Applications of Medical Artificial Intelligence: 4th International Workshop, AMAI 2025, Held in Conjunction with MICCAI 2025, Daejeon, South Korea, September 23, 2025, Proceedings(16206 Lecture Notes in Computer Science)

This book constitutes the refereed proceedings of the 4th International Workshop on Applications of Medical Artificial Intelligence, AMAI 2025, held in conjunction with MICCAI 2025, in Daejeon, South Korea on September 23, 2025. The volume includes 37 papers which were carefully reviewed and selected from 61 submissions. The AMAI 2025 workshop created a forum to bring together researchers, clinicians, domain experts, AI practitioners, industry representatives, and students to investigate and discuss various challenges and opportunities related to applications of medical AI.

Table of Contents:
.- Sequential Organ Motion Prediction via Autoregressive Modeling. .-Seeing More with Less: Video Capsule Endoscopy with Multi-Task Learning. .-TUBA: AI-Assisted Nasogastric Tube Placement Assessment System.-Evaluating Foundation Models with Pathological Concept Learning for Kidney Cancer.-Joint Task Network for Integrating Cognitive Scores and Image Feature in AD Diagnosis.-Interpretable Rheumatoid Arthritis Scoring via Anatomy-aware Multiple Instance Learning.-PanDx: AI-assisted Early Detection of Pancreatic Ductal Adenocarcinoma on Contrast-enhanced CT.-MVMIL: Multi-view Multiple Instance Learning for Whole Slide Image Classification of Bladder Cancer.-Multi-stage Multi-resolution Fusion for Accurate and Efficient Whole Slide Image Segmentation in Colorectal Cancer.-Transformer-Based Instance Detection in 3D Medical Images.-MIRAGE: Retrieval and Generation of Multimodal Images and Texts for Medical Education.-Multitask Deep Learning Model for Liver Segmentation and Lesion Classification from Multisequence MRI.-HU-based Foreground Masking for 3D Medical Masked Image Modeling.-FLOw-Loss: A Hybrid Loss for Centerline-Aware Segmentation in XCA .-XTag-CLIP: Robust and Reliable Thyroid Scar Analysis with Limited Data via Cross-Attention.-Text2Organ: Text-Driven Multimodal Organ Segmentation for CT scans.-Towards Field-Ready AI-based Malaria Diagnosis: A Continual Learning Approach.-Privacy-Centric Seizure Diagnosis via Relation-Aware Fusion of Minimally-Invasive Modalities.-Whole-body Representation Learning For Competing Preclinical Disease Risk Assessment.-Multimodal Sheaf-based Network for Glioblastoma Molecular Subtype Prediction.-Evaluating Large Language Models for Automated Clinical Abstraction in Pulmonary Embolism Registries: Performance Across Model Sizes, Versions, and Parameters.-MFG Sampling: Solving Inverse Problems in Multi-Level High-Frequency Guidance via Diffusion Models.-Vision-Language Sliding Cross Attention for Text-guided Pneumonia Segmentation.-Flexible Multimodal Neuroimaging Fusion for Alzheimer’s Disease Progression Prediction.-Dynamic Robot-Assisted Surgery with Hierarchical Class-Incremental Semantic Segmentation.-A modular deep-learning pipeline for automated aorta characterization on CT.-Echo-Path: Pathology-Conditioned Echo Video Generation.-Domain-Specific Pretraining and Fine-Tuning with Contrastive Learning for Fluorescence Microscopic Image Segmentation.-Automatic Segmentation of Lower-Limb Arteries on CTA for Pre-surgical Planning of Peripheral Artery Disease.-Feature-space Kernel Prediction Network for Denoising of Low-dose Brain CT .-Disentanglement of Biological and Technical Factors via Latent Space Rotation in Clinical Imaging Improves Disease Pattern Discovery.-Towards Automatic Diagnosis of Pediatric Obstructive Sleep Apnoea-Hypopnoea Syndrome using Facial Features.-Benchmarking MRISegmenter++ for Splenomegaly: A Comprehensive Comparative Study.-3D CT-Based Coronary Calcium Assessment: A Feature-Driven Machine Learning Framework.-MoERad: Mixture of Experts for Radiology Report Generation from Chest X-ray Images.-Tabular Data-enhanced Multi-modal Alignment and Synthesis for Alzheimer’s Disease Diagnosis.-Bias-Resilient Feature Learning for Robust Domain Adaptation in Mammography.