Multi-messenger astrophysics is a fast-growing, interdisciplinary field that combines data, which vary in volume and speed of data processing, from many different instruments that probe the Universe using different cosmic messengers: electromagnetic waves, cosmic rays, gravitational waves and neutrinos. In this Expert Recommendation, we review the key challenges of real-time observations of gravitational wave sources and their electromagnetic and astroparticle counterparts, and make a number of recommendations to maximize their potential for scientific discovery. These recommendations refer to the design of scalable and computationally efficient machine learning algorithms; the cyber-infrastructure to numerically simulate astrophysical sources, and to process and interpret multi-messenger astrophysics data; the management of gravitational wave detections to trigger real-time alerts for electromagnetic and astroparticle follow-ups; a vision to harness future developments of machine learning and cyber-infrastructure resources to cope with the big-data requirements; and the need to build a community of experts to realize the goals of multi-messenger astrophysics.

Significant investments to upgrade and construct large-scale scientific facilities demand commensurate investments in R&D to design algorithms and computing approaches to enable scientific and engineering breakthroughs in the big data era. Innovative Artificial Intelligence (AI) applications have powered transformational solutions for big data challenges in industry and technology that now drive a multi-billion dollar industry, and which play an ever increasing role shaping human social patterns. As AI continues to evolve into a computing paradigm endowed with statistical and mathematical rigor, it has become apparent that single-GPU solutions for training, validation, and testing are no longer sufficient for computational grand challenges brought about by scientific facilities that produce data at a rate and volume that outstrip the computing capabilities of available cyberinfrastructure platforms. This realization has been driving the confluence of AI and high performance computing (HPC) to reduce time-to-insight, and to enable a systematic study of domain-inspired AI architectures and optimization schemes to enable data-driven discovery. In this article we present a summary of recent developments in this field, and describe specific advances that authors in this article are spearheading to accelerate and streamline the use of HPC platforms to design and apply accelerated AI algorithms in academia and industry.

Study Need and Importance: Nerve-sparing radical prostatectomy represents the current standard of care for localized prostate cancer, prioritizing oncologic outcomes while secondarily seeking to limit injury to the surrounding neurovascular bundle. Current video-based evaluation standards require expert review, are time-consuming to perform, and are subjective to reviewer bias. Encompassing 14.7% of all new cancer diagnoses in the United States in 2023, improving assessment and training of this procedure for prostate cancer management has potential for substantial benefit to patients. Machine learning has recently been employed to objectively assess surgical skills in several surgical tasks, offering promising alternatives to the current standard. What We Found: We combined robotic kinematic data from the da Vinci console, surgical gesture (cut, dissect, clip, retract) data collected from video review, and model-integrated force sensor data from within our validated hydrogel nerve-sparing robot-assisted radical prostatectomy simulation platform. Using supervised classification algorithms, we were able to achieve receiver operating characteristic area under curve scores of 0.96 and maximum accuracy of 86% in predicting completion of a published learning curve of 250 cases for nerve sparing during the procedure. Limitations: This study featured a limited sample size (n[35) and did not include patient postoperative outcome data from participants. Interpretation for Patient Care: We have identified a series of surgical dissection actions and explainable

Introduction:The impact of Medicare reimbursement changes on urology office visit reimbursements has not been fully examined. This study aims to analyze the impact of urology office visit Medicare reimbursements from 2010 to 2021, with a focus on 2021 Medicare payment reforms.Methods:The Centers for Medicare and Medicaid Services Physician/Procedure Summary data from 2010-2021 were utilized to examine office visit CPT (Current Procedural Terminology) new patient visit codes 99201-99205 and established patient visit codes 99211-99215 by urologists. Mean office visit reimbursements (2021 USD), CPT specific reimbursements, and proportion of level of service were compared.Results:The 2021 mean visit reimbursement was $110.95, up from $99.42 in 2020 and $94.44 in 2010 (both P < .001). From 2010 to 2020, all CPT codes, except for 99211, had a decrease in mean reimbursement. From 2020 to 2021, there was an increase in mean reimbursement for CPT codes 99205, 99212-99215 and decreases in 99202, 99204 and 99211 (P < .001). New and established patient urology office visits had significant migration of billing codes from 2010 to 2021 (P < .001). New patient visits were most commonly as 99204, which increased from 47% in 2010 to 65% in 2021 (P < .001). The most commonly billed established patient urology visit was 99213 until 2021 when 99214 became the most common at 46% (P < .001).Conclusions:Urologists have seen increases in mean reimbursements for office visits both before and after the 2021 Medicare payment reform. Contributing factors consist of increased established patient visit reimbursements despite decreased new patient visit reimbursements, and changes in level of CPT code billings.