Pith. sign in

REVIEW

Machine Learning Innovations in CPR: A Comprehensive Survey on Enhanced Resuscitation Techniques

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2411.03131 v1 pith:NLYAJO5O submitted 2024-11-03 cs.LG cs.AI

Saidul Islam , Gaith Rjoub , Hanae Elmekki , Jamal Bentahar , Witold Pedrycz , Robin Cohen This is my paper · ORCID
classification cs.LGcs.AI
keywords resuscitationanalysiscomprehensivelearningmachinesurveyai-enhancedapplications
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

This survey paper explores the transformative role of Machine Learning (ML) and Artificial Intelligence (AI) in Cardiopulmonary Resuscitation (CPR). It examines the evolution from traditional CPR methods to innovative ML-driven approaches, highlighting the impact of predictive modeling, AI-enhanced devices, and real-time data analysis in improving resuscitation outcomes. The paper provides a comprehensive overview, classification, and critical analysis of current applications, challenges, and future directions in this emerging field.

Discussion (0). Continue with ORCID to comment.

Pith tools