ArticleCureus2026
Theory of Artificial Intelligence Adaptation for Linguistic Concordance: Bridging Gaps in Patient-Centered Communication.
Article in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and aims The use of artificial intelligence (AI) for healthcare translation remains underexplored, particularly in the literature, leading to a lack of evidence on how AI can help bridge language barriers between healthcare professionals (HCPs) and patients. This article introduces a new middle-range theory of healthcare communication designed to promote language concordance in AI-integrated healthcare environments. Methods Walker and Avant's theory construction framework was used for theory synthesis, targeted literature review, and theory construction. Cumulative Index to Nursing and Allied Health Literature (CINAHL) and PubMed were searched using MeSH terms, including "artificial intelligence theory AND language concordance" and "artificial intelligence and language concordance." The parameters used included full-text, peer-reviewed articles in English published within the past 10 years. A total of 163 abstracts were reviewed. Most articles were not AI-related and not related to language concordance. Eighteen relevant articles meeting the inclusion criteria were reviewed. The extracted key concepts were analyzed and synthesized into core constructs, followed by the construction of relational statements and theoretical propositions based on the logical connections among the synthesized concepts. Results AI Adaptation for Linguistic Concordance (ALiCon) is a middle-range theory that extends sociotechnical and communication theories. The key concepts, including language discordance, patient safety, HCP-patient relationship, patient engagement, improved clinical outcomes, and equitable technology (digital health and AI-driven technology), serve as the fundamental components of this theory, conceptualizing and operationalizing AI adaptation to achieve linguistic concordance in healthcare. Five propositions explain how these concepts interact with one another. Conclusion ALiCon theory advances nursing science by redefining technology and HCPs as mediators bridging communication gaps in healthcare. The rapid evolution of AI means the theory must be flexible as the technology advances.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.