VirtualTrialHarness (VTHarness) is an agent framework designed to streamline the operationalization of virtual trial protocols by selecting reusable procedural skills for each workflow stage and using explicit contracts to ensure consistent study decisions. It introduces VTBenchmark for evaluating source selection and cohort construction, achieving 95.1% workflow success after requirement clarification versus 60.4% with a standard procedural corpus. The framework demonstrated improved source recovery across language models, increased overlap with analyst code in Mayo Clinic cases, and enabled reference‑matched cohort execution across multiple MIMIC‑IV tasks, supporting investigator review within the Mayo Clinic Virtual Trials Program.
arXiv:2605. 09366v3 Announce Type: replace Abstract: Transforming neuroimaging data into clinically actionable biomarkers is a knowledge-intensive and labor-intensive process.
arXiv:2610.15714v1 Announce Type: new Abstract: Healthcare agents must transform longitudinal clinical evidence into reliable actions through heterogeneous tools and stateful workflows, conditions un...
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:2610.41368v1 Announce Type: new Abstract: Overly restrictive eligibility criteria contribute to widespread enrollment failure in clinical trials, yet published recommendations for how specific...
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:2610.16175v1 Announce Type: new Abstract: Real-world studies (RWS) analyze routinely collected healthcare data, such as electronic health records and insurance claims, to estimate treatment eff...
MedBenchAgent is a multi‑agent framework that automates the entire process of constructing medical vision‑language model benchmarks, from deriving the benchmark specification to generating and auditing evaluation items. It uses a Benchmark Intermediate Representation (BIR) to encode task definitions, evidence mappings, evaluation protocols, and item specifications, separating planning from instantiation. The system achieves a Task‑Space F1 of 90.9% and demonstrates high audit pass rates, outperforming existing task induction methods and showing portability across specialized medical domains.
arXiv:2609.13543v1 Announce Type: new Abstract: LLM agents are predominantly benchmarked on short, single-task trajectories, yet real deployments run for hours under contention, surfacing a different...
arXiv:2606. 28900v1 Announce Type: new Abstract: Doctor agents are moving beyond single-turn answer generation toward evolving clinical decision systems.
arXiv:2609.21859v1 Announce Type: new Abstract: Nearly 90% of drugs entering clinical development ultimately fail, despite billions of dollars in investment. Pharmaceutical companies therefore rely o...
arXiv:2608. 07796v1 Announce Type: new Abstract: Large language models perform strongly on medical knowledge benchmarks, but reliable clinical deployment requires agents to conduct defensible investigations over heterogeneous, longitudinal records: determining what evidence is needed, retrieving and reconciling structured and free-text data, grounding conclusions in verifiable evidence, and deferring cases that cannot be resolved reliably.
RadOnc-Agent is an AI framework that organizes radiotherapy into four clinical phases and offers 26 callable functions via a conversational interface. A large‑language‑model controller maps clinical intent to schema‑constrained calls, maintains patient and workflow context, and routes requests to specialist services. In evaluations, the system achieved high accuracy in single‑function calls (98.79%) and cross‑stage workflow completions (96.50% scripted, 96.67% real‑patient), demonstrating technical feasibility of LLM‑orchestrated coordination across heterogeneous radiotherapy capabilities.