Need for Transparency and Clinical Interpretability in Hemorrhagic Stroke Artificial Intelligence Research: Promoting Effective Clinical Application.

ABSTRACT:

Purpose

This study aimed to evaluate the quality of artificial intelligence (AI)/machine learning (ML) studies on hemorrhagic stroke using the Minimum Information for Medical AI Reporting (MINIMAR) and Minimum Information About Clinical Artificial Intelligence Modeling (MI-CLAIM) frameworks to promote clinical application.

Materials and methods

PubMed, MEDLINE, and Embase were searched for AI/ML studies on hemorrhagic stroke. Out of the 531 articles found, 29 relevant original research articles were included. MINIMAR and MI-CLAIM scores were assigned by two experienced radiologists to assess the quality of the studies.

Results

We analyzed 29 investigations that utilized AI/ML in the field of hemorrhagic stroke, involving a median of 224.5 patients. The majority of stu

SUBMITTER: Lim CY 

PROVIDER: S-EPMC11427125 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature