The application of AI to medical image interpretation tasks has largely been limited to the identification of a handful of individual pathologies. In contrast, the generation of complete narrative radiology reports more closely matches how radiologists communicate diagnostic information in clinical workflows. Recent progress in artificial intelligence (AI) on vision-language tasks has enabled the possibility of generating high-quality radiology reports from medical images. Automated metrics to evaluate the quality of generated reports attempt to capture overlap in the language or clinical entities between a machine-generated report and a radiologist-generated report. In this study, we quantitatively examine the correlation between automated metrics and the scoring of reports by radiologists. We analyze failure modes of the metrics, namely the types of information the metrics do not capture, to understand when to choose particular metrics and how to interpret metric scores. We propose a composite metric, called RadCliQ, that we find is able to rank the quality of reports similarly to radiologists and better than existing metrics. Lastly, we measure the performance of state-of-the-art report generation approaches using the investigated metrics. We expect that our work can guide both the evaluation and the development of report generation systems that can generate reports from medical images approaching the level of radiologists.

Retroviruses are useful for genetics studies to deliver genes that express proteins, peptides, and RNAs. Several steps, including DNA preparation, transfection, packaging, transduction, and assay, are required to execute screens using retroviral constructs. Unlike screens with purified components, whole-cell assays using retroviral constructs need a large number of steps with microplate manipulations. The nature of these steps, especially the involvement of cultured mammalian cells, limits the throughput of such screens. To improve the efficiency of genetic experiments with retroviral expression vectors, an automated system for retroviral screening in microplates was devised and tested. The system, called Somata, provides high throughputs and robust, reproducible performance.

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