The health-tech industry is booming, yet most wearables and nutrition apps fail to create lasting clinical impact. The root cause is a massive disconnect: software developers build systems based on assumed biology, while healthcare professionals lack the technical vocabulary to guide better architecture. We are drowning in raw biometric data from smart devices but starving for clinically actionable insights.

In this session, we will break down the critical gap between technology and healthcare domain expertise. We will explore how to stop building shallow trackers and start engineering robust, AI-driven data pipelines that respect actual clinical protocols. I will cover the complexities of cleaning and integrating messy biological data, the architectural decisions required for handling sensitive health metrics, and how to structure models that healthcare professionals can genuinely trust.

Whether you are working with wearables, AI models, or large databases, this talk will outline what it truly takes to engineer systems that bridge the gap between code and the clinic.

Target Audience: Software Engineers, Data Engineers, AI Developers, and Tech Leads building or interested in HealthTech.

Preferred Duration: 45-60 minutes (can be adjusted to fit track requirements).

Speaker Perspective: I bring a unique dual-perspective to this topic. As someone concurrently pursuing degrees in Dietetics and Computer Science, I actively live at the intersection of clinical science and software engineering, allowing me to bridge the gap between the two fields realistically.

Technical Requirements: Standard AV setup for presentation.