Performance Assessment Strategies for Generative AI Applications in Healthcare
This work tackles the critical problem of reliable performance assessment for generative AI in healthcare, which is essential for safe clinical implementation, though it appears incremental by discussing existing methodologies.
The paper addresses the challenge of evaluating generative AI applications in healthcare, highlighting limitations of current quantitative benchmarks and advocating for strategies that incorporate human expertise and cost-effective computational models to improve generalizability.
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.