The paper presents a method for detecting spin in clinical trials by automatically identifying outcome switching using large language models (LLMs). The authors trained on 300 pairs of outcomes labeled for semantic similarity, then evaluated baseline text similarity models and open-source LLMs, applying prompt engineering, token‑probability classification, and majority voting. On a test set of 2,496 examples, the LLM‑based approach achieved an F1 score of 0.78 and accuracy of 0.90, surpassing baseline models but falling short of fine‑tuned BERT, and also produced natural‑language explanations that were manually evaluated for quality.
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...
The paper introduces a retrieval‑augmented multi‑agent framework that automatically generates instance‑specific evaluation rubrics for medical language models. By retrieving authoritative medical evidence, decomposing it into atomic facts, and combining these with user interaction constraints, the system produces fine‑grained criteria that outperform GPT‑4o on HealthBench and LLMEval‑Med. The generated rubrics also guide response refinement, improving medical LLM output quality by 9.2%.
arXiv:2609.14819v1 Announce Type: cross Abstract: Large language models (LLMs) have a growing range of applications in medicine, and their evaluation is critical for ensuring they provide benefit and...
arXiv:2607. 09932v1 Announce Type: cross Abstract: Large language models are increasingly used to summarize clinical trial results for healthcare providers, patients, and payers, but their tendency to hallucinate poses significant risks in this high-stakes context.
The paper introduces a multidimensional framework for classifying human‑AI interactions (HAIIs) in clinical trials, categorising interactions by AI tasks, human‑AI relationships, interaction configurations, and interacting human groups. It evaluates the framework by applying it to 15 trials, with categorisation performed by both human reviewers and large language model classifiers. The study demonstrates that LLMs can assist in categorisation while highlighting the continued need for human judgment when trial records are incomplete or ambiguous.
The study investigates how post‑training alignment techniques affect small language models (SLMs) for generating medication leaflets from structured biomedical data. Four alignment methods—supervised fine‑tuning (SFT), direct preference optimization (DPO), odds ratio preference optimization (ORPO), and group relative policy optimization (GRPO)—are evaluated using Qwen‑2.5 models, with a new FDALeaflets dataset derived from OpenFDA drug labels for zero‑shot cross‑regulatory testing. Results show that aligned SLMs can outperform GPT‑5 on reference‑based metrics, while ORPO and GRPO achieve the highest semantic textual similarity scores in‑domain and on FDALeaflets, respectively, highlighting the importance of alignment for semantic fidelity and generalization.
arXiv:2608. 16643v1 Announce Type: cross Abstract: Automated detection of errors in clinical documentation is a promising application of large language models (LLMs), yet decisions to deploy such models rest on benchmarks that evaluate each clinical note in isolation.
arXiv:2609.22734v1 Announce Type: cross Abstract: Clinical domain classification plays an important role in organizing and analyzing large volumes of unstructured medical text. However, medical trans...
arXiv:2509.19375v2 Announce Type: replace-cross Abstract: Large language models are increasingly used for clinical text classification, where overconfident misclassifications can directly affect pati...
Automated detection of errors in clinical documentation is a promising application of large language models (LLMs), yet decisions to deploy such models rest on benchmarks that evaluate each clinical note in isolation. Error-detection benchmarks are typically constructed by injecting errors into notes, such that each erroneous note has a natural counterpart.
arXiv:2605.20292v2 Announce Type: replace Abstract: Numerical time-series models effectively process irregular electronic health record (EHR) trajectories, but do not expose which temporal patterns s...
arXiv:2603. 05308v3 Announce Type: replace-cross Abstract: Assessing whether an article supports an assertion is essential for hallucination detection and claim verification.