AutoAdapt: Reliable Few-Shot Adaptation under Clinical Distribution Shifts

2026-10-07

AI Summary

This study addresses the unreliability of selecting and evaluating few-shot adaptation strategies under clinical distribution shifts by proposing the Adapter and Automator architectures. Methodologically, it defines an expanded adaptation space combined with reliability rules to automatically search for optimal strategy combinations. Furthermore, evidence-based weighted fusion and reliability screening mechanisms are introduced to achieve efficient few-shot adaptation. The approach integrates techniques from large model pre-training, few-shot learning, and AutoML. Experimental results demonstrate that the proposed method attains state-of-the-art performance on critical care datasets using only minimal patient data, providing a robust and reliable solution for transfer learning in clinical scenarios.

Abstract

Large pretrained clinical models provide a practical way to reuse learned prior knowledge across hospitals by adapting models to them. In practice, a target hospital may only have a small labeled patient cohort, a setting commonly referred to as few-shot adaptation. This requires making multiple decisions, such as which pretrained model to adapt, how much of the model to update, and which patients to use. Nevertheless, this process faces two primary challenges. First, the best adaptation strategy varies across clinical tasks. Second, evaluating and comparing candidate strategies becomes unreliable due to the small patient cohort. In this work, we introduce AutoAdapt with two core designs to deal with these challenges. The Adapter defines an extensible space of adaptation recipes, and the Automator forms a weighted recipe combination from evidence within the adaptation patients. We propose a reliability rule to ensure that only the most effective strategy on most available patients will be selected. These selected strategies then form a combination for effective few-shot adaptation. We conduct extensive experiments across critical care, emergency care, and diagnostic datasets, and the results show that AutoAdapt consistently achieves state-of-the-art performance using only a few patients for adaptation.

Problem

• few-shot adaptation
• clinical distribution shifts
• pretrained clinical models
• strategy selection
• reliability

Innovation

• Few-Shot Adaptation
• Clinical Distribution Shifts
• AutoAdapt
• Reliability Rule
• Weighted Recipe Combination