Feb 2026· Digital Health· Vol 12· 0 citations· 23 references
Medicine
TL;DR
The concept of the “AI-educated patient” and a related conceptual framework, “soft accountability”, are introduced, describing the informal pressure that may arise when well-informed patients enter consultations with structured expectations.
Abstract
Patients increasingly consult generative artificial intelligence (GenAI) tools before and after clinical encounters. We argue this represents a qualitative shift beyond the “Dr. Google” era: large language models (LLMs) synthesise information into coherent, guideline-framed narratives that may raise the baseline knowledge patients bring to consultations. We introduce the concept of the “AI-educated patient” and a related conceptual framework, “soft accountability”, describing the informal pressure that may arise when well-informed patients enter consultations with structured expectations. We discuss potential mechanisms by which AI-mediated patient education could influence clinician behaviour, while framing these explicitly as hypotheses awaiting empirical testing. Substantial risks remain, including hallucinations, false patient confidence, inequities in AI access and literacy, clinician workload implications, and privacy concerns. The opportunity and challenge is to harness this shift equitably for both sides of the clinical relationship.
This critical narrative review synthesizes evidence from the past decade on AI dependency among college students, focusing on conceptualizations, theoretical frameworks, prevalence and demographic variations, measurement tools, determinants, intervention strategies, and research gaps.
Xuehua He, Shan Li, Rongping Cha et al.· Frontiers in Psychology· 0 citations
Combining the power of artificial intelligence (AI) and the clinical data within Electronic Health Records is an innovation that may provide actionable educational insights on learners' experiences in the clinical learning environment. However, the adoption of such processes highlights significant systemic challenges. The "digital divide" poses a risk of inequity, as institutions with sophisticated data architectures can provide superior precision feedback compared to resource-limited centers. Also, the generalizability of AI models remains a concern, as tools trained on local coding patterns and patient populations may not translate across diverse clinical learning environments without rigorous calibration. Finally, the ability to quantify learners' clinical exposures raises fundamental questions regarding who defines the "adequacy" of clinical experiences. This commentary articulates how the medical education community must thoughtfully address these policy and technical challenges to help AI reach its potential to enhance physician training.
B. Thoma, T. Chan· Academic medicine : journal...· 0 citations
A narrative review of the available evidence presents a narrative review of the available evidence on the effect of LLMs on diagnostic reasoning, the optimal design of clinician-LLM interaction, the appropriate timing of consultation during the clinical encounter, the safest models of clinical-AI integration, and the main risks associated with their use.
L. Corral-Gudino, M. Ramos-Casals, M. Marcos et al.· Medicina clínica (Ed. impres...· 0 citations
Artificial intelligence is reshaping how physicians reason, yet the cognitive dimension of sustained physician-LLM interaction remains conceptually undefined. This Commentary proposes that repeated, methodologically guided dialogue with personalised large language models-termed Clinical Cognitive Interaction-progressively imprints the physician's epistemic signature onto a personalised exocortex, giving rise to an emergent Human-LLM Cognitive Unit. Whether this interaction promotes cognitive flattening and deskilling or genuinely augments clinical reasoning depends not on the technology itself but on the interaction's epistemic quality. We argue that medical education must move beyond AI literacy to teaching Clinical Cognitive Interaction as a core professional competency, thereby augmenting rather than diminishing the physician's central cognitive role.
Salvatore Corrao· QJM : monthly journal of the...· 0 citations
Two short vignettes and a design guide to help human factors researchers create explanation systems that are practical and trustworthy are presented to show how explainability can become part of the care system instead of being treated as a separate technical feature.
T. Mamun, Laurie Novak, M. Salwei· Proceedings of the Internati...· 0 citations
GenAI should be viewed as a powerful augmentative tool, not a replacement for human educators, and its successful integration will depend on leveraging its strengths to enhance efficiency and scalability while preserving the essential humanistic elements of medical practice through expert oversight and validation.
R. Xie, Bei-En Zhang, Lifeng Xiao· Frontiers in Medicine· 0 citations
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