arXiv Computer Vision October 9, 2026 By Song Wang, Jie Peng, Davis Hobley, Zachary Plotkin, Tianlong Chen

AutoAdapt: Reliable Few-Shot Adaptation under Clinical Distribution Shifts

Read the original on arXiv Computer Vision →

AutoAdapt is a framework for reliable few-shot adaptation of large pretrained clinical models to new hospitals. It introduces an Adapter that defines a flexible space of adaptation recipes and an Automator that combines these recipes based on evidence from limited adaptation patients. A reliability rule selects the most effective strategy across available patients, enabling state‑of‑the‑art performance on critical care, emergency care, and diagnostic datasets with only a few labeled patients.

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 Computer Vision.

benchmarks

arXiv Computer Vision

Oct 27

MedAdaptAgent: An Automatic Harness for Adapting Medical Imaging Models to New Sites

arXiv:2610.34884v1 Announce Type: new Abstract: Reusing pretrained medical-imaging models offers an alternative to training from scratch. A model trained for the same task may perform poorly at a new...

By Hanxue Gu, Zhonghao Liu, Xingrui Gu, Kang Wang, Yang Yang

llms computer-vision

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

Hugging Face Trending Papers

Sep 9

Learning to Adapt and Calibrate: Score Distribution Alignment for Few-Shot Uncertainty Prediction in Medical VLMs

Uncertainty estimation for medical vision--language models (VLMs) using conformal prediction has gained increasing attention due to its distribution-free coverage guarantees. However, standard conform...

llms fine-tuning multimodal safety

arXiv AI

Jun 11

Tabular Foundation Models for Clinical Survival Analysis via Survival-Aware Adaptation

arXiv:2606. 12006v1 Announce Type: cross Abstract: Predicting time-to-event outcomes such as mortality is a fundamental task in clinical decision-making, commonly addressed through survival analysis.

By Minh-Khoi Pham, Luca Cotugno, Alina Sirbu, Tai Tan Mai, Martin Crane, Marija Bezbradica

fine-tuning benchmarks

arXiv AI

Jul 24

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

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.

By Jessica Sena, Shesadree Priyadarshani, Miguel Contreras, Bharat Gandhi, Scott Siegel, Subhash Nerella, Parisa Rashidi

llms fine-tuning safety

arXiv AI

Jul 22

Biological Amnesia in ICU Time-Series Prediction: A Drift-Adaptive Two-Stream Architecture with Temporal Retrieval

arXiv:2607. 19020v1 Announce Type: cross Abstract: Background: Clinical decision support systems degrade silently as treatment protocols evolve, yet standard adaptation methods treat models as monolithic blocks, unable to distinguish stable patient physiology from shifting institutional practice.

By Fatema Ferdous Tamanna, K. M. Merajul Arefin, Md. Abdul Masud

rag

One email a morning, machine-written

Related stories

MedAdaptAgent: An Automatic Harness for Adapting Medical Imaging Models to New Sites

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

Learning to Adapt and Calibrate: Score Distribution Alignment for Few-Shot Uncertainty Prediction in Medical VLMs

Tabular Foundation Models for Clinical Survival Analysis via Survival-Aware Adaptation

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

Biological Amnesia in ICU Time-Series Prediction: A Drift-Adaptive Two-Stream Architecture with Temporal Retrieval