Research, clinical workflows, and the future of AI-assisted diagnostics — from the team building Fractify.
Traditional dental reports describe findings in prose: 'caries noted on tooth 16 mesial surface, periapical radiolucency on tooth 36.' A clinician then manually maps these findings onto a treatment plan. But what if every tooth was analyzed independently by AI, findings were flag…
May 11, 2026 · 12 min read
Read moreA 22-year-old falls on an outstretched wrist. Plain film X-rays are negative. The emergency physician discharges the patient with a wrist sprain diagnosis. Six weeks later, the patient returns with wrist collapse—a scaphoid fracture progressed to avascular necrosis. This scenario…
May 11, 2026 · 15 min read
Read moreCan you confidently distinguish epidural hematoma from subarachnoid hemorrhage in a 2am emergency ct scan when you've already read 40 studies that shift?
May 11, 2026 · 12 min read
Read moreRadiologists review 4+ million chest x-rays annually across the US—and miss 20-30% of clinically significant findings. What if an AI system flagged all 18+ major pathologies before the radiologist's eye landed on the image?
May 11, 2026 · 12 min read
Read moreMost hospitals treat CT DICOM datasets as binary: process everything or nothing. A single chest CT generates 300–512 axial slices. If your AI system analyzes every slice with equal rigor, you've engineered massive latency into your radiology workflow. If you skip slices, you risk…
May 11, 2026 · 10 min read
Read moreDICOM stands for Digital Imaging and Communications in Medicine. It's both a file format and a communication standard that has existed since 1985, created by the American College of Radiology and the National Electrical Manufacturers Association. Every ct scan, X-ray, ultrasound,…
May 11, 2026 · 15 min read
Read moreHow many hospitals have signed three-year contracts with AI vendors, only to discover the system can't handle their PACS workflows or explain why it flagged a critical finding? It happens more often than most hospital executives care to admit. In my experience deploying AI radiol…
May 08, 2026 · 13 min read
Read moreHospital CFOs and IT directors often frame the cloud versus on-premise AI radiology decision as a binary choice. In reality, it's a financial optimization problem with hidden costs that most RFPs never surface.
May 08, 2026 · 13 min read
Read moreMost hospital CIOs tell me their AI implementation is delayed 12–18 months. Why? They're rebuilding what Fractify already validated. If you're deploying AI radiology today, you can compress that timeline to 90 days—with clinical evidence intact. Here's the realistic breakdown.
May 08, 2026 · 12 min read
Read moreHospitals budgeting for AI radiology expect 20-30% productivity gains and faster turnarounds. Six months after go-live, most hospitals realize they're measuring something entirely different. This gap between expectation and reality defines whether Year One becomes a clinical succ…
May 07, 2026 · 14 min read
Read moreAbdominal CT is the single most information-dense scan in routine clinical practice — and also the most time-consuming to interpret. A single CT abdomen/pelvis series can contain 400 to 1,200 axial slices. A radiologist interpreting 40 such studies in one shift reviews close to 2…
May 07, 2026 · 14 min read
Read moreA radiologist who cannot understand why an AI flagged a finding will not act on it — and they are right not to. Explainability is not a supplementary feature in medical imaging AI. It is the clinical threshold between a tool that assists diagnosis and one that accumulates ignored…
May 07, 2026 · 13 min read
Read moreWhile validating our chest x-ray engine, we saw medical residents constantly mistaking skin folds for simple pneumothorax. It was a recurring headache. We had to train the model to ignore these using specific edge-filtering layers because true utility isn't just flagging an abnor…
May 06, 2026 · 7 min read
Read moreComparing scans side-by-side isn't enough. Not even close. In my research at Databoost Sdn Bhd, we found that AI must perform deformable image registration (DIR) to account for changes in patient positioning and internal organ movement between scans. This ensures that a specific …
May 06, 2026 · 8 min read
Read moreradiology ai adoption is accelerating across hospital networks, but most delayed go-lives share a single root cause: the clinical team approved the AI, the vendor delivered the software, and the IT department opened the integration specifications for the first time on deployment …
May 05, 2026 · 16 min read
Read moreHospitals that deploy AI radiology tools without structured procurement evaluation report a 43% higher rate of workflow abandonment within 12 months — almost always for the same reason: the procurement team evaluated the demo, not the integration. A system achieving 97.9% brain M…
May 05, 2026 · 13 min read
Read moreThe primary hurdle in hospital AI radiology procurement evaluation is differentiating between laboratory performance and real-world clinical utility. Procurement teams must demand Area Under the Curve (AUC), sensitivity, and specificity data derived from multi-center clinical tri…
May 03, 2026 · 5 min read
Read moreThe standard workflow in thoracic imaging has long been reactive, with radiologists reviewing studies in the order they appear in the worklist. The AI chest X-ray analysis two-stage pipeline developed by Fractify shifts this paradigm toward a proactive, intelligence-led model. By…
May 03, 2026 · 6 min read
Read moreModern diagnostic radiology produces a massive volume of data, with a single Computed Tomography (CT) or Magnetic Resonance Imaging (MRI) study often exceeding 3,000 individual DICOM slices. This volumetric density creates a bottleneck in the diagnostic pipeline. Radiologists are…
May 03, 2026 · 6 min read
Read moreAI-powered fracture detection is revolutionizing orthopedic pre-operative planning, offering enhanced accuracy and efficiency for orthopedic surgeons.
May 02, 2026 · 4 min read
Read moreLeveraging artificial intelligence for pulmonologist AI chest X-ray CT interpretation is revolutionizing diagnostic workflows, offering enhanced accuracy and efficiency in identifying critical conditions.
May 02, 2026 · 5 min read
Read moreUnlocking significant financial benefits in radiology hinges on accurately calculating AI radiology ROI and optimizing hospital cost per read through radiology automation.
May 02, 2026 · 6 min read
Read moreAI is revolutionizing lung CT nodule detection, dramatically improving early lung cancer screening and patient outcomes.
May 01, 2026 · 4 min read
Read moreAI in radiology significantly enhances diagnostic accuracy by acting as a crucial second reader, preventing critical oversights in medical imaging interpretation.
May 01, 2026 · 4 min read
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