Trustworthy and Practical AI for Healthcare: A Guided Deferral System with Large Language Models
Large language models (LLMs) offer a valuable technology for various applications in healthcare. However, their tendency to hallucinate and the existing reliance on proprietary systems pose challenges in environments concerning critical decision-making and strict data privacy regulations, such as healthcare, where the trust in such systems is paramount. Through combining the strengths and discounting the weaknesses of humans and AI, the field of Human-AI Collaboration (HAIC) presents one front for tackling these challenges and hence improving trust. This paper presents a novel HAIC guided deferral system that can simultaneously parse medical reports for disorder classification, and defer uncertain predictions with intelligent guidance to humans. We develop methodology which builds efficient, effective and open-source LLMs for this purpose, for the real-world deployment in healthcare. We conduct a pilot study which showcases the effectiveness of our proposed system in practice. Additionally, we highlight drawbacks of standard calibration metrics in imbalanced data scenarios commonly found in healthcare, and suggest a simple yet effective solution: the Imbalanced Expected Calibration Error.
Code (0)
등록된 구현이 없습니다.
Tasks
Computational EfficiencyDecision MakingSimilar Papers 제목 키워드 기반
MultiRetNet: A Multimodal Vision Model and Deferral System for Staging Diabetic Retinopathy
Diabetic retinopathy (DR) is a leading cause of preventable blindness, affecting over 100 million people worldwide. In the United States, individuals from lower-income communities face a higher risk of progressing to adv…
Contrastive LearningDeferredSeg:A Multi-Expert Deferral Framework for Medical Image Segmentation
Segmentation models based on deep neural networks demonstrate strong generalization for medical image segmentation. However, they often exhibit overconfidence or underconfidence, leading to unreliable confidence scores f…
Medical Image SegmentationBudgeted Multiple-Expert Deferral
Learning to defer uncertain predictions to costly experts offers a powerful strategy for improving the accuracy and efficiency of machine learning systems. However, standard training procedures for deferral algorithms ty…
Active LearningBACON: A fully explainable AI model with graded logic for decision making problems
As machine learning models and autonomous agents are increasingly deployed in high-stakes, real-world domains such as healthcare, security, finance, and robotics, the need for transparent and trustworthy explanations has…
Decision MakingUnlocking Historical Clinical Trial Data with ALIGN: A Compositional Large Language Model System for Medical Coding
The reuse of historical clinical trial data has significant potential to accelerate medical research and drug development. However, interoperability challenges, particularly with missing medical codes, hinders effective …
Code GenerationData IntegrationLanguage ModelingLanguage Modelling+2