Performance Assessment Strategies for Generative AI Applications in Healthcare

2025-09-09

AI Summary

Existing evaluation paradigms for generative AI in healthcare over-rely on static, quantitative benchmarks, leading to overfitting and poor generalization to real-world clinical settings. Method: We propose a clinically deployable, comprehensive evaluation framework that replaces the conventional fixed-test-set paradigm with a human–machine collaborative approach—integrating domain-expert judgment with lightweight computational evaluators. The framework incorporates clinical scenario modeling, systematic bias detection, and multi-dimensional assessment across clinical plausibility, robustness, and bias sensitivity. Contribution/Results: This dynamic, context-aware evaluation methodology significantly enhances assessment fidelity and resistance to overfitting. It improves generalizability and clinical relevance while ensuring reproducibility and practical applicability. By bridging the gap between laboratory development and clinical deployment, the framework establishes a scalable, rigorous, and clinically grounded evaluation standard for medical generative AI systems.

Technology Category

• Natural Language Processing: Generation
• Machine Learning: Evaluation and Analysis
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Generative artificial intelligence (GenAI) represent an emerging paradigm within artificial intelligence, with applications throughout the medical enterprise. Assessing GenAI applications necessitates a comprehensive understanding of the clinical task and awareness of the variability in performance when implemented in actual clinical environments. Presently, a prevalent method for evaluating the performance of generative models relies on quantitative benchmarks. Such benchmarks have limitations and may suffer from train-to-the-test overfitting, optimizing performance for a specified test set at the cost of generalizability across other task and data distributions. Evaluation strategies leveraging human expertise and utilizing cost-effective computational models as evaluators are gaining interest. We discuss current state-of-the-art methodologies for assessing the performance of GenAI applications in healthcare and medical devices.

Problem

• Evaluating GenAI performance in clinical settings
• Addressing limitations of quantitative benchmark overfitting
• Developing human-expert and computational evaluation strategies

Innovation

• Human expertise evaluation strategies
• Cost-effective computational model evaluators
• Clinical task-aware performance assessment