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Call me (call me) on the line. Call me, call me any, anytime. Why interoperability across medical image sharing platforms is long overdue.

Call me (call me) on the line. Call me, call me any, anytime. Why interoperability across medical image sharing platforms is long overdue.

In the song “Call me” by American new wave band Blondie, released in 1980, lead singer Debra Harry repeats the lyrics “call me, on the line, call me, call me any, anytime.” This catchy and simple song spent six weeks at number 1 on Billboard Hot 100. Whenever I find myself humming this tune or hear it played in a commercial, I’m reminded of a Radiological Society of North America (RSNA) session I attended 12 years ago when a significant and astute point about phone companies and cloud based image sharing was made.

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Healthcare AI Marketplaces: Three Radiologists Walk Into a Bar…

Healthcare AI Marketplaces: Three Radiologists Walk Into a Bar…

In many ways, AI marketplaces remind me of the Mos Eisley cantina, the most famous dive bar in the Star Wars universe. You don’t know what or who you will encounter. Ben Kenobi and Luke Skywalker were looking to hitch a ride, so they traveled to the cantina with purpose: to find someone who had a transport, and the willingness to fly them to their destination. They found Han Solo but hiring him came with its own set of challenges and baggage, as well as an opportunity cost. But if you don’t know what you’re looking for when stepping in, or if you’re lacking purpose and just browsing what’s being offered, then any answer may be the right one.

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The Elusive Problem of Bias in AI

The Elusive Problem of Bias in AI

To achieve trustworthy AI-dependent healthcare applications, developers need to recognize and mitigate the inherent bias. Learn more about the elusive bias in machine learning, and a framework for identifying bias when analyzing or conceiving an algorithm.

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AI Fights Back Part 3: Welcome to the Matrix. Safety of the Enterprise vs. Freedom of the Individual

AI Fights Back Part 3: Welcome to the Matrix. Safety of the Enterprise vs. Freedom of the Individual

In part 1 and 2 of the AI Fights Back Series, we examined vulnerabilities that allow nefarious hackers to penetrate healthcare institutions, battleground protocols, and a new weapon (AI) to be used in the fight against hackers. The final installment in this series covers the cybersecurity adoption journey from the vantage point of IT professionals who are expected to deploy AI while also preserving the integrity of the Enterprise.

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AI Fights Back Part 2: Battleground Protocols

AI Fights Back Part 2: Battleground Protocols

In part 1 of the AI fights back series: Halt, Quo Vadis? (Who Goes There?) we examined vulnerabilities that allow nefarious hackers to penetrate healthcare institutions and the treasure troves of sensitive and broad clinical and financial patient data that they store. In part 2, we will explore battleground protocols and a new weapon (AI) to be used in the fight against hackers.

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Ceci n’est pas une pipe (This is not a pipe): OCR (Optical Character Recognition) progress and pitfalls for healthcare

Ceci n’est pas une pipe (This is not a pipe): OCR (Optical Character Recognition) progress and pitfalls for healthcare

Recognizing objects and patterns in medical images or documents can be fraught with misinterpretation and errors. Distinguishing the meaning of words that are represented by pixels can be even more challenging because words must be first identified, then processed, then understood with the right context and knowledge of that word. While OCR (Optical Character Recognition) has come a long way since Ray Kurzweil’s OCR computer program in the 1970s, it will take continuous improvement in Artificial intelligence (AI) and Machine learning (ML) to advance this technology further.

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Don’t feed Gizmo (AI) after midnight!

Don’t feed Gizmo (AI) after midnight!

In the 1984 Joe Dante film Gremlins, a cute and gentle creature can turn into a nightmarish monster if you don’t precisely follow the care instructions. Training a healthcare AI algorithm, although not quite as dramatic, can give its creators cold sweats nonetheless.

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Taming the AI Gold Rush

Taming the AI Gold Rush

Is AI a threat to physicians? Is it possible for AI algorithms to make physicians obsolete? Will the robots take over? In the second part of this series, Florent Saint-Clair kiboshes some of the many fears associated with AI. And highlights the many advancements the merging of AI and healthcare has benefitted the industry.

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Launching AI Solutions Beyond The Cloud

Launching AI Solutions Beyond The Cloud

Artificial Intelligence (AI) in healthcare imaging is at an inflection point. Despite the growing body of evidence that shows the promise of AI in healthcare, radiology is adopting AI with some hesitation. AI algorithms made available strictly as a cloud solution exclude many healthcare organizations that have not adopted the cloud.

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COVID-19 Impact on Radiology

COVID-19 Impact on Radiology

COVID-19 has impacted healthcare around the globe, and radiology is no exception. Read what neuroradiologist Dr. Peter Chang and Executive Vice President Florent Saint-Clair had to say about the near-term and long-term shifts to teleradiology brought about by the COVID-19 pandemic.

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Advantages of Teleradiology

Advantages of Teleradiology

Radiology has long been at the forefront of technological innovation, with the adoption of teleradiology. The advantages of teleradiology include reduced workload on radiologists, faster turn-around time for patients, and cost savings for hospitals.

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Optimize Your Enterprise Imaging Data Flow and Teleradiology with Life Image

Optimize Your Enterprise Imaging Data Flow and Teleradiology with Life Image

In 2019, Dicom Systems collaborated with Life Image and Southern California Permanente Medical Group (SCPMG) to host a webinar on how to optimize enterprise imaging data flow by reducing manual steps and automating, delivering faster results for teleradiology workflows. Read selected highlights from this webinar where SCPMG, a user of both the Life Image and Dicom Systems platforms, shared common clinical use cases on the results they have seen from leveraging complementary systems to automate workflows that previously required substantial manual intervention

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Enterprise Imaging Performance Benchmarking

Enterprise Imaging Performance Benchmarking

In 2018, Dmitriy Tochilnk, President and CTO, Dicom Systems and Trevor Walker, Principal Systems Analyst, Stanford Health Care held a webinar and discussed strategies for evaluating enterprise imaging with real, measurable impact on network performance, clinical workflows, and interoperability. Read selected highlights from this webinar and learn how Systems performance is an effective way to benchmark the value of your enterprise imaging workflows.

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From Radiology to… Archeology?

From Radiology to… Archeology?

When evaluating the IT infrastructure of a healthcare organization, we often see layers of disparate systems and varying degrees of documentation. To avoid unnecessary costs and headaches, we encourage IT departments to plan ahead and implement a sustainable and interoperable IT environment.

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Unsung Heroes of Health IT

Unsung Heroes of Health IT

Unsung Heroes of Health IT As a Health IT company, Dicom Systems lives at the intersection of multiple worlds, each of which exerts a substantial influence on our work. We are often expected to troubleshoot a VMware or network-related issue with as...

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Why AI Needs a Unified Workflow Conductor

Find out how you can use Unifier to successfully deploy AI algorithms to your PACS and EHR.

Optimize Your Teleradiology Workflows

Hear how Vision Radiology improved data ingestion, IT performance, and turn-around time by as much as 10X with Dicom Systems.

Get Prepared For Your PACS Migration or Upgrade

Learn why leading healthcare systems select Unifier to ensure continuity, uptime, and clinical workflows.