Abxtract

One of the biggest challenges within healthcare technology is the interface design. Volkswagen's famous "Fun Theory" campaign transformed an ordinary subway staircase in Stockholm into a giant functioning piano. The experiment showed that people naturally choose better behaviors when the experience is better designed. Too much of today's EHR experience creates cognitive friction through fragmented information, constant alerts, and administrative work. Context-aware AI has the opportunity to change that, not by adding more notifications, but by understanding clinical context and surfacing the right information at the right time. Check out our latest blog on why the future of clinical AI isn't just more automation 👉 https://lnkd.in/dab2-ytP Curious to hear what others think. #HealthcareAI #ClinicalAI #HealthTech #DigitalHealth #EHR

Radiologists need the right clinical context at the right time. When patient history, prior imaging, and lab results are scattered across disparate PACS, RIS, and EHR systems, fragmented clinical information becomes a major bottleneck. A dangerous domino effect follows: 🔻 Longer search time 🔻 Cognitive overload 🔻 Missed clinical clues 🔻 Delayed diagnosis & follow-up 🔻 Worse patient outcomes Radiologists shouldn't have to spend valuable minutes playing detective across multiple screens just to find relevant priors or clinical indications. AI should eliminate the hunt for information so radiologists can focus on interpretation and critical thinking. 🩺 By synthesizing disparate patient data directly into the diagnostic workflow, AI tools can lighten cognitive load, reduce diagnostic fatigue, and accelerate critical findings when seconds count. What’s the biggest data bottleneck in your reading room workflow today? Let's discuss in the comments.👇 #Radiology #RadiologyAI #HealthTech #MedicalImaging #DiagnosticImaging #DigitalHealth #PACS #HealthIT #RadiologistBurnout #HealthcareAI

Every revolution in radiology has eliminated a major limitation. In 1895, the advent of the X-ray removed the limitation of seeing inside the human body. In 1971, the first CT scan evolved our vision further by removing the limitation of flat imagery and adding anatomical depth. Now, in 2026, context-aware AI is removing the ultimate limitation: remembering everything about the patient. This shift removes a critical cognitive bottleneck, serving purely as a tool for comprehensive patient context while keeping the roles of image interpretation and clinical judgment firmly with radiologists. At Abxtract, this is our focus. Join us as we shape this next medical revolution. #Radiology #HealthTech #MedicalAI #HealthcareInnovation #FutureOfMedicine

MRI safety begins before the patient reaches the scanner. Many MRI safety delays and cancellations can be prevented by identifying critical information like implanted devices, prior surgeries, and contraindications much earlier in the workflow. This carousel explores why early screening and better clinical context are essential for safer, more efficient MRI operations. Swipe through to learn more. 👉 #MRI #MRISafety #Radiology #MedicalImaging #PatientSafety #HealthcareInnovation #HealthIT

Clinical context matters for accurate interpretations. Dr. Singhal's example outlines a common example of this. Clinicians often record their reasoning for ordering the exam but it remains hidden is some inaccessible location. The rest of the time, a proper clinical context is enough for the rad to infer the reason with a high degree of confidence. This was a luxury with last gen tooling but with AI, about to become a non negotiable standard. #QualityMatters #RadiologyAI

Thank you Jesse Courtier, MD! It's a privilege to help radiologists spend less time searching for information. Identifying an implant is often just the beginning. The chart review, MRI safety verification, and cross-team coordination that follow can create significant workflow interruptions. As device innovation accelerates, making implant information instantly accessible is essential for improving efficiency, supporting patient care, and reducing burnout. At Abxtract, we eliminate these tasks so radiologists focus more time doing what they are trained to do and enjoy: interpreting images and caring for patients.

A powerful reminder from Dr. Akrum Al-Zubaidi, D.O. FCCP that one of healthcare’s biggest challenges is ensuring patients receive appropriate follow-up care. Eon is doing incredible work helping healthcare organizations identify incidental findings and drive the follow-through needed to improve outcomes 👏 👏 👏

A report is only as accurate as the clinical context behind it. Yet critical information is often buried across the EHR, forcing radiologists to spend valuable time searching for answers before they can interpret with confidence. The result is reporting variability, workflow inefficiencies, and missed opportunities for better clinical decision-making. In our latest blog, we explore context-first radiology reporting and how real-time EHR data extraction enables not only improved report quality but also diagnostic accuracy, workflow efficiency, and patient outcomes. Learn why the future of radiology AI isn't just image analysis but delivering the right clinical context at the right time 👉 https://lnkd.in/gpDzbwzU #Radiology #RadiologyAI #HealthcareAI #MedicalImaging #EHR #ClinicalDecisionSupport #HealthIT #DigitalHealth #RadiologyReporting

What stops an AI tool from being used in practice? Almost never accuracy alone. The 2020 ACR Data Science Institute AI survey, published in JACR, found about 30% of US radiologists were using AI in clinical practice, what the authors described as "modest penetrance," despite hundreds of FDA-cleared tools. The number, and what's behind it, in the infographic below.

A 2026 JACR study followed 39,000 radiologists for a decade and found the exact moment they quit. Below it, more work builds engagement. Above it, more work drives people out the door. And that's before factoring in the interruptions. Two numbers. One structural problem the industry can't keep ignoring.