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Issues And Trends In Healthcare Delivery System

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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Updated: Aug 16, 2025

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

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Published on: May 10, 2024

Combating Covid-19 using machine learning and deep learning: Applications, challenges, and future perspectives.

Showmick Guha Paul¹, Arpa Saha¹, Al Amin Biswas¹

¹Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh.

Array (New York, N.Y.)

|December 19, 2022

Summary

This summary is machine-generated.

Artificial Intelligence (AI), machine learning (ML), and deep learning (DL) show significant promise in addressing COVID-19 challenges. This review compares various AI, ML, and DL approaches for pandemic management and future research directions.

Keywords:

Artificial intelligence COVID-19 Deep learning Machine learning Pandemic

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Area of Science:

Medical Informatics
Artificial Intelligence in Healthcare
Computational Biology

Background:

The COVID-19 pandemic highlighted the need for advanced technological solutions in healthcare.
Artificial Intelligence (AI) has demonstrated utility in medical imaging, diagnostics, and patient monitoring.
The integration of AI, machine learning (ML), and deep learning (DL) has accelerated during the pandemic.

Purpose of the Study:

To assess the performance of various AI, ML, and DL approaches applied to COVID-19.
To investigate the effectiveness of combined AI, ML, and DL strategies.
To compare standalone and integrated AI-based methods for combating COVID-19.

Main Methods:

Systematic review of recent studies utilizing AI, ML, and DL for COVID-19.
Analysis of diverse data formats and methodologies in AI applications.
Comparative assessment of standalone ML/DL versus combined AI, ML, and DL approaches.

Main Results:

AI, ML, and DL techniques have shown considerable potential in various COVID-19 related tasks.
Combined AI, ML, and DL approaches often outperform standalone methods.
Performance varies based on the specific application and data format.

Conclusions:

AI, ML, and DL are valuable tools for managing the COVID-19 pandemic.
Further research and development of integrated AI models are recommended.
This review provides guidance for developing practical AI applications in healthcare settings.