Rafael Hostettler

Rafael Hostettler
Founder & CEO | Devanthro
Co-Founder | Medical Templates

„Exploring Longevity: Aging, Consciousness Uploading, and Robotic Bodies“

What does it take to live indefinitely? The solution involves addressing aging, achieving successful uploading, and developing desirable robotic bodies for sustained existence. While progress is being made in tackling aging, it remains an ongoing challenge without a definitive solution. Uploading consciousness is still a mystery, leaving much to be discovered. Thus, the focus shifts to the creation of appealing robot bodies.

With Devanthro, Rafael Hostettler’s efforts are underway to construct bio-inspired humanoid robotic bodies that mimic the functionality of the human body while maintaining longevity, paving the way for potential consciousness transfer.

Background

Having spent 8 years mastering the development of basic musculoskeletal robotic systems,
Rafael Hostettler’s journey now advances to the creation of Robodies.

Robodies serve as remote robotic humanoid avatars, enabling human operators to teleport
and engage physically wherever the Robody is located. This technology, akin to a fully
immersive and interactive Zoom experience, holds the promise of dignified aging for the
baby boomer generation.

In another venture, the co-founding of Medical Templates AG marks a revolution in image-
guided navigation, addressing the inefficiencies in current medical procedures. The Cube
Navigation System, a patented innovation, seeks to streamline the process by minimizing
friction at every stage, from procurement to utilization.

2024-05-18T14:45:56+02:00

Background
Arjan, a 58-year-old teacher, has no symptoms but worries about his risk of prostate cancer due to his father’s history with the disease. His doctor recommends screening, but Arjan is unsure if he really needs it. Meanwhile, Blerim, a 65-year-old retired mechanic, was diagnosed at a late stage, despite having regular check-ups. Advances in AI and data science offer the potential to change these stories—by identifying high-risk individuals earlier and ensuring timely interventions.

Problem
Despite medical advancements, many men still face late diagnoses due to limited screening accessibility, resource constraints, and gaps in existing risk prediction models. Some models lack accuracy across diverse populations, while others fail to integrate smoothly into clinical workflows, making them less effective in real-world settings. A more inclusive, data-driven solution could help bridge these gaps and improve early detection efforts.

The Challenge
How can AI-driven risk prediction improve prostate cancer detection and prevention? Your task is to develop a robust and inclusive machine-learning model that analyzes patient data—such as genetic profiles, lifestyle habits, and clinical records—to identify individuals at risk. The model should prioritize accuracy, fairness, and usability while ensuring patient privacy. Can your solution seamlessly integrate into clinical settings, helping doctors make better-informed decisions? Design a tool that brings early detection within reach for more people, improving health outcomes one step at a time.

Rafael Hostettler