arXiv AI July 24, 2026 By Jessica Sena, Shesadree Priyadarshani, Miguel Contreras, Bharat Gandhi, Scott Siegel, Subhash Nerella, Parisa Rashidi

A Knowledge-Injection Framework for Zero-Shot Adaptation of LLMs to Delirium Prediction

Read the original on arXiv AI →

arXiv:2607. 20453v1 Announce Type: cross Abstract: Large language models show promise for clinical prediction, but zero-shot performance on specialized tasks is limited by incomplete domain knowledge, especially for smaller locally deployable models.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Jul 21

Retrieval-Augmented Interpretable Learning: Towards Task-Specific Zero-Shot Models in Healthcare

arXiv:2607. 17508v1 Announce Type: cross Abstract: We introduce Retrieval-Augmented Interpretable Learning (RAIL), a probabilistic meta-learning framework for zero-shot generation of task-specific interpretable models that synthesizes coefficient-space structure from natural-language task descriptions and a memory of previously learned task-specific predictors.

By Sazan Mahbub, Caleb Ellington, Zhiyuan Li, Yixin Yang, Souvik Kundu, Ben Lengerich, Eric P. Xing

rag safety

arXiv AI

Aug 3

EarlyDx: An Admission-Anchored Benchmark for Open-Ended Generation of Evidence-Supported ED-Encounter Diagnoses

arXiv:2607. 28788v1 Announce Type: new Abstract: Clinical diagnosis at hospital admission must be made rapidly from limited, incomplete evidence.

By Jiahui Li, Ruili Fang, Zishuai Liu, Yutong Guo, Nan Yang, Wenzhan Song, Jin Lu, Fei Dou

llms benchmarks

arXiv Machine Learning

Jul 9

Counterfactual Modeling with Fine-Tuned LLMs for Health Intervention Design and Sensor Data Augmentation

arXiv:2601. 14590v3 Announce Type: replace Abstract: Counterfactual explanations (CFEs) provide human-centric interpretability by identifying the minimal, actionable changes required to alter a machine learning model's prediction.

By Shovito Barua Soumma, Asiful Arefeen, Stephanie M. Carpenter, Melanie Hingle, Hassan Ghasemzadeh

llms fine-tuning multimodal safety

arXiv Computation and Language

3d ago

MedKIT: Evaluating Knowledge Integration and Generalization in Large Language Models

arXiv:2609.38543v1 Announce Type: new Abstract: Constantly evolving real-world knowledge necessitates models to be updated continuously. Especially in medicine, as clinical evidence changes over time...

By Lukas Thede, Yash Kumar Atri, David Chen, Danielle Bitterman, Matthias Bethge, Tom Hartvigsen, Zeynep Akata

llms benchmarks safety

arXiv Machine Learning

2d ago

Learning to Select Source-Traceable Evidence for Language-Model Prediction from Irregular Clinical Time Series

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...

By Kwanhyung Lee, Juhwan Choi, Jongheon Kim, Joohyung Lee, Hyeongwon Jang, Jeonguk Lee, Jisoo Jung, Eunho Yang

llms benchmarks

arXiv AI

Jun 8

REMEDI: A Benchmark for Retention and Unlearning Evaluation in Multi-label Clinical Disease Inference

arXiv:2606. 07141v1 Announce Type: cross Abstract: Language models trained for clinical disease inference are trained on patient data, which may include sensitive and private information, and data owners may request the removal of their data from a trained model due to privacy or copyright concerns.

By Anurag Sharma, Sai Teja Chunchu, Prasenjit Mitra, Sandipan Sikdar, Koustav Rudra

llms benchmarks safety

One email a morning, machine-written

Related stories

Retrieval-Augmented Interpretable Learning: Towards Task-Specific Zero-Shot Models in Healthcare

EarlyDx: An Admission-Anchored Benchmark for Open-Ended Generation of Evidence-Supported ED-Encounter Diagnoses

Counterfactual Modeling with Fine-Tuned LLMs for Health Intervention Design and Sensor Data Augmentation

MedKIT: Evaluating Knowledge Integration and Generalization in Large Language Models

Learning to Select Source-Traceable Evidence for Language-Model Prediction from Irregular Clinical Time Series

REMEDI: A Benchmark for Retention and Unlearning Evaluation in Multi-label Clinical Disease Inference