Artificial intelligence, explainability, and the scientific method: A proof-of-concept study on novel retinal biomarker discovery.

ABSTRACT: We present a structured approach to combine explainability of artificial intelligence (AI) with the scientific method for scientific discovery. We demonstrate the utility of this approach in a proof-of-concept study where we uncover biomarkers from a convolutional neural network (CNN) model trained to classify patient sex in retinal images. This is a trait that is not currently recognized by diagnosticians in retinal images, yet, one successfully classified by CNNs. Our methodology consists of four phases: In Phase 1, CNN development, we train a visual geometry group (VGG) model to recognize patient sex in retinal images. In Phase 2, Inspiration, we review visualizations obtained from post hoc interpretability tools to make observations, and articulate exploratory hypotheses.

SUBMITTER: Delavari P 

PROVIDER: S-EPMC10517742 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature