Comparative accuracy of artificial intelligence versus manual interpretation in detecting pulmonary hypertension across chest imaging modalities: a diagnostic test accuracy meta-analysis
AI-integrated imaging significantly enhances diagnostic accuracy for pulmonary hypertension, with higher sensitivity (0.83) and specificity (0.91) compared to manual interpretation across chest imaging modalities. However, further high-quality trials with externally validated cohorts may be needed to confirm these findings and reduce variability among AI models across diverse clinical settings.
Authors
- Bilal Haider Malik (ORCID: https://orcid.org/0000-0001-8762-7321)
- Muhammad Arham (ORCID: https://orcid.org/0009-0004-4722-9774)
- Faizan Ahmed (ORCID: https://orcid.org/0000-0002-2760-6892)
- Ramsha Ali (ORCID: https://orcid.org/0009-0001-6701-6926)
- Kinza Bakht
- Mushood Ahmed (ORCID: https://orcid.org/0000-0001-9872-8224)
- Rubiya Ali
- Faseeh Haider
- Najam Gohar
- Allah Dad
- Yusra Junaid
- Yasar Sattar
- Maryam Abbasi
- Fawaz Alenezi
- Mohamed Bakr
- Jesus Almendral
- Abdul Mateen
- Swapnil Patel
Institutions
- Shaheed Benazir Bhutto University (PK)
- Karachi Medical and Dental College (PK)
- Duke University (US)
- Rawalpindi Medical University (PK)
- Allama Iqbal Medical College (PK)
- Lahore General Hospital (PK)
- Jinnah Sindh Medical University (PK)
- Shaikh Zayed Medical College and Hospital (PK)
- Dow University of Health Sciences (PK)
- Society of Interventional Radiology (US)
- Memorial Healthcare System (US)
- Jersey Shore University Medical Center (US)
- Shaikh Zayed Postgraduate Medical Institute (PK)
Publication Details
- Journal
- Frontiers in Artificial Intelligence
- Published
- 2026-01-13
- DOI
- https://doi.org/10.3389/frai.2025.1709489
- Primary Topic
- Pulmonary Hypertension Research and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 0.00