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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: Jun 8, 2025

Biobank for Translational Medicine: Standard Operating Procedures for Optimal Sample Management

Biobank for Translational Medicine: Standard Operating Procedures for Optimal Sample Management

Published on: November 30, 2022

AI-readiness Criteria for Biomedical Data.

Timothy Clark¹, Harry Caufield², Jillian A Parker³

¹University of Virginia.

Biorxiv : the Preprint Server for Biology

|November 1, 2024

Summary

This summary is machine-generated.

The National Institutes of Health

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

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

Published on: September 20, 2018

Area of Science:

Biomedical research and clinical practice
Artificial Intelligence (AI) and Machine Learning (ML) in medicine

Background:

Biomedical research is increasingly adopting AI/ML for deeper insights.
Deep learning methods present challenges in explainability and ethics.
The U.S. National Institutes of Health (NIH) launched Bridge2AI to advance AI/ML in biomedicine.

Purpose of the Study:

To present methods for assessing AI-readiness of biomedical data.
To outline data standards, criteria, and considerations for AI/ML in biomedicine.
To address ethical, legal, and social implications (ELSI) and explainable AI (XAI).

Main Methods:

Development of criteria for AI-readiness of data.
Establishment of data standards perspectives within the Bridge2AI program.
Focus on XAI and ELSI considerations for AI technologies.

Main Results:

Foundational criteria for scientific rigor in biomedical AI.
Methods for assessing data suitability for AI/ML analysis.
Guidelines for ethical design and application of AI in medicine.

Conclusions:

Established criteria are essential for rigorous and ethical biomedical AI.
The Bridge2AI program aims to disseminate data, tools, and methods.
These standards support the responsible integration of AI/ML in healthcare.