Why Most Radiology AI Fails | Khan Siddiqui, MD | Fixing Healthcare Workflow Bottlenecks

AI can detect disease faster than ever—but most radiology AI tools fail in real-world clinical practice. In this episode of Med Tech Gurus, Dr. Khan Siddiqui, radiologist and CEO of HOPPR, explains why the biggest bottleneck in medical imaging isn’t detection—it’s workflow, reporting, and clinical integration. As imaging volumes rise and radiologist shortages worsen, AI solutions must deliver measurable ROI in time savings and efficiency. Tools that add complexity or increase cognitive load won’t survive beyond pilot programs. HOPPR is building an AI-native platform that accelerates model development, reduces training time from months to minutes, and enables health systems to fine-tune AI locally—integrating it seamlessly into existing radiology workflows. In this episode, you’ll learn: • Why detection isn’t radiology’s biggest challenge • The real reason AI struggles with clinical adoption • How workflow efficiency drives ROI in healthcare • Lessons from raising $31M in Series A funding • How to scale AI from pilot to practice If you're interested in AI in healthcare, radiology workflow optimization, medical imaging innovation, healthcare startups, or digital health strategy, this episode is a must-watch. #AIinHealthcare #Radiology #MedicalImaging #MedTech #HealthcareInnovation #DigitalHealth #HealthTech #StartupStrategy #clinicalai