Measuring how central or typical a data point is underpins robust estimation, ranking, and outlier detection, but classical depth notions become expensive and unstable in high dimensions and are hard to extend beyond Euclidean data. We introduce Fused Unified centrality Score Estimation (FUSE), a neural centrality framework that operates on top of arbitrary representations. FUSE combines a global head, trained from pairwise distance-based comparisons to learn an anchor-free centrality score, with a local head, trained by denoising score matching to approximate a smoothed log-density potential. A single parameter between 0 and 1 interpolates between these calibrated signals, yielding depth-like centrality from different views via one forward pass. Across synthetic distributions, real images, time series, and text data, and standard outlier detection benchmarks, FUSE recovers meaningful classical ordering, reveals multi-scale geometric structures, and attains competitive performance with strong classical baselines while remaining simple and efficient.

Just as it has been difficult for us to predict the course of this pandemic, so too have healthcare organizations been challenged to predict and evolve their operations to optimize patient care -- as well as revenue. In the initial wave, healthcare's technology needs shifted rapidly. Some organizations immediately shifted as clusters emerged, increasing bed capacity, converting non-clinical spaces to intensive care units and expanding telehealth programs. Meanwhile, others prepared for overflows that did not materialize, leading them to lose their predictable revenue streams from "regular" business. Radiology has become even more stretched thin, facing long- and short-term challenges and revealing just how unsustainable our current ways of working are. Healthcare leaders know the answer is to innovate.

In such an environment, managers should use analytics not as a telescope that, if only it were accurate enough, would let them see the future perfectly. Instead, they should use analytics as a flashlight that, as they scan their surroundings, can help them find their way through a murky, fast-changing environment. Agile analytics uses advanced AI to continually assess the currency, relevancy and accuracy of data and analytic models, updating both to reflect changing real-world conditions and how useful given data or models were in the past. In agile analytics, advanced AI also continually assesses for model perishability -- developing and testing new models, improving them over time, and giving decision makers an estimated confidence level for each prediction. Achieving agile analytics begins with a deep understanding of the lifecycle of data -- which datasets should be used to train models and make business-critical decisions in uncertain times.