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Related Experiment Video

Updated: May 26, 2026

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

Machine Learning-Based Data Extraction Tools in Healthcare: A Systematic Review.

Zain Khalpey1, Matthew Rorvig2, Zacharya I Khalpey3

  • 1Cardiothoracic Surgery, HonorHealth, Scottsdale, USA.

Cureus
|May 25, 2026
PubMed
Summary

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Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...

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This summary is machine-generated.

Machine learning (ML) tools enhance healthcare data extraction accuracy (61-98%) and efficiency. Developing a large language model (LLM) for medical data could revolutionize patient care and research by reducing costs.

Area of Science:

  • Digital Health
  • Health Informatics
  • Medical Data Management

Background:

  • The healthcare industry faces a data explosion due to digital transformation.
  • Multimodal data requires advanced management solutions.
  • Machine learning (ML) offers potential for efficient data extraction.

Purpose of the Study:

  • To systematically review the performance and implementation costs of ML-based data extraction tools in healthcare.
  • To assess the potential of large language models (LLMs) in revolutionizing medical data management.

Main Methods:

  • Systematic review following PRISMA guidelines.
  • Searched major databases and grey literature (2018-2024).
  • Analyzed 21 selected studies focusing on ML tool accuracy and costs.
Keywords:
artificial intelligencedata extractionfederated databasehealthcare technologylarge language modelsmachine learningmedical data managementmedical informaticssystematic review

Related Experiment Videos

Last Updated: May 26, 2026

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

Main Results:

  • ML-based extraction showed superior accuracy (61-98%) compared to traditional methods.
  • Implementation costs ranged from $500,000 to $2.5 million.
  • Identified image-based and text-oriented ML extraction tools.

Conclusions:

  • ML-based tools significantly improve healthcare data management.
  • Successful implementation requires careful cost, security, and regulatory consideration.
  • A dedicated LLM could transform healthcare data extraction, benefiting patient care and research.