3 papers
q-bio.QM2025
Reinforcement Learning for Clinical Reasoning: Aligning LLMs with ACR Imaging Appropriateness Criteria
Anni Tziakouri, Filippo Menolascina
Medical imaging has revolutionized diagnosis, yet unnecessary procedures are rising, exposing patients to radiation and stress, limiting equitable access, and straining healthcare…
q-bio.QM2025
Bridging Clinical Narratives and ACR Appropriateness Guidelines: A Multi-Agent RAG System for Medical Imaging Decisions
Satrio Pambudi, Filippo Menolascina
The selection of appropriate medical imaging procedures is a critical and complex clinical decision, guided by extensive evidence-based standards such as the ACR Appropriateness Cr…
cs.CL2025
ModernBERT + ColBERT: Enhancing biomedical RAG through an advanced re-ranking retriever
Eduardo Martínez Rivera, Filippo Menolascina
Retrieval-Augmented Generation (RAG) is a powerful technique for enriching Large Language Models (LLMs) with external knowledge, allowing for factually grounded responses, a critic…