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Aidoc announces $27 million in VC funding to advance AI in imaging Brings company's total funding to $40 million

New study questions patient understanding on AI in radiology Asserts that greater education and communication is required

Fredrik Palm ContextVision appoints new CEO

ACR engages in collaborations for AI development with launch of AI-LAB platform Allows radiologists to create algorithms of their own

New AI software identifies make and model of cardiac implants in seconds Speed up diagnosis and delivery of treatment for patients with faulty devices

Dicom Systems scores enterprise imaging contract with Radiology Partners Will integrate IT and clinical workflows of more than 850 provider facilities

Apple study suggests wearable technology may be useful in detecting atrial fibrillation May assist in stroke and hospitalization prevention

Nvidia unveils Clara AI platform at GPU Technology Conference Equipped with 13 state-of-the-art classification and segmentation algorithms

BSWH to install Glassbeam's CLEAN blueprint to leverage machine uptime Will include integrated CMMS software by EQ2

Beyond the hype: How practical AI is enhancing radiology Insights from Imad B. Nijim, chief information officer for MEDNAX Radiology Solutions

How AI fits in the healthcare puzzle – four things to know

By Kevin Ruiz and Jimmy Solis

U.S. healthcare operators, regardless of size or specialty, face increasing pressure to reduce costs while maximizing revenue and cash flow in an already complicated system. Outdated controls, disparate IT systems and complex processes for patient interaction, coding, billing and reimbursement make it difficult to identify and implement change. The increased scrutiny on patient outcomes, safety and satisfaction, along with increasing labor, medical supply and drug costs only make the task of running a successful healthcare business more challenging.

At the intersection of information technology, healthcare operations and business performance improvement, artificial intelligence has emerged as a production-level, highly-customizable solution that has driven increased profitability at the largest healthcare providers and payors. Advances in AI tools and technology have made these robust solutions increasingly accessible to early-life cycle healthcare startups, PE and VC-held growth operators and other middle market healthcare organizations.
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What does AI mean for healthcare organizations across the U.S.? How can organizations take practical steps to assess, implement and leverage AI solutions? Here are four things every healthcare organization should know as they consider introducing AI into their IT environment.

1. What is artificial intelligence?
The term artificial intelligence broadly refers to a wide array of capabilities along a spectrum of complexity. This spectrum goes from the familiar, such as expert systems, decision support and predictive analytics tools to advanced automation solutions, such as machine learning, natural language processing and robot process automation (RPA). The truly autonomous, sentient AI, arguably, does not exist – as far as we know. The spectrum of innovation and application is ever-growing and constantly debated.

Most of us are already familiar with some version of “AI”. We are comfortable receiving AI guidance on our shopping decisions, health, entertainment or even romantic partners. To accomplish this feat, systems rely on relatively straightforward predictive analytics: recognizing patterns and making assumptions based on large data sets, consumption activities and behaviors. Getting the “system” to detect patterns and understand the relevant signal within the noise requires a great deal of guidance, rules-based logic, a large data set to train the system and manual exception processing. All of this activity – all of these rules and selected inputs – are developed, built and maintained by humans.
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