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

• URL: http://arxiv.org/abs/2512.22182v1
• Date: Fri, 19 Dec 2025 18:14:16 GMT
• Title: Enhancing Medical Data Analysis through AI-Enhanced Locally Linear Embedding: Applications in Medical Point Location and Imagery
• Authors: Hassan Khalid, Muhammad Mahad Khaliq, Muhammad Jawad Bashir,
• Abstract summary: This paper introduces an innovative approach by integrating AI with Locally Linear Embedding (LLE)<n>This AI-enhanced LLE model is specifically tailored to improve the accuracy and efficiency of medical billing systems and transcription services.
• Score: 0.254890465057467
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Research on Medical Named Entity Identification Based On Prompt-Biomrc Model and Its Application in Intelligent Consultation System [2.5889737226898446]
  Our research introduces the Prompt-bioMRC model, which integrates both hard template and soft prompt designs.<n>Our findings consistently demonstrate that our approach surpasses traditional models.<n>This study contributes to advancing automated medical data processing, facilitating more accurate medical information extraction, and supporting efficient healthcare decision-making processes.
  arXiv  Detail & Related papers  (2025-05-08T00:09:02Z)
• Machine Learning Innovations in CPR: A Comprehensive Survey on Enhanced Resuscitation Techniques [52.71395121577439]
  This survey paper explores the transformative role of Machine Learning (ML) and Artificial Intelligence (AI) in Cardiopulmonary Resuscitation (CPR) It highlights the impact of predictive modeling, AI-enhanced devices, and real-time data analysis in improving resuscitation outcomes. The paper provides a comprehensive overview, classification, and critical analysis of current applications, challenges, and future directions in this emerging field.
  arXiv  Detail & Related papers  (2024-11-03T18:01:50Z)
• AI-Driven Healthcare: A Review on Ensuring Fairness and Mitigating Bias [4.9325218275489515]
  This review paper examines the integration of AI in healthcare, highlighting critical challenges related to bias and exploring strategies for mitigation.<n>We emphasize the necessity of diverse datasets, fairness-aware algorithms, and regulatory frameworks to ensure equitable healthcare delivery.<n>The paper concludes with recommendations for future research, advocating for interdisciplinary approaches, transparency in AI decision-making, and the development of innovative and inclusive AI applications.
  arXiv  Detail & Related papers  (2024-07-29T02:39:17Z)
• STLLaVA-Med: Self-Training Large Language and Vision Assistant for Medical Question-Answering [58.79671189792399]
  STLLaVA-Med is designed to train a policy model capable of auto-generating medical visual instruction data. We validate the efficacy and data efficiency of STLLaVA-Med across three major medical Visual Question Answering (VQA) benchmarks.
  arXiv  Detail & Related papers  (2024-06-28T15:01:23Z)
• Automated Information Extraction from Thyroid Operation Narrative: A Comparative Study of GPT-4 and Fine-tuned KoELECTRA [1.137357582959183]
  This study focuses on the transformative capabilities of the fine-tuned KoELECTRA model in comparison to the GPT-4 model. The study leverages advanced natural language processing (NLP) techniques to foster a paradigm shift towards more sophisticated data processing systems.
  arXiv  Detail & Related papers  (2024-06-12T06:44:05Z)
• GAMedX: Generative AI-based Medical Entity Data Extractor Using Large Language Models [1.123722364748134]
  This paper introduces GAMedX, a Named Entity Recognition (NER) approach utilizing Large Language Models (LLMs) The methodology integrates open-source LLMs for NER, utilizing chained prompts and Pydantic schemas for structured output to navigate the complexities of specialized medical jargon. The findings reveal significant ROUGE F1 score on one of the evaluation datasets with an accuracy of 98%.
  arXiv  Detail & Related papers  (2024-05-31T02:53:22Z)
• Automated Fusion of Multimodal Electronic Health Records for Better Medical Predictions [48.0590120095748]
  We propose a novel neural architecture search (NAS) framework named AutoFM, which can automatically search for the optimal model architectures for encoding diverse input modalities and fusion strategies. We conduct thorough experiments on real-world multi-modal EHR data and prediction tasks, and the results demonstrate that our framework achieves significant performance improvement over existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-01-20T15:14:14Z)
• Applying Bayesian Ridge Regression AI Modeling in Virus Severity Prediction [0.0]
  We review the strengths and weaknesses of Bayesian Ridge Regression, an AI model that can be used to bring cutting edge virus analysis to healthcare professionals. The model's accuracy assessment revealed promising results, with room for improvement. In addition, the severity index serves as a valuable tool to gain a broad overview of patient care needs.
  arXiv  Detail & Related papers  (2023-10-14T04:17:00Z)
• Robust and Efficient Medical Imaging with Self-Supervision [80.62711706785834]
  We present REMEDIS, a unified representation learning strategy to improve robustness and data-efficiency of medical imaging AI. We study a diverse range of medical imaging tasks and simulate three realistic application scenarios using retrospective data.
  arXiv  Detail & Related papers  (2022-05-19T17:34:18Z)
• BERT WEAVER: Using WEight AVERaging to enable lifelong learning for transformer-based models in biomedical semantic search engines [49.75878234192369]
  We present WEAVER, a simple, yet efficient post-processing method that infuses old knowledge into the new model. We show that applying WEAVER in a sequential manner results in similar word embedding distributions as doing a combined training on all data at once.
  arXiv  Detail & Related papers  (2022-02-21T10:34:41Z)
• MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluation [110.31526448744096]
  We argue that unlocking this potential requires a systematic way to measure the performance of medical AI models on large-scale heterogeneous data. We are building MedPerf, an open framework for benchmarking machine learning in the medical domain.
  arXiv  Detail & Related papers  (2021-09-29T18:09:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.