ArticleDigital health
The AI-educated patient.
Article in Digital health. 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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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.