TY - GEN
T1 - AI-Driven Innovations for Secure and Efficient Medical Consultations
AU - Alawida, Moatsum
AU - Aljaghbeir, Ahmad Nasser
AU - Albaz, Khaled Raed
AU - El Zaher, Heba Nabil
AU - El Ayoubi, Moutasim Billah
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - The escalating demand for accessible and efficient healthcare, coupled with advancements in artificial intelligence (AI), motivates a paradigm shift in medical consultations. This research introduces the Hayat Medical Consultation System, aiming to revolutionize communication and data management in healthcare. By integrating AI-driven solutions, the system addresses longstanding challenges, including secure medical image watermarking and accurate speaker diarization. This paper reviews existing literature on medical image watermarking and speaker diarization techniques, proposing an integrated solution that balances security, quality preservation, and robustness. The Hayat System's approach encompasses secure image watermarking, advanced speaker diarization, AI-driven speech summarization, and a custom-built chatbot assistant. Tested on various aspects such as efficiency and security, the system effectively summarizes consultation descriptions using AI-based ChatGPT, allowing doctors to review and align summaries with diagnoses. Additionally, the system successfully encrypts data using RC4 and embeds it into medical images, addressing critical challenges in data transfer, integrity, and the secure management of patient records across different healthcare facilities.
AB - The escalating demand for accessible and efficient healthcare, coupled with advancements in artificial intelligence (AI), motivates a paradigm shift in medical consultations. This research introduces the Hayat Medical Consultation System, aiming to revolutionize communication and data management in healthcare. By integrating AI-driven solutions, the system addresses longstanding challenges, including secure medical image watermarking and accurate speaker diarization. This paper reviews existing literature on medical image watermarking and speaker diarization techniques, proposing an integrated solution that balances security, quality preservation, and robustness. The Hayat System's approach encompasses secure image watermarking, advanced speaker diarization, AI-driven speech summarization, and a custom-built chatbot assistant. Tested on various aspects such as efficiency and security, the system effectively summarizes consultation descriptions using AI-based ChatGPT, allowing doctors to review and align summaries with diagnoses. Additionally, the system successfully encrypts data using RC4 and embeds it into medical images, addressing critical challenges in data transfer, integrity, and the secure management of patient records across different healthcare facilities.
KW - AI-based Summarization
KW - AI-driven Solutions
KW - Medical Image Watermarking
KW - Secure Medical Imaging
KW - Speaker Diarization
UR - https://www.scopus.com/pages/publications/105003201655
U2 - 10.1109/BDCAT63179.2024.00024
DO - 10.1109/BDCAT63179.2024.00024
M3 - Conference contribution
AN - SCOPUS:105003201655
T3 - Proceedings - 2024 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2024
SP - 85
EP - 90
BT - Proceedings - 2024 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 11th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2024
Y2 - 16 December 2024 through 19 December 2024
ER -