arXiv:2610.15029v1 Announce Type: cross Abstract: Automated disease classification from patient-reported symptoms requires balancing the speed of lightweight discriminative models against the context...
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: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...
The paper evaluates whether a 9‑billion parameter decision foundation model, Clef‑Flash, is necessary for symptom‑to‑disease classification. Fine‑tuning the model on the 24‑class Symptom2Disease benchmark yields 95.42% accuracy, but simple TF‑IDF linear classifiers achieve higher accuracy (98.75%) with far faster inference. Ablation studies show that predictions can hinge on single descriptive clauses, indicating that the benchmark may not fully capture the model’s capabilities.
arXiv:2608.03854v4 Announce Type: replace Abstract: Quantized large language models can run on consumer hardware, which motivates interest in on-premises processing of sensitive data. The reliability...
arXiv:2604. 26498v3 Announce Type: replace Abstract: The rapid growth of molecular foundation models and large language models (LLMs) has encouraged a scale centred view of AI in drug discovery, in which larger pretrained models are expected to supersede compact cheminformatics models.
arXiv:2601. 06300v2 Announce Type: replace-cross Abstract: Clinical trial amendments frequently introduce delays, increased costs, and administrative burden, with eligibility criteria being the most commonly amended component.
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:2607. 02175v1 Announce Type: new Abstract: Multiple-choice medical benchmarks are increasingly saturated, and recent rubric-based evaluations such as HealthBench have shown that open-ended clinical performance is far from solved - its "Hard" subset top score remains 32%.
The paper introduces Clinical System One (CS1), a 4‑billion‑parameter decision model that rapidly produces typed judgments from clinical records. Trained on 12.4 million Q&A pairs from 3.6 million patients, CS1 can answer multiple questions in a single forward pass, providing probability estimates for uncertain cases. In evaluations on 963,408 held‑out patients and 21 external datasets, CS1 outperformed larger open models and, when paired with a language‑model agent, efficiently identified trial‑eligible patients with far fewer tokens.
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...
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.