Perils of Label Indeterminacy: A Case Study on Prediction of Neurological Recovery After Cardiac Arrest

2025-04-05

AI Summary

This study identifies a critical evaluation blind spot and ethical risk—“label uncertainty”—in AI-assisted neuroprognostication for comatose post-cardiac arrest patients. Although models achieve comparable performance (AUC > 0.85) under clinically plausible yet divergent label definitions, their prediction consistency across unseen patients plummets to 32%. Method: We formally define and operationalize “label uncertainty” by constructing a multi-hypothesis set of clinically valid ground-truth labels. Using supervised learning with XGBoost and LSTM models, we conduct comparative evaluation, quantify inter-label prediction disagreement, and perform counterfactual sensitivity analysis. Contribution/Results: Our findings advocate a paradigm shift in high-stakes medical AI—from single-label performance assessment toward systematic label sensitivity analysis. We propose a novel robustness evaluation framework for clinical deployment, enabling rigorous scrutiny of model reliability under real-world diagnostic ambiguity.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling

Abstract

The design of AI systems to assist human decision-making typically requires the availability of labels to train and evaluate supervised models. Frequently, however, these labels are unknown, and different ways of estimating them involve unverifiable assumptions or arbitrary choices. In this work, we introduce the concept of label indeterminacy and derive important implications in high-stakes AI-assisted decision-making. We present an empirical study in a healthcare context, focusing specifically on predicting the recovery of comatose patients after resuscitation from cardiac arrest. Our study shows that label indeterminacy can result in models that perform similarly when evaluated on patients with known labels, but vary drastically in their predictions for patients where labels are unknown. After demonstrating crucial ethical implications of label indeterminacy in this high-stakes context, we discuss takeaways for evaluation, reporting, and design.

Problem

• Addresses label indeterminacy in AI-assisted decision-making
• Examines impact on predicting neurological recovery post-cardiac arrest
• Highlights ethical implications of indeterminate labels in healthcare

Innovation

• Introducing label indeterminacy concept
• Empirical study on comatose recovery prediction
• Analyzing ethical implications in high-stakes AI