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Other literature type . 2026
License: CC BY
Data sources: Datacite
ZENODO
Other literature type . 2026
License: CC BY
Data sources: Datacite
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Assessing the Integration of Artificial Intelligence in Telemedicine: Public Trust, Awareness, and Ethical Frameworks

Authors: Githanjali M, Dr.Clara Shanti, Abdul Razzak Shingati, Adithyan Sunil Kumar, Gagguturu Ahmad, Harsh Nath;

Assessing the Integration of Artificial Intelligence in Telemedicine: Public Trust, Awareness, and Ethical Frameworks

Abstract

Artificial Intelligence has increasingly become a key component in telemedicine. Improved remote diagnosis, patient triaging, continuous monitoring, and engagement. Public perceptions have a vital role to play in shaping the future of telehealth amid its rapid rise with responsible deployment of AI-enabled health care systems. This study seeks to investigate user awareness, trust, benefits, and ethical concerns related to AI in telemedicine, when a poll of a total of 101 participants was conducted. The results indicate that even though most of the participants are aware of the existence of AI, Applications in healthcare, direct experience with such systems is scant. It highlighted a few benefits: speedier preliminary assessments, ease, and more access to health services. However, a number of concerns relating to data were noted: Privacy, diagnostic accuracy, algorithmic fairness, and minimizing human interaction. AI's trust Predominantly, responses were conditional. Several participants would like AI systems to operate under clinician supervision. Such findings reinforce the importance of robust factors include governance, rigid privacy measures, and human-centered design to build trust. The study concludes by proposing an ethical framework to ensure safe and responsible integration. incorporation of AI into telemedicine practice.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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