At the American Association of Physicists in Medicine (AAPM) 2019 meeting, new artificial intelligence (AI) software to assist with radiotherapy treatment planning systems was highlighted. The goal of the AI-based systems is to save staff time, while still allowing clinicians to do the final patient review. RaySearch demonstrated a new U.S. Food and Drug Administration (FDA)-cleared machine learning treatment planning system. The RaySearch RayStation machine learning algorithm is being used clinically by University Health Network, Princess Margaret Cancer Center, Toronto, Canada, where it was rolled out over several months in late-2019. Medical physicist Leigh Conroy, Ph.D., was involved in this rollout and helped conduct a study, showing the automated plans and traditionally made plans to radiation oncologists to get valuable feedback.

At the American Association of Physicists in Medicine (AAPM) 2019 meeting, new artificial intelligence (AI) software to assist with radiotherapy treatment planning systems was highlighted. The goal of the AI-based systems is to save staff time, while still allowing clinicians to do the final patient review. RaySearch demonstrated a new U.S. Food and Drug Administration (FDA)-cleared machine learning treatment planning system. The RaySearch RayStation machine learning algorithm is being used clinically by University Health Network, Princess Margaret Cancer Center, Toronto, Canada, where it was rolled out over several months in late-2019. Medical physicist Leigh Conroy, Ph.D., was involved in this rollout and helped conduct a study, showing the automated plans and traditionally made plans to radiation oncologists to get valuable feedback.

Healthcare supply chains face logistics challenges, changes in software, as well as the need to reduce costs to meet demand. Following are a few ways to better segment products and markets, lower costs, and increase partnerships and collaborations across the supply chain. "The good news is that models do exist to strengthen and improve the health-care supply chain. We believe that by learning from the experience of industries such as fast-moving consumer goods (FMCG), the health-care sector could cut production lead times and obsolescence, while manufacturers, distributors, hospitals, and pharmacies could carry significantly smaller inventories," McKinsey. Like any other industry, healthcare supply chain management aims to to improve overall quality and lower costs.

When you've got a hammer, everything looks like a nail, but the world starts to look more interesting if your hammer can change shape. For the builders of a class of robots called modular self-reconfigurable robots (MSRR), shape-shifting is the first step toward endowing robots with an animal-like adaptability to unknown situations. "The question of autonomy becomes more complicated, more interesting," when robots can change themselves to meet changing circumstances, said roboticist Hadas Kress-Gazit of Cornell University. The key to achieving adaptability for robots rests in centralized sensory processing, environmental perception, and decision-making software, Kress-Gazit and colleagues report this week in a new paper in Science Robotics. The authors claim their new work represents the first time a modular robot has autonomously solved problems by reconfiguring in response to a changing environment.

MIT researchers have improved award winning automatic planning software by adding in code that mimics human intuition. The strategies used by high performing human planners were converted into a machine readable form, and then encoded into the automatic planning software. Adding human intuition to the planning software saw an increase in performance between 10 to 15 percent on a challenging set of problems. The research was conducted by scientists at Computer Science and Artificial Intelligence Laboratory (CSAIL), which is known for a number of cutting edge artificial intelligence breakthroughs. The results from the finding will be presented at an upcoming conference of the Association for the Advancement of Artificial Intelligence.

We describe a timeline-based scheduling algorithm developed for mission operations of the EO-1 earth observing satellite. We first describe the range of operational constraints for operations focusing on maneuver and thermal constraints that cannot be modeled in typical planner/schedulers. We then describe a greedy heuristic scheduling algorithm and compare its performance to both the prior scheduling algorithm - documenting an over 50% increase in scenes scheduled with estimated value of millions of dollars US. We also compare to a relaxed optimal scheduler showing that the greedy scheduler produces schedules with scene count within 15% of an upper bound on optimal schedules.