Timothy L. Kline

Implementing Artificial Intelligence Algorithms in the Radiology Workflow: Challenges and Considerations

2024 · 24 Zit. · Mayo Clinic Proceedings Digital Health

Reproducibility of Deep Learning Algorithms Developed for Medical Imaging Analysis: A Systematic Review

2023 · 21 Zit. · Journal of Digital Imaging

Reproducible Artificial Intelligence Research Requires Open Communication of Complete Source Code

2020 · 15 Zit. · Radiology Artificial Intelligence

Using a Large Language Model for Postdeployment Monitoring of FDA-Approved Artificial Intelligence: Pulmonary Embolism Detection Use Case

2025 · 5 Zit. · Journal of the American College of Radiology

RIDGE: Reproducibility, Integrity, Dependability, Generalizability, and Efficiency Assessment of Medical Image Segmentation Models

2024 · 5 Zit. · Journal of Imaging Informatics in Medicine

Digital Twin Technology In Radiology

2025 · 3 Zit. · Journal of Imaging Informatics in Medicine

Evaluating prompt and data perturbation sensitivity in large language models for radiology reports classification

2025 · 3 Zit. · JAMIA Open

FDA-approved artificial intelligence products in abdominal imaging: A comprehensive review

2025 · 3 Zit. · Current Problems in Diagnostic Radiology

Best Practices and Checklist for Reviewing Artificial Intelligence-Based Medical Imaging Papers: Classification

2025 · 3 Zit. · Journal of Imaging Informatics in Medicine

Teaching AI for Radiology Applications: A Multisociety‑Recommended Syllabus from the AAPM, ACR, RSNA, and SIIM

2025 · 2 Zit. · Medical Physics

Reproducibility in medical image radiomic studies: contribution of dynamic histogram binning

2022 · 1 Zit. · arXiv (Cornell University)

Teaching AI for Radiology Applications: a Multisociety-Recommended Syllabus from the AAPM, ACR, RSNA, and SIIM

2025 · 1 Zit. · Journal of Imaging Informatics in Medicine

AI in the Loop: functionalizing fold performance disagreement to monitor automated medical image segmentation workflows

2023 · 1 Zit. · Frontiers in Radiology

RIDGE: Reproducibility, Integrity, Dependability, Generalizability, and Efficiency Assessment of Medical Image Segmentation Models

2024 · 0 Zit. · arXiv (Cornell University)

AI in the Loop -- Functionalizing Fold Performance Disagreement to Monitor Automated Medical Image Segmentation Pipelines

2023 · 0 Zit. · arXiv (Cornell University)