The study evaluates the interventional validity of clinical trial simulators by creating an intervention‑blind reference model that withholds intervention identifiers. Using 9,996 within‑trial contrasts from 7,207 phase 2/3 trials, the authors measure how much a simulator’s accuracy improves when it knows the intervention. Results show that a registry‑trained gradient‑boosting model performs poorly on interventional skill, while a large language model reading only the protocol achieves positive interventional skill, and perturbation tests confirm that the model relies on the intervention information.
arXiv:2610.41710v1 Announce Type: new Abstract: Clinical trial failures are a major barrier to efficient drug development, incurring substantial costs and delaying patient access to effective therapi...
arXiv:2608. 14551v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for title-abstract screening in systematic reviews, but their decisions lack calibrated uncertainty.
arXiv:2606. 05263v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards improves reasoning and tool use, yet long-horizon language agents still learn unsupported evidence chains, belief drift, and shortcut actions that satisfy terminal checks.
The paper introduces the concept of intervention fidelity in latent world models, measuring whether a model’s open‑loop transitions align with actual environment interventions. Experiments on TD‑MPC2, Cheetah, and DreamerV3 show that high reward fit does not guarantee fidelity, and that self‑supervised models can outperform task‑anchored ones in preserving intervention effects. The authors propose a capture‑gated audit to localize failures and argue that fidelity must be directly audited on the model’s native interface.
The paper investigates how fine‑tuned language agents can selectively withdraw actions when evidence changes or authority is revoked. Using a synthetic NAQD‑Env environment, the authors compare an original curriculum with an enriched one across multiple system‑prompt framings and evaluate four LoRA checkpoints of Qwen2.5‑3B‑Instruct against instruction‑tuned baselines. Results show that fine‑tuned checkpoints achieve higher decision agreement and withdrawal recall, reduce incorrect withdrawals, and narrow prohibited combinations, though they also omit valid event reports and exhibit mixed preservation of authorized actions.
The study demonstrates that multi‑turn reinforcement learning (RL) in simulation environments significantly improves a language model’s performance on structured, text‑based psychiatric intake tasks. In a randomized, double‑blind crossover trial involving 60 matched scenario pairs, the RL‑trained agent outperformed its base model in 76.7% of cases, showing higher scores on clinical examination, patient‑centered communication, and differential diagnosis appropriateness. Across 127 validated simulated scenarios, the trained agent nearly doubled the base model’s clinical rubric score and reduced under‑triage of acute safety risks by more than half, indicating that simulation provides a controlled testbed for training and stress‑testing psychiatric conversational agents.
FD‑SCoPE is a language‑model framework that answers clinicians’ questions about systematic review evidence tables, exposing the underlying query, selected trials, and derivation rule for each answer. It handles both directly recorded attributes and derived attributes, achieving high accuracy on an oncology evidence table of 159 immune‑checkpoint inhibitor trials. After incorporating expert corrections, its performance on unseen questions improved from 77.9% to 84.9% F1.
The study evaluates JEV, a non‑generative decision model, against the schema‑constrained Qwen Flash for clinical trial prescreening tasks. Across 2,048 patient‑condition pairs from TrialGPT and MIMIC‑III/IV datasets, JEV matched Qwen in macro‑F1 on TrialGPT and MIMIC‑IV but significantly outperformed it on MIMIC‑III, largely due to higher recall for conditions with insufficient information. JEV also demonstrated lower latency and cost per condition, suggesting it could serve as an initial assessment tool that flags information gaps for further review.
The study evaluates counterfactual bias in ten open‑source large language models (LLMs) for pediatric Emergency Severity Index (ESI) prediction. By creating paired clinical vignettes that differ only in demographic or socioeconomic variables, the authors measure shifts in acuity assignment, finding that counterfactual sensitivity varies widely across model families and sizes. A fine‑tuned Qwen2.5‑7B model exhibited the lowest sensitivity, while larger or medical‑domain models sometimes showed greater shifts, highlighting the need for fairness assessment before clinical deployment.
arXiv:2607. 28608v1 Announce Type: new Abstract: Clinical risk models routinely achieve strong aggregate performance while producing materially different error rates across patient subgroups.
arXiv:2609.37858v1 Announce Type: new Abstract: Many localized large language model (LLM) unlearning methods select a small parameter subset from a localization signal and keep it fixed during optimi...
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...