arXiv AI By Yin Fang, Qiao Jin, Shubo Tian, Lauren He, Maya Geer, Noor Naffakh, Ryan Huu-Tuan Nguyen, Zifeng Wang, Jimeng Sun, Charalampos S. Floudas, James L. Gulley, Kamilia Moalem, Catarina Martins Maia, Amanda Nottke, Juan W. Valle, Melinda Bachini, Lourdes Rocha-Nussbaum, Kari Ramage, Nikita Curry, Megan Barnes, Mandy Mansaray, Darlene Gabeau, Craig E. Grossman, Heath Skinner, Michael Burczynski, NIH-TrialBench Consortium, Zhiyong Lu

Towards AI-Assisted Clinical Trial Matching: Practical Considerations, Multicenter Evaluation, and Real-World Deployment

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arXiv AI
Aug 14

MatchMiner-AI: Open-source, Privacy-preserving Cancer Clinical Trial Matching using Artificial Intelligence

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.

By Jennifer Altreuter, Pavel Trukhanov, Morgan A. Paul, Michael J. Hassett, Irbaz B. Riaz, Muhammad Umar Afzal, Arshad A. Mohammed, Ayub Umair, Huan He, Chueh Husan Hsu, Sarah Sammons, James Lindsay, Emily Mallaber, Harry R. Klein, Gufran Gungor, Matthew Galvin, Michael Deletto, Sabrina Y. Camp, Stephen C. Van Nostrand, James Provencher, Joyce Yu, Naeem Tahir, Jonathan Wischhusen, Olga Kozyreva, Taylor Ortiz, Hande Tuncer, Jad El Masri, Alys Malcolm, Tali Mazor, Ethan Cerami, Kenneth L. Kehl
arXiv AI
Oct 2

OpenMTB-Audit: Exposing Over-Refusal and Clinical Expert Perspectives in LLM-Based Molecular Tumor Board Safety Evaluation

OpenMTB‑Audit is an open‑source benchmark that tests large language models on 500 synthetic non‑small cell lung cancer cases, covering five adversarial error categories and four safety labels: Supported, Partially Supported, Unsupported, and Insufficient Information. The study found that all eight tested LLMs over‑refused Partially Supported recommendations, collapsing labels to achieve high safety scores. A deterministic seven‑module framework, MTB‑AuditAgent, was introduced to reduce over‑refusal to 6.7% and reach 91.2% accuracy, while an oncologist annotation study highlighted disagreement around the boundary between information sufficiency and treatment optimization.

By Negin Ashrafi, Jia Luo, Stacey M. Frumm, Roxana Daneshjou
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.

By Antonino Vaccarella, Riccardo Cantini, Domenico Talia, Paolo Trunfio, Marianna Talia, Rosamaria Lappano, Marcello Maggiolini