71% of FDA-Approved AI May Be a Clinical Liability (Here’s Why) скачать в хорошем качестве

71% of FDA-Approved AI May Be a Clinical Liability (Here’s Why)

Get advisory insights about Healthcare AI — at zero cost: https://junaidkaliamd.substack.com/ When 71% of training data for FDA-approved AI comes from just three states, geographic bias shifts from a technical oversight to a direct clinical liability. To deliver on the promise of value-based care, we must move beyond "clean" academic datasets and integrate the diverse, real-world data that reflects the actual patients we serve. In this episode, Dr. Junaid Kalia and Dr. Harvey Castro sit down with Dr. Martin Willemink, a radiologist and former Stanford faculty member who transitioned into the entrepreneurial world to solve AI’s most significant bottleneck: data diversity. As a clinician-turned-founder of Segmed, Dr. Willemink provides a systematic look at why "garbage in, garbage out" is a systemic risk to patient safety and how building a more representative data pipeline is the only way to achieve true generalizability in healthcare AI. The conversation moves beyond the hype of algorithms to the "messy reality" of medicine. We explore the strategic shift from vision-only models to multimodal vision-language models (VLMs) and the emerging role of synthetic data in augmenting training sets where real-world data is scarce. What You Will Discover: [00:00] Intro: The Data Bottleneck of Healthcare AI [01:32] Dr. Willemink’s Journey [05:56] The Risk of Geographic Bias [07:26] Real-World Evidence vs. Academic Data [09:27] The Evolution of Multimodal AI [11:43] Ensuring AI Reliability across different Equipment Vendors [13:16] Navigating HIPAA compliance and "Expert Determination" [15:31] The Future of Synthetic Data [17:55] Strategic Implementation for Healthcare Systems Ultimately, solving the "data problem" is a prerequisite for clinical safety. By building representative pipelines that account for hardware heterogeneity and geographic diversity, we ensure that AI serves as a reliable extension of the clinician. This infrastructure is the only way to deliver accurate diagnostics and improved outcomes for every patient, ensuring that innovation translates into better care at the bedside rather than just a technical success in a silo. Referenced in the show: 🖇️ "Geographic Distribution of US Cohorts Used to Train Deep Learning Algorithms" – https://pmc.ncbi.nlm.nih.gov/articles... Connect with us: Dr. Junaid Kalia, Neurocritical Care Specialist and Founder of Savelife.AI™ 💼LinkedIn -   / junaidkaliamd   🔗Website - https://www.junaidkalia.com/ 📹YouTube -    / @junaidkaliamd   Dr. Harvey Castro, ER Physician, #DrGPT™ 💼LinkedIn -   / harveycastromd   🔗Website - https://www.harveycastromd.com/ 📷Instagram - https://www.instagram.com/harveycastr... 📑 Substack - https://harveycastromd.substack.com/ Edward Marx, CEO, Advisor 💼LinkedIn -   / edwardmarx   🔗Website - https://www.marxadvisory.com/ 📹YouTube -    / @edwardmarx   Special Guest: Martin Willemink, MD, PhD, FSCCT, Co-founder & Chief Scientific Officer at Segmed 💼LinkedIn -   / martin-willemink   Visit our website: https://signalandsymptoms.com/ #HealthcareAI #MedicalImaging #HealthcareLeadership