Enhancing Medical Data Analysis Through AI-Enhanced Locally Linear Embedding: Applications in Medical Point Location and Imagery

The rapid evolution of Artificial intelligence in healthcare has opened avenues for enhancing various processes, including medical billing and transcription. This paper introduces an innovative approach by integrating AI with Locally Linear Embedding (LLE) to revolutionize the handling of high-dimensional medical data. This AI-enhanced LLE model is specifically tailored to improve the accuracy and efficiency of medical billing systems and transcription services. By automating these processes, the model aims to reduce human error and streamline operations, thereby facilitating faster and more accurate patient care documentation and financial transactions. This paper provides a comprehensive mathematical model of AI-enhanced LLE, demonstrating its application in real-world healthcare scenarios through a series of experiments. The results indicate a significant improvement in data processing accuracy and operational efficiency. This study not only underscores the potential of AI-enhanced LLE in medical data analysis but also sets a foundation for future research into broader healthcare applications.

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Publication Details

Published
2024-11-19
DOI
https://doi.org/10.1109/icet63392.2024.10935214
Primary Topic
Medical Imaging and Analysis
Type
article
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article

Enhancing Medical Data Analysis Through AI-Enhanced Locally Linear Embedding: Applications in Medical Point Location and Imagery

Muhammad Jawad Bashir, Hassan Abdullah Khalid, Muhammad Mahad Khaliq
Medical Imaging and Analysis
article

Enhancing Medical Data Analysis Through AI-Enhanced Locally Linear Embedding: Applications in Medical Point Location and Imagery

Muhammad Jawad Bashir, Hassan Abdullah Khalid, Muhammad Mahad Khaliq
article en

Abstract

The rapid evolution of Artificial intelligence in healthcare has opened avenues for enhancing various processes, including medical billing and transcription. This paper introduces an innovative approach by integrating AI with Locally Linear Embedding (LLE) to revolutionize the handling of high-dimensional medical data. This AI-enhanced LLE model is specifically tailored to improve the accuracy and efficiency of medical billing systems and transcription services. By automating these processes, the model aims to reduce human error and streamline operations, thereby facilitating faster and more accurate patient care documentation and financial transactions. This paper provides a comprehensive mathematical model of AI-enhanced LLE, demonstrating its application in real-world healthcare scenarios through a series of experiments. The results indicate a significant improvement in data processing accuracy and operational efficiency. This study not only underscores the potential of AI-enhanced LLE in medical data analysis but also sets a foundation for future research into broader healthcare applications.

National University of Sciences and Technology (PK)
Openalex Percentile: Top 75%
Medical Imaging and Analysis
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Enhancing Medical Data Analysis Through AI-Enhanced Locally Linear Embedding: Applications in Medical Point Location and Imagery — Muhammad Jawad Bashir, Hassan Abdullah Khalid, et al. · (2024) | TGRS Research Map | TGRS