Dimitris Kasdaglis
Aspiring Data Engineer | Dietetics & HealthTech
Athens, Greece
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While studying Clinical Dietetics, I discovered my true passion lies in Technology and Software Engineering. I am now transitioning into Health Data Engineering, combining my analytical mindset with my scientific background in healthcare.
My philosophy is simple: Unlike traditional developers, I understand clinical data. Unlike clinicians, I can build code.
I am currently developing OmniPlate, an AI-powered clinical nutrition platform, and working as a Freelance AI Data Evaluator to help train and optimize machine learning models. My ultimate goal is to bridge the gap between healthcare needs and modern data solutions.
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Topics
Building an AI-Powered Clinical Nutrition App: A Dietitian's Journey into Flutter & Python
Traditional dietetics apps rely on tedious manual entry, while most developers lack the clinical background to build better alternatives. In this session, I will share my journey of bridging this gap by building OmniPlate, an AI-powered clinical nutrition platform. I will discuss how I leveraged Python, Flutter, and AI-assisted development to create a macro tracker and an AI food scanner, transforming clinical knowledge into a functional health-tech product.
The Great HealthTech Divide: Engineering AI-Driven Clinical Pipelines
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.
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