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How Machine Learning Detects Anomalies in Healthcare


The digital revolution has changed the healthcare landscape irrevocably. Patients expect faster, more efficient care that costs less, which is where artificial intelligence (AI) can help. AI and machine learning allow healthcare organizations to evolve and keep up with trends and new methodologies. Data science enables systems to ingest massive quantities of information quickly, to generate insights and predictions that allow healthcare organizations to focus human attention on what's really important: providing quality care. One of the techniques that are essential for data teams, physicians, insurance analysts, etc., in healthcare to understand is anomaly detection.

How insurance can mitigate AI risks


There is a growing consensus that artificial intelligence (AI) will fundamentally transform our economy and society.1 A wide range of commercial applications are being used across many industries. Among these are anomaly detection (e.g., for fraud mitigation), image recognition (e.g., for public safety), speech recognition and natural language generation (e.g., for virtual assistants), recommendation engines (e.g., for robo-advice), and automated decision-making systems (e.g., for workflow applications). While AI's potential benefits are huge, the concerns are substantial as well. Fears exist regarding potential discrimination, safety, privacy, ethics, and accountability for undesired outcomes.

How chatbots can settle an insurance claim in 3 seconds


At Amazon's prototype grocery store, Amazon Go, customers can walk in, pick up what they want and walk out, without ever waiting in a checkout line or pulling out a wallet. Amazon will automatically charge their account and send them a receipt. Amazon Prime customers can place an order from their phone and get same-day delivery. These are the kinds of no-hassle experiences that consumers have come to expect -- and artificial intelligence (AI) is powering many of them. Amazon Go, for instance, uses computer vision, fusion sensors and deep learning to track when items are removed from or put back on shelves.