Jarrel Seah

Effect of a comprehensive deep-learning model on the accuracy of chest x-ray interpretation by radiologists: a retrospective, multireader multicase study

2021 · 220 Zit. · The Lancet Digital Health

Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations

2024 · 105 Zit. · The Lancet Digital Health

Evaluation of an Artificial Intelligence Model for Detection of Pneumothorax and Tension Pneumothorax in Chest Radiographs

2022 · 59 Zit. · JAMA Network Open

Generative Large Language Models for Detection of Speech Recognition Errors in Radiology Reports

2024 · 58 Zit. · Radiology Artificial Intelligence

Assessment of the effect of a comprehensive chest radiograph deep learning model on radiologist reports and patient outcomes: a real-world observational study

2021 · 46 Zit. · BMJ Open

Prime Time for Artificial Intelligence in Interventional Radiology

2022 · 39 Zit. · CardioVascular and Interventional Radiology

Charting the potential of brain computed tomography deep learning systems

2022 · 30 Zit. · Journal of Clinical Neuroscience

Effects of a comprehensive brain computed tomography deep learning model on radiologist detection accuracy

2023 · 20 Zit. · European Radiology

Artificial intelligence and medical imaging: applications, challenges and solutions

2021 · 13 Zit. · The Medical Journal of Australia

Tackling Algorithmic Bias and Promoting Transparency in Health Datasets: The STANDING Together Consensus Recommendations

2024 · 13 Zit. · NEJM AI

Drafting the Future: The Dawn of AI Report Generation in Radiology

2025 · 8 Zit. · Radiology

League of Radiologists—an End-to-End AI Framework for Scalable and Gamified Radiology Education: A Pilot Implementation in Chest Radiography

2026 · 0 Zit. · Journal of Imaging Informatics in Medicine

Enhancing Radiographic Diagnosis: CycleGAN-based methods for reducing cast shadow artifacts in wrist radiographs

2024 · 0 Zit. · medRxiv