Hello everyone. Good morning, good afternoon, or good evening, depending on where you're joining from. My name is Rafael and I'm a senior software engineer and also an independent researcher. It is a pleasure to welcome you to today's session where I will be presenting on building high performance Healthcare, a system, and rust, a senior travel safety platform. To begin, I'll first highlight the growing need for a senior travel safety platform. As global life expectancy continues to rise and healthcare standards improve, it is projected that by 2030, the number of travelers aged 60 and above will reach approximately 703 million worldwide. However, these journeys are often accompanied by unique challenges, including chronic health conditions, limited mobility, language barriers, and uneven access to emergency medical services across different regions. Despite the rapid growth of this demographic, the travel industry currently lacks a comprehensive digital infrastructure designed to address the specific healthcare and safety needs of all the travelers. So why Rust? Rust offers a powerful combination of performance, safety, and concurrency, making it an ideal choice for developing AI systems that handle sensitive medical data where speed and security are paramount. Asset score. Rest ownership model ensures memory safety at compile time, effectively eliminating common programming errors such as null pointed de referencing. And data races. This allows developers to manage memory I density operations efficiently without compromising safety. An essential requirement for healthcare AI systems where software reliability can directly impact patient outcomes from a performance perspective. Arrest delivers native machine level execution with zero cost abstractions and fine grain control. Making it highly suitable for real-time data processing applications such as Edge AI on medical devices and high throughput image analysis significantly benefit from risk performance capabilities. RAs concurrency model further strengthens its suitability for healthcare applications by guaranteeing threat safety at compile time. This is critical when managing multiple patient data streams simultaneously, enabling robust and continuous monitoring from wearable devices and other real-time sensors. Additionally, rust is well suited for cross-platform development, supporting deployment across a variety of operating systems and hardware architectures, including Windows, Linux M embedded systems, iOS and Android. With the cross compilation support via rust, step, and target specific tool chains. Rust enables flexible, scalable, and secure development across platforms, making it a strategic choice for building future ready healthcare and infrastructure. Senior travel healthcare platform architecture is built upon a fully integrated end-to-end technology stack, powered by rust at every critical layer. From embedded biosensors operating on edge devices to cloud-based, AI driven predictive analytics. Rust enables high performance, safety, and efficiency throughout the system. The platform is designed to seamlessly process real-time health data from over 2,847 concurrent users. Each with a unique health profile comprising more than 47 health related variables. Despite the system's complexity and scale, it maintains a sub millisecond latency, ensuring that critical data, such as vital signs and health risk alerts are processed and responded tomo almost instantaneously. Furthermore, the infrastructure adheres strictly to GDPR compliance standards. Operating across 34 countries, ensuring the privacy, security, and lawful handling of sensitive medical data in a cross border context. In essence, this platform showcases ecosystem, can power and advance real time, secure and globally compliant healthcare solution purpose built for unique and growing needs of senior travelers. When it comes to performance, high performance data processing is a key advantage of rust. One of the most significant advantage of adopting rust is its exceptional capability for high performance data processing. Rust is engineered for speed and accurate efficiency, making it ideally suited for applications where larger volume of data must be processed quickly, reliably, and with a minimal system overhead. Rust Platform employs a custom serialization pipeline based on rust powerful survey framework, enabling the processing of medical records at speeds approximately 340% faster than comparable implementation in Python. This approach not only delivers substantial performance gains, but also offers fine-grained control over the DC ization process, developers can precisely tailor how data types are utilized. C utilized optimizing for both speed and efficiency. The Tokyo on asynchronous runtime is a cornerstone of RU ecosystem, providing a robust and highly efficient foundation for developing high performance asynchronous applications. From powering scalable web servers to managing iot devices, Tokyo is the most widely adopted. A Synchron time and trust known for its ability to handle demanding workloads with a minimum overhead. By leveraging Tokyo, the platform efficiently manage continuous streams of health data, maintaining responsiveness and reliability, essential for real-time healthcare monitoring and analysis. Zero copy parsing of biosense data interest is a highly efficient and performance oriented technique. This time to minimize memory overhead, this approach is especially well suited for embedded systems. Real-time applications and data pipelines were rapid data posting and reduced memory allocation are critical to system performance. In essence, a zero copy parsing allows structure data to be interpreted directly from a by buffer, such as rust lines without the need to allocate additional memory or copy of the data into new structures. By eliminating these costly memory operations, zero copy parsing reduces latency and resource consumption, thereby improving overall throughput. The technique not only enhances the response units of healthcare applications that rely on continuous biosensor data streams, but also supports the stringent resource constraints often encountered in embedded and computing environments types. A type safe schema Evolution in rust refers to the process of updating the evolving data models over time without breaking backward compatibility, while preserving rust inherent type safety. And compell enforce guarantees. Rest strong static typing combined with the features such as s and powerful serialized framework provides a robust foundation for managing schema evolution in a safe and explicit manner. Third, safe machine learning pipelines Rest book. Platforms employs can errors and TCH first bindings to enable realtime health risk assessment across multiple concurrent user sessions. This architecture ensures threat safety and pre prevents databases providing reliable and efficient AI processing. Rest ownership model is a foundational design principle that enables predictor analytics engines to process over around 150 risk factors simultaneously while eliminating entire classes of bug related to memory and concurrency. Ownership based safety is rest compiled DAM system for managing memory and data access without relying on a garbage collect collector. By leveraging rust ownership model system achieves robust memory safety and threat. Safe concurrency critical for processing large scale sensitive health data with utmost reliability, the integration of foreign and next runtime within rust enables efficient deployment of pre-trained machine learning models with a minimal resource consumption supporting both CPU and GPU acceleration. By leveraging on the next run time, rest applications can execute complex AI models efficiently, delivering fast interface while maintaining a lightweight footprint ideal for production grade healthcare AI system that demand both performance and resource efficiently. Creating a threat, safe health prediction system in rust request. Leveraging the languages sentence, seeing traits, these choice ensure that the model and, associated data can be safely shared or transferred across multiple threats, thereby preventing data races and eliminating unsafe behavior. During the concurrent execution, rust code implements a threat safe health risk prediction component leveraging rust concurrency primitives to ensure safe and efficient multi-threaded access to machine learning mode. Interface the risk predictor struck and its predict method. Demonstrate an rest pattern for safe, concurrent access to mutable shared state, in this case a machine learning model by leveraging arc for shared ownership and TEX for mutual exclusion. The This implementation is crucial in health critical applications where concurrent predictions across multiple user session must be performed reliably and safely. Web assembly allows running sophisticated natural language processing models such as ma machine translation engines directly in the browser or on edge devices. The integration of rust with the web assembly where offers a powerful solution for building. Performance, secure and cross platform medical translation applications that run directly in web browsers or edge environments. RUS compiled web assembly modules enable medical terminology translation across 23 languages, ensuring broad linguistic outreach achieves and impressive 94% translation accuracy while strictly maintaining user privacy by processing all sensitive data locally on decline device. The use of vam by engine significantly reduces translation latency by up to 60% compared to traditional server side processing, enhancing a response units. This approach is especially critical in emergency situations where clear immediate communication is vital to prevent. Patient criticality via empowered translation model empowers seniors to communicate their medical needs effectively, despite language barriers. Without transmitting any sensitive health information to external servers or cloud services, this ensures data privacy and security while improving accessibility. In embedded systems such as biosensors, resource constraints, like limited memory processing, power and lack of operating system services, associate lightweight and efficient software solutions. Rest now is steady environment offers an ideal platform for developing reliable, safe, and performant firmware for biosensor devices. No SAD refers to rust programs that are compelled without the standard library. It is designed for bare metal or embedded environments where features like dynamic memory allocation threats, or oil abstractions are unavailable or unnecessary. Instead, core relies on the core library, which provides fundamental risk functionalities such as hydrators slices and option. Types, but excludes oil dependent features, leveraging noise study rust along with embedded hal hardware abstraction layer with develop iot bio firmware, optimized for minimal resource usage and robust realtime performance, which is which has minimal footprint real time performance, continuous monitoring, power efficiency as features. And real the custom protocol implementation in Rust is designed to ensure data integrity and reliable communication, even in environments with the poor or intermittent connectivity. Tailored to operate efficiently over limited and unreliable network conditions. Minimizing data loss and retransmissions Employees are robust buffering strategy that temporarily stores data locally and forwards it when connectivity is restored, ensuring no critical information is lost, integrates end-to-end cryptographic checks to guarantee data, authenticity, and prevent tampering during transmission. ENSURES medical data is transmitted and processed with a higher priority, facilitating timely intervention. JDPR complaint medical data storage solution is built using the SUBRA framework, a powerful trust backed blockchain development platform to ensure security, transparency and regulatory compliance. Every data access and modification is securely recorded on the blockchain, creating tamr proof logs that guarantee transparency and accountability. Built in mechanisms enforce GDPR and other region regulations automatically ensuring adherence to privacy laws across multiple countries. Enable secure, seamless sharing of medical data across JU restrictions supporting coordinated care while maintaining strict security standards. Patients manage their data permission directly on chain, giving them full control over who can access their sensitive medical information. Rest based smart contracts, automate consent management and enforce data access controls, ensuring regulatory complaints in maintained seamlessly without the need of a need for manual oversight. Medical record processing and rust achieves a three, 340% performance improvement compared to an equivalent Python implementation. This significant gain is attributed to rust, zero cost, abstraction compiled and memory safety, and efficient control over system resources, making it ideal for high throughput healthcare data pipeline. Implementing medical translation, interest compiled web assembly. Asim delivers a 60% reduction in latency compared to traditional server side processing. By executing the translation logic directly on the client side, this approach minimizes network overhead while leveraging rest, performance, and memory safety, enabling real time privacy, preserving communication in critical healthcare contacts. Rest based medical translation engine achieves 94% accuracy across 23 languages for critical medical terminology. This high level of precision ensures that the vital health information is communicated clearly and reliably, even in multilingual or emergency care settings, supporting safer, more inclusive patient outcomes. The risk assessment engine implemented in Rust delivers sub millisecond response time when processing over 40 cent health variables per user profile. This ultra low latency enables real time decision making and rapid alerting, which is essential for time critical healthcare applications such as remote monitoring and emergency triage. Case study was conducted with, the remote with the emergency response in a remote location. And as per the case study, a health alert trigger was invoked. And we are able bio sensor detects and irregular heart rhythm and elevated blood pressure in a 72-year-old traveler located in rural Portugal. And. A powered machine learning model processes over one 50 health risk factors in a real time and defying a potential cardiac event. In with the 92% confidence, the system automatically locates the nearest medical facilities and translates the patient's medical history into Portuguese ensuring seamless communication with the local care provider. Blockchain verified medical records are securely shared with the received receiving hospital, enabling immediate informed treatment upon the patient's arrival without delays or manual data handling. Challenges in adopting SPO healthcare systems despite its strength implementing rust in the healthcare domain. Present several key challenges limited availability of specialized medical libraries and domain specific tools compared to more established languages like Python or Java. Interfacing with the existing healthcare infrastructure requires careful foreign function interface handling, and increases development complexity. Ensuring strict adherence to regulations such as G-D-G-D-P-R, HIPAA and MDR can be a time consum. Especially when adopting newer technologies that lack pre-certified components, rest, advanced compiled time, checks and optimization can lead to longer build times during the early phases of development, rest, ownership, and borrowing model. While powerful introduces deeper learning curve for teams, transition from languages with the garbage collection or looser memory management. To address the key challenges of using rust in a regulated healthcare environment, the following strategies were implemented, built custom domain specific. Develop focused, tested medical grants create a type safe bendings using integrated compliance checks into CICD pipelines. Optimized to build process, implemented target training programs. The next steps and future learnings. Federated learning privacy, preserving ml using encrypted health data across devices with those centralized storage, releasing core medical data processing libraries to foster healthcare innovation. And extended language support expanding medical translation to 40 plus languages with a specialized re regional medical terminology at JPA deployment, moving more intelligent to wearable devices to reduce the connectivity requirements. Healthcare provider integration, developing standardized APIs for hospitals and clinics to interface with our platform. Thank you all for your time and attention today. I hope this session was provided valuable insights into the development of High Performance Secure Healthcare, a PA AI system using rust, wishing you all continued success and innovation in your work. Thank you once again for joining.