UGC Approved Journal no 63975(19)

ISSN: 2349-5162 | ESTD Year : 2014
Call for Paper
Volume 11 | Issue 10 | October 2024

JETIREXPLORE- Search Thousands of research papers



WhatsApp Contact
Click Here

Published in:

Volume 10 Issue 11
November-2023
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

7.95 impact factor calculated by Google scholar

Unique Identifier

Published Paper ID:
JETIR2311405


Registration ID:
528499

Page Number

e32-e35

Share This Article


Jetir RMS

Title

Enhancing Medicine Kiosk Efficiency Through AI Integration CURE A.I.

Abstract

The rapid advancement of telemedicine has brought healthcare accessibility to remote and underserved populations. Telemedicine kiosks serve as a critical bridge between patients and healthcare providers, offering vital services. However, there is a growing need to optimize the functionality of these kiosks to ensure the highest quality of care. Existing telemedicine kiosks often lack the efficiency and accuracy needed for optimal patient care. Delays, misdiagnoses, and ineffective communication between patients and AI systems have hindered the potential of these kiosks. This study aims to address these challenges and improve the overall functionality of telemedicine kiosks using AI. To solve this problem, we propose the integration of advanced artificial intelligence (AI) algorithms, natural language processing (NLP), and computer vision technologies into telemedicine kiosks. AI will assist in faster symptom recognition, secure patient data management, and real-time communication with healthcare providers, enhancing the overall patient experience. Preliminary results indicate significant improvements in kiosk performance, reducing misdiagnoses and wait times. AI-driven systems can efficiently process patient data, facilitating more accurate diagnoses and enabling better communication between patients and healthcare professionals. The successful integration of AI into telemedicine kiosks has far-reaching implications. It promises to enhance healthcare access, particularly in remote areas, improve diagnosis accuracy, and streamline the telemedicine experience. Ultimately, this innovation will lead to better patient outcomes and more efficient healthcare delivery, marking a significant step towards the future of AI-driven telemedicine kiosks.

Key Words

Artificial Intelligence, healthcare, diagnose, Computer Vision, Healthcare accessibility, Remote healthcare.

Cite This Article

"Enhancing Medicine Kiosk Efficiency Through AI Integration CURE A.I.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 11, page no.e32-e35, November-2023, Available :http://www.jetir.org/papers/JETIR2311405.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"Enhancing Medicine Kiosk Efficiency Through AI Integration CURE A.I.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 11, page no. ppe32-e35, November-2023, Available at : http://www.jetir.org/papers/JETIR2311405.pdf

Publication Details

Published Paper ID: JETIR2311405
Registration ID: 528499
Published In: Volume 10 | Issue 11 | Year November-2023
DOI (Digital Object Identifier):
Page No: e32-e35
Country: Pune , Maharashtra, India .
Area: Science & Technology
ISSN Number: 2349-5162
Publisher: IJ Publication


Preview This Article


Downlaod

Click here for Article Preview

Download PDF

Downloads

000168

Print This Page

Current Call For Paper

Jetir RMS