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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.

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.

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.

Cloud computing is shifting the way healthcare providers deliver quality, affordable services to patients. Learn how the healthcare cloud has expanded from functioning as simple data storage to enabling cutting-edge research and artificial intelligence (AI)-based innovations in healthcare.

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.

In a world where hackers relentlessly prey on healthcare providers, and cryptocurrency becomes synonymous with ransom, AI rises to the challenge to help ransomware victims fight back.

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.

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.

While Speech Recognition was a definite and necessary building block that made Natural Language Processing (NLP) ultimately possible, equating it with NLP is like comparing a 1921 Model T Ford with a 2021 model, self-driving Tesla.

In spring 2019, Dicom Systems collaborated with WinguMD to host a webinar about how the healthcare industry is working to make imaging workflows more accepting of mobile devices.

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.

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

Enterprise Imaging IT solutions for workflows, interoperability, archiving, and AI for hospitals, teleradiology and healthcare organizations. Read the highlights of our interview with neuroradiologist Dr. Alan Pitt, including the need for AI in healthcare.

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.

Dicom Systems’ approach to DICOM tag morphing improves your existing imaging and teleradiology workflows to help boost clinical and operational efficiency.

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.

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...