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Showing 9 of 269 academic articles

Application of artificial intelligence to the electrocardiogram

Zachi Itzhak Attia, David Harmon, Elijah Raphael Behr, Paul A. Friedman • European Heart Journal (2021)

Artificial intelligence (AI) has given the electrocardiogram (ECG) and clinicians reading them super-human diagnostic abilities. Trained without hard-coded rules by finding often subclinical patterns in huge datasets, AI transforms the ECG,...

OPENALEX Open Access DOI: https://doi.org/10.1093/eurheartj/ehab649 PMC: 8500024

AI-Driven Integration of Deep Learning With Lung Imaging, Functional Analysis, and Blood Gas Metrics for Perioperative Hypoxemia Prediction

Kecheng Huang, Chujun Wu, Rongpeng Pi, Jieyu Fang • JMIR Medical Informatics (2025)

This viewpoint article explores the transformative role of artificial intelligence (AI) in predicting perioperative hypoxemia through the integration of deep learning with multimodal clinical data, including lung imaging, pulmonary function...

DOAJ Open Access (DOAJ) DOI: 10.2196/73995

Artificial intelligence in diagnostic pathology

Saba Shafi, Anil Vasdev Parwani • Diagnostic Pathology (2023)

Digital pathology (DP) is being increasingly employed in cancer diagnostics, providing additional tools for faster, higher-quality, accurate diagnosis. The practice of diagnostic pathology has gone through a staggering transformation wherei...

OPENALEX cc-by DOI: https://doi.org/10.1186/s13000-023-01375-z PMC: 10546747

Artificial Intelligence for Lentigo Maligna: Automated Margin Assessment via Sox-10-Based Melanocyte Density Mapping

Rieke Löper, Lennart Abels, Daniel Otero Baguer, Felix Bremmer et al. • Dermatopathology (2025)

Lentigo maligna (LM) is a melanoma in situ with high cumulative sun damage. Histological evaluation of resection margins is difficult and time-consuming. Melanocyte density (MD) is a suitable, quantifiable, and reproducible diagnostic crite...

DOAJ Open Access (DOAJ) DOI: 10.3390/dermatopathology13010001

Identification of Aspergillus at section and species levels by artificial intelligence-based microscopic morphology image recognition

Meng Tan, Zhe Guo, Yanyi Wang, Xinyi Xu et al. • Journal of Clinical Microbiology (2026)

ABSTRACT Rapid and accurate identification of Aspergillus species in clinical microbiology laboratories is crucial for aspergillosis diagnosis and antifungal therapy. However, traditional methods still face challenges in distinguishing phyl...

DOAJ Open Access (DOAJ) DOI: 10.1128/jcm.00012-26

Artificial intelligence and statistics

Bin Yu, Karl Kumbier • Frontiers of Information Technology & Electronic Engineering (2018)

Artificial intelligence (AI) is intrinsically data-driven. It calls for the application of statistical concepts through human-machine collaboration during the generation of data, the development of algorithms, and the evaluation of results....

OPENALEX Open Access DOI: https://doi.org/10.1631/fitee.1700813