ArticleCureus2026
Patient Attitudes Towards Ambient Artificial Intelligence in Clinical Consultations.
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.
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
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background Ambient Artificial Intelligence (AI) technologies are increasingly being integrated into clinical practice to support documentation. These systems employ speech recognition, talk-to-text technology, generative intelligence and large language models (LLMs) to automatically transcribe and summarise clinician-patient consultations into structured clinical notes. While it may reduce documentation burden and enable patient-focused interactions, there remains limited evidence regarding patients' perspectives, experiences and the acceptability of ambient AI use in healthcare settings. Objective Our study aims to explore patients' awareness, attitudes, consent preferences and perceived comfort factors regarding the use of ambient AI during consultations. Methods A cross-sectional anonymous questionnaire survey was conducted among 55 adult patients attending an orthopaedic fracture clinic. The questionnaire included demographic items, Likert-scale questions (Q9-Q21; scored 1-5) assessing attitudes towards ambient AI use, closed-ended preference questions and open-ended free-text responses. Quantitative data were analysed using descriptive statistics, while qualitative free-text responses were analysed using thematic analysis to identify recurring themes and perspectives. Findings General awareness of AI in healthcare was high (72.7%), but detailed knowledge of ambient AI remained limited, with only 10.9% reporting a strong understanding. Participants showed moderate acceptance and trust in clinicians, while expressing concerns about transparency, privacy, consent, openness, accuracy and preservation of patient-clinician relationships during AI use. Conclusions As the adoption of AI in healthcare continues to expand, its implementation strategies should prioritise clinician-led explanation and transparency, active and ongoing consent processes, appropriate human oversight and strong data governance safeguards. Recognising variation in patient attitudes, preferences and concerns, accessible and meaningful opt-out mechanisms are essential to maintain patient autonomy, trust and acceptability.
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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.