Segmed Blogs

Artificial intelligence (AI) in radiology is more than just a buzzword. Countless developers are creating tools to optimize imaging analytics in healthcare and each advancement has unimaginable potential. So how is AI being deployed in radiology right now? And perhaps more importantly, where should AI in radiology be headed? I sat down to talk with one of Segmed’s medical advisors, Matthew Lungren, MD MPH, to get his thoughts on these very questions. Lungren is a trained radiologist and an associate professor at Stanford University. He is also the co-director of  Stanford’s Center for Artificial Intelligence for Medicine and Imaging . The center brings together teams of clinicians, computer scientists, biostatisticians, engineers, and legal experts to develop and support AI methods that advance patient health. As one of Segmed’s medical advisors, Lungren says he helps the team “come up with ways to ethically, responsibly make patient data available so that we can see advancements a...

Here at Segmed, we believe in the power of medical imaging data. Medical imaging is a valuable component of healthcare, providing visual representations of the human body's inner workings. From X-rays and CT scans to MRI and PET scans, imaging allows doctors to see inside patients to aid in diagnosis, treatment planning, and much more. With technological advances, medical imaging continues to become more detailed, fast, and accessible. In recent years, there has been a growing focus on using real-world data (RWD) and real-world evidence (RWE) in healthcare and medical research. Real-world data comes from a variety of sources, including electronic health records, insurance claims data, patient registries, and now, medical imaging data. Here’s why imaging data is a vital piece of the real-world data puzzle:  The Power of Imaging Ground Truth In domains such as radiology, dermatology, ophthalmology, and pathology, the picture is a reference - ground-truth data. Medical imaging provide...

Intro First a quick biology primer.  Our nervous system is responsible for keeping us safe, for coordinating the body’s responses and maintaining overall homeostasis. The sympathetic nervous system reacts to stressful situations by triggering a fight or flight response.  In true life-threatening situations, this is quite literally running from danger. At work, the nervous system reacts in the very same way (increased heart rate and blood pressure, rapid and shallow breathing, release of stress hormones, etc.), and it has evolved as a survival mechanism allowing us to react quickly to threatening situations.  Once the stressful situation passes, and we are safe again, the parasympathetic nervous system takes over to help restore the body to a state of balance and relaxation, slowing the heart rate, decreasing blood pressure, and lowering the level of stress hormones. Prolonged or chronic stress can lead to negative effects on both our physical and mental well-being. Why is...

Zwanger Pesiri Radiology is an innovative chain of 32 outpatient radiology centers that stretch across Long Island and New York City. Unlike many groups of imaging centers, they aren’t affiliated with any hospitals and so don’t benefit from hospital reimbursement rates or referrals. Keeping quality of care high Zwanger Pesiri leadership has long recognized that running a successful outpatient business, especially one without the benefit of referrals, hinges on providing great care to their patients. What that means for them is focusing on patient satisfaction -- progressive technology, great service, and short wait times.  As Zwanger Pesiri expanded and began serving more and more patients, their largest cost became payroll. Like many businesses, they were cognizant that improving quality by hiring more radiologists, technologists, and support staff was counter to their business goal of becoming more efficient. A few years ago, they realized that a potential solution was on the hor...

Right now the public narrative around data sharing is confused and suspicious, with more “data brokers” being railed in the news every day. It is hard enough to tell who can access data from the apps on your mobile phone, nonetheless your medical records. Most patients  do not understand  where their medical data is stored and who has access to it. This becomes further complicated when we consider differences in culture, socioeconomic level, and education between different countries. Many citizens are  pro-sharing their health data , and realize that it can significantly benefit society. Services like  Ciitizen  let patients upload and share their data for free to help themselves, their family members, and others like them. But using data uploaded by patients poses some problems for research -- introducing selection bias, providing very fragmented datasets, and leaving out much of the population who could still benefit from the development of medical artificial ...

Developing an AI algorithm that will make it into the clinical setting is not easy. The whole process is challenging, from gathering and curating medical data, to training, validating, and testing an AI model, and finally to deploying the algorithm in the clinics. The World Health Organization (WHO) recently published a  framework  that helps developers of AI-based software as a medical device (AI-SaMD) to understand the requirements of evidence generation from development to post-market surveillance [1]. It is expected that AI will significantly improve healthcare delivery and outcomes. The clinical application of AI models is, however, still challenging, not only in low- and middle-income countries, but also in high-income countries. One of the biggest hurdles for clinical application of AI algorithms is access to well-curated and diverse data used for training, validation, and testing. Many AI models are developed in an academic setting, based on homogeneous data from a sin...

Here at Segmed, we’re simplifying access to real-world imaging data for AI research and collaboration. Due to this, we regularly process massive quantities of data - both radiology reports and associated DICOMs. To date, we have accumulated 60M+ patient records sourced from 1500+ sites across 5 continents (and this number continues to grow!). Medical data has incredible potential - researchers and developers in medical AI benefit from high-quality, standardized data to train, test and validate their algorithms. However, prior to sharing and collaborating on this data, patient privacy and confidentiality must be maintained. For this, said data must be effectively de-identified. What is data de-identification? Organizations that use patient data for research must prevent the exposure of protected health information (PHI). HIPAA (Health Insurance Portability and Accountability Act) mandates the removal of any information that could potentially lead to the re-identification of a patient....

  In the recently released  FDA guidance on real-world evidence (RWE) from real-world datasets (RWD ) like electronic health records (EHRs) and medical claims data, which further can be extrapolated for other types of data sources (like Real-world imaging data (RWiD), genetic data, etc.). The assessment of RWD plays a pivotal role in regulatory decision-making for drug and biological products. These data sources are crucial for understanding real-world patient outcomes, treatment efficacy, and safety profiles. The guidance provides insights for the prudent selection and usage of the data sources under the following headings: Relevance of the Data Sources The guidance suggests that the data sources should be evaluated after the study requirements and outcomes have been finalized rather than the reverse. The data sources should be relevant and reliable to the study. It should be able to provide the required information such as – exposure/treatment, dosage, period, and other info...