• Harsh Taru
• Aryan Sangwai
• Vaishnavi Shinde
• Mansi Sonawane

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.

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

"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

"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