Research Paper#Artificial Intelligence, Medical Imaging, Pathology, Multimodal Learning🔬 ResearchAnalyzed: Jan 3, 2026 18:41
PathFound: Agentic AI for Evidence-Seeking Pathology Diagnosis
Published:Dec 29, 2025 15:34
•1 min read
•ArXiv
Analysis
This paper introduces PathFound, an agentic multimodal model for pathological diagnosis. It addresses the limitations of static inference in existing models by incorporating an evidence-seeking approach, mimicking clinical workflows. The use of reinforcement learning to guide information acquisition and diagnosis refinement is a key innovation. The paper's significance lies in its potential to improve diagnostic accuracy and uncover subtle details in pathological images, leading to more accurate and nuanced diagnoses.
Key Takeaways
- •PathFound is an agentic multimodal model designed for evidence-seeking pathological diagnosis.
- •It uses reinforcement learning to refine diagnoses through repeated slide observations and examination requests.
- •PathFound achieves state-of-the-art diagnostic performance and can discover subtle details in images.
Reference
“PathFound integrates pathological visual foundation models, vision-language models, and reasoning models trained with reinforcement learning to perform proactive information acquisition and diagnosis refinement.”