Algorithmic systems are increasingly woven into healthcare, from triage applications to AI-enabled decision support tools. While such systems promise efficiency and diagnostic accuracy, they also perform a normative function: shaping whose knowledge is treated as credible, which symptoms matter, and how patient testimony is interpreted. This article introduces algorithmic gaslighting to name the patterned dynamic through which testimonial and hermeneutical injustices become recursively stabilized and internalized as patient self-doubt in algorithmically mediated clinical encounters. Building on feminist and disability bioethics accounts of medical gaslighting-where clinicians dismiss or reinterpret patient reports, often in gendered or racialized ways-I extend the analysis to algorithmic contexts. Drawing on Miranda Fricker's theory of epistemic injustice, I show how interlocking sociotechnical mechanisms-data violence, opacity, automated authority, and feedback loops-can displace patient credibility, constrain interpretive resources, and destabilize patient self-trust. These dynamics recalibrate epistemic agency, rendering patients as data objects rather than recognized knowers. To counter such harms, I propose a normative framework aimed at interrupting gaslighting dynamics in algorithmically mediated care, emphasizing rights to contestation, participatory design, critical audits, and shared responsibility. Recognizing algorithmic gaslighting helps safeguard trust, dialogue, and agency in the digital transformation of healthcare.
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Tanaka et al. (2021) studied this question.