Anomaly detection in medical via multimodal foundation models

Area of Science:

• Artificial Intelligence in Medicine
• Medical Data Analysis
• Computational Pathology

Background:

• Traditional AI struggles with temporal and semantic relationships in clinical data.
• Limited generalization and interpretability hinder real-world AI applications in healthcare.
• Need for robust medical anomaly detection systems.

Purpose of the Study:

• To develop a novel AI framework for medical anomaly detection.
• To improve the generalization and interpretability of AI models in clinical settings.
• To address challenges in capturing complex temporal and semantic relationships in healthcare data.

Main Methods:

• Proposed a framework integrating symbolic representations, a graph-based neural model (PathoGraph), and knowledge-guided refinement.
• Leveraged structured clinical records, evolving symptom graphs, and medical ontologies.
• Built semantically interpretable latent spaces for enhanced robustness.

Main Results:

• The PathoGraph model outperformed existing baselines in detecting rare comorbidity patterns.
• Demonstrated superior performance in identifying abnormal treatment responses.
• Showcased improved model robustness under sparse supervision and distributional shifts.

Conclusions:

• The proposed framework offers a generalizable and explainable solution for healthcare anomaly detection.
• Aligning domain knowledge with multimodal AI enhances trustworthiness for clinical deployment.
• PathoGraph advances the application of AI in medical anomaly detection and clinical decision support.