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.
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.
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.
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.
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.
What does Frankenstein have to do with health IT consolidation? Are IT professionals more like Indiana Jones or McGyver? Is there an end in sight to the consolidation trend?
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.
Florent Saint-Clair challenges the healthcare community to let go of lingering fear or doubts and accept that in 2021 AI is here to stay.
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.
AI applications are entering radiology at rapid and increasing rates. AI distinguishes patterns and irregularities in large collections of data, which makes radiology an ideal application. More than other medical disciplines, radiology has a long history of storing studies digitally, with plenty of images to train AI algorithms.
Artificial Intelligence presents a promising future for radiology. In this article, we identify the major challenges of AI adoption in enterprise imaging. Overcoming these obstacles will allow radiology to realize the full potential and reap the benefits of AI.
Learn the latest insights about AI in Emergency Radiology, as discussed on a webinar with Dr. Peter Chang of Avicenna and Florent Saint-Clair of Dicom Systems.
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.
2020 teleradiology trends that require workflow optimization and consolidated resources: practice consolidation, greater collaboration and data throughput.
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.
Florent St. Clair and Dr. Alan Pitt share insights into the economics of AI in Enterprise Imaging, the “AI Bubble,” and a checklist of 5 questions for companies to consider before committing resources to AI endeavors.
How often do physicians make mistakes? Can AI be an objective diagnostician? How many images does it take for an algorithm to learn? Florent Saint-Clair takes on these questions and more in part III of his blog series on Machine Learning and AI.
In Part II of the AI blog series, Florent Saint-Clair reviews a practical use case for AI in medical imaging, shares the early challenges in image labeling, and references Atari.
Dicom Systems Executive Vice President Florent Saint-Clair discusses the early promises, limitations and concerns of AI in enterprise imaging, including the distinction between artificial intelligence and empirical machine learning.
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...