AI in Healthcare Overview Understanding the foundational AI technologies Transforming the Medical Device Innovation model AI Enabled Patient-Centric Device Development Incorporating AI into normal engineering processes
Case Study: Key steps required to operationalize Agentic & Generative AI in enhancing Quality Systems Hearing complementary journeys in Quality – driving measurable gains in productivity and compliance Operating model constructs that enable successful operationalized GenAI platform deployment Discussing regulatory challenges with AI and the need for greater standardization
Integrating digital therapeutics with hardware seamlessly Utilizing wearables for personalized, real-time monitoring Improving outcomes through data-informed patient engagement Addressing cybersecurity in IoMT-enabled solutions Designing for inclusivity across global patient demographics
Navigating eco-friendly materials without compromising safety Aligning product lifecycle with environmental regulations Quantifying impact through life cycle assessments Futureproofing products against evolving sustainability standards
‘-How SaMD is transforming diagnostics, monitoring, and personalized patient care delivery -Exploring regulatory frameworks must evolve to match SaMD’s rapid development pace -Real-world evidence and AI integration drive smarter clinical decision tools Interoperability with legacy systems remains critical for hospital-wide adoption -Cybersecurity and patient data protection define trust in SaMD platforms
How to build adaptive supply networks with AI-driven demand and risk forecasting Strengthening supplier collaboration through real-time data sharing and transparency platforms Leveraging digital twins to optimize supply chain design and responsiveness Driving resilience with diversified sourcing strategies and predictive disruption modeling Enabling traceability and compliance through blockchain and smart contract integration
Accelerating innovation cycles with scalable, secure cloud-native development environments Using AI to automate compliance, risk management, and regulatory reporting workflows Improving clinical outcomes through AI-driven data analysis and predictive modeling Enabling real-time device monitoring and updates via connected cloud infrastructure Enhancing interoperability and patient experience through AI-personalized digital health ecosystems