Enhancing Clinical Data Infrastructure for AI Research: Comparative Evaluation of Data Management Architectures.

ABSTRACT:

Background

The rapid growth of clinical data, driven by digital technologies and high-resolution sensors, presents significant challenges for health care organizations aiming to support advanced artificial intelligence research and improve patient care. Traditional data management approaches may struggle to handle the large, diverse, and rapidly updating datasets prevalent in modern clinical environments.

Objective

This study aimed to compare 3 clinical data management architectures-clinical data warehouses, clinical data lakes, and clinical data lakehouses-by analyzing their performance using the FAIR (findable, accessible, interoperable, and reusable) principles and the big data 5 V's (volume, variety, velocity, veracity, and value). The aim was to provide guidance on sele

SUBMITTER: Gebler R 

PROVIDER: S-EPMC12357119 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature