MassAITC Webinar | From AI Validation to Evidence: Proving Readiness for Real-World Aging Care Systems
Content
Abstract:
- Why AI systems that perform well in pilot studies underperform in aging care environments
- How to evaluate safety, fairness, and reliability across older adult populations
- Translating AI validation into evidence for real-world deployment and clinical decision-making
Artificial intelligence is increasingly being deployed to support aging populations through applications such as remote monitoring, fall detection, cognitive assessment, and caregiver support. While many AI systems demonstrate strong performance in pilot studies, only a small fraction successfully translate into safe, reliable, and scalable real-world deployments.
A key challenge lies in the gap between validation and real-world evidence. Traditional evaluation approaches emphasize model performance, but aging care environments introduce additional complexities—including multimorbidity, heterogeneous living conditions, and safety-critical decision contexts—that are often not adequately captured during pilot validation.
This webinar presents a structured approach to moving from AI validation to evidence-driven deployment readiness for aging care systems. The session will explore lifecycle-based evaluation methods spanning data, model development, pilot testing, real-world deployment, and continuous monitoring. It will highlight approaches for assessing fairness across aging populations, ensuring safety in real-world environments, and generating structured evidence to support deployment decisions.
The talk concludes with a practical roadmap for building AI systems that are not only effective in controlled settings, but demonstrably safe, reliable, and trustworthy in real-world aging care environments.
Biographies:
- Dilip Mohapatra
- Dilip Mohapatra is the Founder and CEO of CognitiveView, an AI governance platform that enables healthcare organizations and startups to move from AI pilots to safe, compliant, and scalable real-world deployment. His work focuses on translating model evaluation metrics into audit-ready evidence to support regulatory readiness, clinical validation, and continuous monitoring. He is advancing practical AI readiness frameworks that address the gap between research validation and clinical deployment—where most AI pilots fail to reach real-world adoption. His approach aligns with global standards such as NIST AI RMF, ISO/IEC 42001, and healthcare initiatives like the Coalition for Health AI. Dilip works with healthcare and AgeTech teams to operationalize safety, fairness, and governance in AI systems—enabling faster regulatory approval, reduced procurement friction, and trustworthy AI by design..
- Shawnnah Monterrey, MBA
- Shawnnah Monterrey is the CEO and founder of BeanStock Ventures, with 20+ years’ experience in medical and life science software product development. BeanStock Ventures is an FDA accredited third party 510(k) review organization. Prior to founding BeanStock Ventures, she obtained a bachelor’s degree in computer science from the University of California, San Diego and an Executive MBA from San Diego State University, then went on to hold product development management positions across numerous global firms, including Illumina, Invetech, Medtronic and Carl Zeiss Meditec. During her time at Carl Zeiss Meditec, Shawnnah and her team successfully launched and obtained several 510(k) clearances for an AI based glaucoma diagnostics and monitoring application, using a neural network and support vector machine, in the early 2000s.