Work

A framework for evaluating clinical artificial intelligence systems without ground-truth annotations

Year: 2024

Type: article

Abstract: A clinical artificial intelligence (AI) system is often validated on data withheld during its development. This provides an estimate of its performance upon future deployment on data in the wild; those currently unseen but are expected to be encountered in a clinical setting. However, estimating performance on data in the wild is complicated by distribution shift between data in the wild and withheld data and the absence of ground-truth annotations. Here, we introduce SUDO, a framework for evaluating AI systems on data in the wild. Through experiments on AI systems developed for dermatology images, histopathology patches, and clinical notes, we show that SUDO can identify unreliable predictions, inform the selection of models, and allow for the previously out-of-reach assessment of algorithmic bias for data in the wild without ground-truth annotations. These capabilities can contribute to the deployment of trustworthy and ethical AI systems in medicine.

Source: Nature Communications

DOI: 10.1038/s41467-024-46000-9

Authors: Dani Kiyasseh, Aaron Cohen, Chengsheng Jiang, Nicholas Altieri

Institutions: Cedars-Sinai Medical Center, Flatiron Health (United States), New York University

Language: English

Topic: Artificial Intelligence in Healthcare and Education

Subfield: Health Informatics

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $6,790