a governance imperative for clinical AI

Year: 2026

Type: article

Abstract: Clinical AI assumes that the influence of training data can persist indefinitely. This premise fails when patients withdraw consent, evidence evolves, or bias is identified. Machine unlearning aims to remove specific data influence without full retraining. We argue that unlearning readiness should be built into the infrastructure of high-risk healthcare AI across patient autonomy, clinical validity, and system governance, and we outline a governance pathway to keep updates auditable and clinically safe.

Source: npj Digital Medicine

DOI: 10.1038/s41746-026-03050-1

Authors: Anthony Porter, Emily Kirkpatrick, Arpit Garg, Hemanth Saratchandran, Simon Lucey, Johan Verjans

Institutions: Central Adelaide Local Health Network, The University of Adelaide, South Australian Health and Medical Research Institute

Language: English

Topic: Artificial Intelligence in Healthcare and Education

Subfield: Health Informatics

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $4,090