2-Factor Retrieval for Improved Human-AI Decision Making in Radiology

Human-machine teaming in medical AI requires us to understand to what degree a trained clinician should weigh AI predictions. While previous work has shown the potential of AI assistance at improving clinical predictions, existing clinical decision support systems either provide no explainability of their predictions or use techniques like saliency and Shapley values, which do not allow for physician-based verification. To address this gap, this study compares previously used explainable AI techniques with a newly proposed technique termed '2-factor retrieval (2FR)', which is a combination of interface design and search retrieval that returns similarly labeled data without processing this data. This results in a 2-factor security blanket where: (a) correct images need to be retrieved by the AI; and (b) humans should associate the retrieved images with the current pathology under test. We find that when tested on chest X-ray diagnoses, 2FR leads to increases in clinician accuracy, with particular improvements when clinicians are radiologists and have low confidence in their decision. Our results highlight the importance of understanding how different modes of human-AI decision making may impact clinician accuracy in clinical decision support systems.

AtOMICS: A neural network-based Automated Optomechanical Intelligent Coupling System for testing and characterization of silicon photonics chiplets

Recent advances in silicon photonics promise to revolutionize modern technology by improving performance of everyday devices in multiple fields. However, as the industry moves into a mass fabrication phase, the problem of effective testing of integrated silicon photonics devices remains to be solved. A cost-efficient manner that reduces schedule risk needs to involve automated testing of multiple devices that share common characteristics such as input-output coupling mechanisms, but at the same time needs to be generalizable to multiple types of devices and scenarios. In this paper we present a neural network-based automated system designed for in-plane fiber-chip-fiber testing, characterization, and active alignment of silicon photonic devices that use process-design-kit library edge couplers. The presented approach combines state-of-the-art computer vision techniques with time-series analysis, in order to control a testing setup that can process multiple devices and can be easily tuned to incorporate additional hardware. The system can operate at vacuum or atmospheric pressures and maintains stability for fairly long time periods in excess of a month.