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...
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: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:2608.28974v1 Announce Type: new Abstract: Clinically relevant oncology information is distributed across heterogeneous, longitudinal documentation, creating substantial abstraction burden and r...
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: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).
arXiv:2412. 17228v4 Announce Type: replace Abstract: Background: Clinical trials are essential to advancing cancer treatments, but fewer than 10% of adults with cancer enroll in therapeutic trials.
arXiv:2606. 18203v1 Announce Type: cross Abstract: The LLM-empowered personal health agents with user health (sensor) metrics have offered a promising pathway to alleviate global disparities in healthcare access.
arXiv:2608.29241v1 Announce Type: new Abstract: Clinical voice agents are now deployed in routine care, where real patients do not wait their turn: they interrupt. These systems typically use a casca...
arXiv:2607. 25485v1 Announce Type: new Abstract: Health AI is evolving from answering questions to agentic systems that converse with patients, reason about health records, and act on their behalf.
arXiv:2509. 02594v3 Announce Type: replace-cross Abstract: Evaluating large language models (LLMs) on their ability to generate high-quality, accurate, situationally aware answers to clinical questions requires going beyond conventional benchmarks to assess how these systems behave in complex, high-stakes clinical scenarios.
The study evaluates AI-generated summaries for cancer patients using a dual assessment framework that includes human experts and LLM-as-a-judge. Human domain experts—oncology clinicians and patient-facing care staff—assess summary quality on accuracy, clinical relevance, and readability. The research identifies limitations such as omissions and minor inaccuracies, which are then used to iteratively refine prompts, grounding, and safety guardrails.