biostudies-literatureUnknown8Ahmed F<h4>Background</h4>Accurate risk stratification in pulmonary hypertension (PH) is integral for optimizing therapeutic strategies and improving patient outcomes. Recent artificial intelligence (AI) models have demonstrated notable efficacy in risk stratification of PH, achieving area under the curve (AUC) values of 0.94 and 0.81 in internal and external validation cohorts, respectively. This meta-analysis aims to demonstrate the effectiveness of AI models in the risk stratification of PH by comparing their performance to conventional risk stratification methods.<h4>Methods</h4>A systematic search of five databases (PubMed, Embase, ScienceDirect, Scopus, and the Cochrane Library) was conducted from inception to March 2025. Statistical analysis was performed in R (version 2024.12.1 + 563) usiFrontiers in artificial intelligence1692829https://www.ebi.ac.uk/biostudies/studies/S-EPMC12673395biostudies-literatureComparative diagnostic accuracy of artificial intelligence-derived risk stratification versus conventional risk stratification methods in pulmonary hypertension patients: a systematic review and meta-analysis.PMC12673395Sealove BDad AHassan MAlmendral JHaider FMoshiyakhov MLajczak PAhmed MAdnan MHashim MMAArham MAthar FBGohar NBakht KUsman MAhmed FPatel SAlenezi FSattar YBakr MMirza TfalseComparative diagnostic accuracy of artificial intelligence-derived risk stratification versus conventional risk stratification methods in pulmonary hypertension patients: a systematic review and meta-analysis.<h4>Background</h4>Accurate risk stratification in pulmonary hypertension (PH) is integral for optimizing therapeutic strategies and improving patient outcomes. Recent artificial intelligence (AI) models have demonstrated notable efficacy in risk stratification of PH, achieving area under the curve (AUC) values of 0.94 and 0.81 in internal and external validation cohorts, respectively. This meta-analysis aims to demonstrate the effectiveness of AI models in the risk stratification of PH by comparing their performance to conventional risk stratification methods.<h4>Methods</h4>A systematic search of five databases (PubMed, Embase, ScienceDirect, Scopus, and the Cochrane Library) was conducted from inception to March 2025. Statistical analysis was performed in R (version 2024.12.1 + 563) usi2025-01-01T00:00:00Z20252026-06-08T04:55:15.773Z2026-06-08T03:08:33.507ZS-EPMC126733954134685510.3389/frai.2025.1692829