Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource David, Sonia; Sadasivan, Anupama Title A name given to the resource Tech-Enabled Transformations in Gender-Inclusive Healthcare: A Critical Interpretive Synthesis of Artificial Intelligence in India Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Artificial Intelligence for Predictive Healthcare: Towards Personalized Treatment and Disease Prevention;pp.164-183 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1201/9781003674542-10" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003674542-10</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105034230620?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105034230620?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant David S., Department of Psychology, JAIN (Deemed to be) University, Bangalore, India; Sadasivan A., School of Psychological Sciences, CHRIST University, Bangalore, India Description An account of the resource While artificial intelligence (AI) has the potential to revolutionize healthcare, it also runs the risk of exacerbating structural injustices for Indias gender-diverse communities. This critical interpretive synthesis explores the effects of AI-enabled health devices on LGBTQIA+ inclusion, drawing on intersectional feminism, queer theory, and constructivist learning. Reviewing 30 interdisciplinary studies (20102024), three themes emerge: (1) algorithmic bias: AI systems replicate gendered and caste-based exclusions through non-representative datasets and heteronormative design; (2) structural barriers: infrastructural patriarchy, limited gender-sensitive governance, and gaps in Indias AI policy; and (3) digital inequities: low digital literacy, moral conservatism, and caste hierarchies restrict access to affirming care. Research shows AI often erases or misgenders trans and nonbinary identities, causing epistemic harm. Nonetheless, inclusive innovation is possible through participatory, queer-led AI design. The study warns that without centering intersectional justice, AI in healthcare risks amplifying marginalization and epistemic violence. It recommends co-creation with LGBTQIA+ stakeholders, gender-sensitive audits, and care-centered policy reforms. Rejecting techno-solutionism, it advances a Global South, justice-focused approach that prioritizes equity, contextual awareness, and lived realities in AI healthcare design. 2026 selection and editorial matter, Syed Nisar Hussain Bukhari; individual chapters, the contributors. Publisher An entity responsible for making the resource available CRC Press Relation A related resource ISBN: 978-104058017-2; 978-100367454-2; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online