Johannes F. Plate

State-of-the-Art in Responsible, Explainable, and Fair AI for Medical Image Analysis

2025 · 20 Zit. · IEEE Access

Fair AI-powered orthopedic image segmentation: addressing bias and promoting equitable healthcare

2024 · 19 Zit. · Scientific Reports

Explainable AI in Orthopedics: Challenges, Opportunities, and Prospects

2023 · 18 Zit.

Generative AI in orthopedics: an explainable deep few-shot image augmentation pipeline for plain knee radiographs and Kellgren-Lawrence grading

2024 · 8 Zit. · Journal of the American Medical Informatics Association

Enforcing Explainable Deep Few-Shot Learning to Analyze Plain Knee Radiographs: Data from the Osteoarthritis Initiative

2023 · 6 Zit.

Explainable AI in Orthopedics: Challenges, Opportunities, and Prospects

2023 · 4 Zit. · arXiv (Cornell University)

HexAI-TJAtxt: A textual dataset to advance open scientific research in total joint arthroplasty

2023 · 3 Zit. · Data in Brief

Learning Unbiased Image Segmentation: A Case Study with Plain Knee Radiographs

2023 · 2 Zit.

Uncertainty quantification and explainable AI in orthopaedic imaging: A timely call to action

2025 · 2 Zit. · Journal of Clinical Orthopaedics and Trauma

AI Fairness in Hip Bony Anatomy Segmentation: Analyzing and Mitigating Gender and Racial Bias in Plain Radiography Analysis

2023 · 1 Zit.

Trustworthy and Uncertainty-Aware AI for Predicting Respiratory Complications Following Total Hip and Knee Arthroplasty.

2024 · 0 Zit. · PubMed

Trustworthy and Uncertainty-Aware AI for Predicting Respiratory Complications Following Total Hip and Knee Arthroplasty

2025 · 0 Zit. · PubMed Central

Self-supervised Representation Learning for AI-Based Musculoskeletal Radiograph Registry Construction

2026 · 0 Zit. · Lecture notes in computer science

KneeXNet-2.5D: a clinically-oriented and explainable deep learning framework for MRI-based knee cartilage and meniscus segmentation

2026 · 0 Zit. · npj Health Systems

Learning Unbiased Image Segmentation: A Case Study with Plain Knee Radiographs

2023 · 0 Zit. · arXiv (Cornell University)