Lead Author
Dr. Tarek Barakat
دكتور طارق بركات
CEO & Founder · PhD Researcher, AI Medical Imaging · Software Development Lead
Dr. Tarek Barakat founded Fractify (Databoost Sdn Bhd) to close the global radiology gap through accessible, infrastructure-light AI diagnostics. His PhD research centres on deep learning architectures for medical image analysis — specifically the multi-modal fusion models powering Fractify's X-Ray, CT, MRI, and dental screening engines. He writes directly from the development trenches: translating clinical AI research into production decisions that hospital teams can actually use.
97.9%
Brain MRI accuracy
18+
X-Ray pathologies
<3s
Analysis time
Expert Review & Advisory Panel
All articles are peer-reviewed by clinical and AI specialists before publication.
Mohd Rizam
محمد رزام
Co-Founder & CTO
Oversees Fractify's engineering architecture — multi-tenant infrastructure, DICOM pipelines, and AI model deployment at scale across hospital networks.
Dr. Ammar Bathich
دكتور عمار بطحيش
Distinguished Advisor — AI & Digital Transformation
Reviews all articles on AI strategy, clinical implementation, and digital transformation. Ensures content reflects real-world deployment constraints facing healthcare AI.
Dr. Safaa Mahmoud Naes
دكتورة صفاء ناعس
Distinguished Advisor — Medical Sciences & Molecular Medicine
Provides clinical oversight on diagnostic accuracy claims, patient safety implications, and medical science accuracy across all published content.
Recent Articles by Dr. Tarek Barakat
clinical
Incidental Findings in Radiology: How AI Catches What Humans Miss
Incidental findings are unexpected secondary pathologies discovered on imaging performed for a different clinical indication. A patient presents with chest pain; the radiologist rules out acute coronary syndrome on the chest x-ray and documents a 12mm right lower lobe nodule. Tha…
clinical
AI Radiology Triage: Automated Worklist Prioritisation Saves Lives
Every radiologist reads from a worklist. But whose case goes first—the stroke alert at 11:47 AM, or the outpatient ankle X-ray that arrived at 8:23 AM? Manual prioritisation takes cognitive effort and delays critical diagnoses. AI radiology triage removes this problem: it learns …
enterprise
PACS Integration with AI Radiology: Complete Implementation Guide
Most pacs integrations with AI fail not because the AI is weak, but because hospitals try to bolt AI onto PACS without redesigning the clinical workflow. They expect radiologists to click a button, wait for results, and keep working. That's not how diagnostic AI works best in pra…
enterprise
AI Radiology Vendor RFP Template: 40 Essential Procurement Questions
When was the last time you read an RFP response that accurately described what an AI system would actually do in your radiology department? Most vendors promise accuracy figures without explaining the study design, patient demographics, or imaging protocol. Meanwhile, procurement…
enterprise
AI Radiology ROI Calculator: Real Numbers From 12 Hospital Deployments
AI radiology ROI (return on investment) measures the financial benefit a hospital receives from deploying AI diagnostic assistance systems, expressed as the time required to recover the total cost of implementation through operational savings—primarily radiologist time, reduced d…
clinical
Emergency Radiology AI Triage: How AI Cuts Door-to-Diagnosis Time in the ER
Emergency radiology AI triage is an automated system that analyzes incoming imaging studies in real-time and flags critical findings—aortic dissections, intracranial hemorrhages, tension pneumothoraces—to prioritize them to the front of the radiologist's worklist. Unlike general …