arXiv AI
Development and Feasibility Evaluation of an Edge AI as Medical Device System for Breast Cancer Multidisciplinary Team Meetings
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arXiv AI
Sep 2
Towards AI-Assisted Clinical Trial Matching: Practical Considerations, Multicenter Evaluation, and Real-World Deployment
arXiv:2609.01202v1 Announce Type: cross Abstract: Clinical trials are essential for advancing cancer care and drug development, but many fail because of insufficient patient enrollment. While there i...
arXiv AI
Sep 25
CRISS: A Retrieval-Augmented AI Chatbot for Assisting Cancer Registrars
CRISS is a retrieval‑augmented generation chatbot designed to aid cancer registrars by providing rapid, citation‑supported answers to complex coding and staging questions. It uses a domain‑specific knowledge base of national registry standards, indexed as dense embeddings, to retrieve relevant passages that ground responses generated by a large language model. In evaluations, RAG configurations consistently outperformed non‑RAG baselines across easy, medium, and hard questions, achieving higher grounding and semantic similarity scores while maintaining human oversight for final decisions.
arXiv AI
Jul 15
First, do NOHARM: a medical safety benchmark and randomized study of physician and AI teaming on clinical consultations
arXiv:2512. 01241v4 Announce Type: replace-cross Abstract: Large language models (LLMs) and medical AI tools are routinely used by physicians and patients for medical advice, yet their clinical safety profiles remain poorly characterized.
arXiv AI
Sep 1
From Analytics to Tumor Boards: An Evidence-Linked Multi-Agent Workflow for Oncology Feature Extraction
arXiv:2608.28974v1 Announce Type: new Abstract: Clinically relevant oncology information is distributed across heterogeneous, longitudinal documentation, creating substantial abstraction burden and r...
arXiv AI
Sep 15
ClinAgent: A ReAct-Based Agent for Conversational Access to Clinical Trial Information
ClinAgent is a conversational system that uses a ReAct-based LLM agent to retrieve and synthesize clinical trial information from multiple sources such as ClinicalTrials.gov, PubMed, and a local dataset. The agent iteratively reasons over user queries, selects appropriate tools, and refines its actions to provide grounded, up-to-date responses in natural language across multi-turn interactions. Evaluation across three phases shows that DeepSeek (thinking mode) excels in planning quality while Gemini 3.0 Flash delivers the highest overall performance and expert ratings, demonstrating the promise of agentic AI for improving clinical trial data access.
arXiv AI
Jul 13
Evaluating Retrieval-Augmented Generation vs. Long-Context Input for Clinical Reasoning over EHRs
arXiv:2508. 14817v2 Announce Type: replace-cross Abstract: Objective: To evaluate whether retrieval-augmented generation (RAG) can serve as an efficient alternative to long-context prompting for clinical reasoning over electronic health records (EHRs).