An interview with Shannon Lantzy, PhD about why diabetes technology should be judged by how many “spoons” it gives back, not only by A1C and time in range. We dig into FDA decision-making, cybersecurity, faster software validation, and where AI can truly help without creating unsafe risk.

• Shannon’s path from math and NASA consulting into FDA-focused decision science
• Why patient preferences can change how benefit risk gets weighed
• Cybersecurity realities for connected body-worn diabetes devices
• Spoon Theory as a practical model for chronic disease burden
• Spoonshot as a push to speed up safe medtech software throughput
• The real bottlenecks: validation, integration, interoperability testing
• Why medtech cannot ship updates like consumer apps
• Four ways to think about AI in medical devices
• Unmet needs: personalization for pregnancy, young children, alarm tolerance, and affordability
• Why explicit tolerances and clear validation define AI’s limits

We talk with Amir Hayeri, founder and CEO of BioConscious Technologies, about using machine learning to predict glucose trends and turn CGM streams into actionable clinical foresight. We dig into accuracy, trust, liability, and why clustering dysglycemia may reveal risk that HbA1c can miss.

• Amir’s origin story and the prevention-first philosophy behind BioConscious
• Predicting glucose 60 minutes ahead using CGM data and how accuracy is measured
• Why an app without a clinical workflow has limited impact
• Population triage for clinics using clustering to surface high-risk patients fast
• Clinician concerns about liability and the fear of replacing doctors
• Building trust through explainable AI and a clear user interface
• Plans for studies and publication areas including gestational diabetes and dysglycemia
• The “glucose atlas” idea and what CGM can show in non-diabetic users
• Limits of mixing CGM with wearables data and why model complexity can hurt performance

In this episode in our Diabetes Technology Starts series, Lucas Schuster, founder and CEO of Syntactiq, unpacks the gap between what diabetes technology can measure and what research teams can realistically use day to day. We talk about collecting richer contextual data, enabling donation for research, and supporting partners who need to analyze their own sensitive health data, from continuous glucose monitoring logs to EHR datasets, omics data, and clinical trial data.

In the third episode of our Diabetes Technology Starts series, we talk with John Sjolund, co-founder of Luna Diabetes, about bringing nighttime automated insulin delivery to people who use pens, focusing on better mornings, fewer alarms, and simpler tech. We cover evidence so far, algorithm design, travel, Type 2 potential, cost, and environmental impact.

In this second episode of our Diabetes Technology Starts series, we speak with Sweet Spot co-founder and physician Amiad Fredman about turning continuous glucose monitoring (CGM) data into proactive diabetes care. He explains how Sweet Spot partners with endocrinology and primary care practices to remotely monitor patients using tools like the Glycemia Risk Index (GRI) to triage risk and guide timely insulin adjustments between visits without requiring new devices or added work from patients. We also discuss the role of AI in analyzing glucose data and streamlining clinical workflows, as well as Sweet Spot’s provider-aligned business model that supports sustainable remote monitoring.

In the first of our Diabetes Technology Starts series, we talk with SNAQ founder Aurelian Briner (aurelian@snaq.io) about using AI meal photos, CGM integrations, and targeted insights to make mealtime decisions easier for people with diabetes.

An interview on oculomics (using retinal imaging and AI to assess systemic disease) with Tien Wong, MD, PhD, Chair Professor and Founding Head of Tsinghua Medicine at Tsinghua University, Beijing, China.

An interview on implantable CGM with Paul V. Goode, PhD, President and CEO of Glucotrack.