Hi, my name is Kamel and I work as software development engineer in Amazon, Seattle. Today I'm going to present about building high performance healthcare ML systems with go. So this presentation explores how I am able to leverage GO'S unique capabilities to develop machine learning healthcare solutions with remarkable diagnostic accuracy. Let's talk about why we use Go For Healthcare ml. So first thing is concurrency. So goes lightweight, go routines. Enable efficient parallel processing of patient data stream, allowing systems to handle thousands of concurrent analysis with minimal overhead. Second point was security. So goes, comprehensive security libraries and memory safe design, create robust protection for patient data, ensuring hyper compliance while minimizing. Vulner expose during ML operation. Third point is performance. So healthcare application demand million, millisecond level responsiveness for critical decisions. So goals compound nature and compile nature and optimize garbage collection, deliver exceptional performance with predictable latency. So let's talk about the technical architecture overview. For our system. So on right side, I had presented the, data diagram, like how data was flowing. So high level things was, data ingestion. So high performance go microservices, efficient capture and normalized patient data from click and clinical devices, EHRs and variables. So that is like 30 plus medical data formats. Which we need to read and we need to do the result in near, near zero latency. Second part was ML orchestration, so custom go wrappers around TensorFlow, coordinate distributed model execution, enabling parallel feature extraction and predictive analytic analytics, which give us the fragmented healthcare data sets very easily. And prediction delivery. So real time. Insights are securely transmitted to clinical work stations and mobile devices via web sockets with goals, encryption, libraries, ensuring end-to-end hyper compliance. Now let's look at the diagnostic accuracy achievement. So we were able to achieve 94% cardiovascular disease, accuracy, so our algorithms are able to detect heart diseases. Markers with unprecedented accuracy, enabling earlier interventions, and significantly improving the patient prognosis. Then second one was the cancer detection. So we were able to do pretty good cancer detection and we were able to identify cancer biomarkers in initial stages when treatment is most effective, substantially increasing survival rates. Third one is the diagnosis. We were able to achieve the time reduction, so it was like we were able to achieve around roughly 68% of the time reduction, and we are able to reduce pneumonia, diagnosis, timelines, why 68%, allowing faster clinical decision making and more timely therapeutic interpretation. Let's talk about the real time processing in healthcare IO ot. So what we need is, in real time healthcare iot, we need the continuous monitoring. So our go microservice process, 10,000 plus vital data points per second with 99% reliability. The system maintains consistent sub millisecond response time, even under peak hospital load conditions. Second one is the edge processing. So lightweight, optimized go code runs directly on medical IOT devices, reducing network bandwidth by 78% while enforcing end to end data encryption and maintaining hyper compliance at every processing stage. Third one is the anomaly detection. So advanced ML algorithms identify critical patient condition charge changes. Within, two 30 millisecond, which is like very exceptional and it is an outperforming traditional monitoring. By 12 times, clinical teams receive prioritized alerts when contextual diagnostic recommendation for immediate intervention. Let's look at the privacy per preserving implementations. So these are differential privacy. So advanced statistical noise algorithms protect individual patient records while preserving population level insights. Then second one is the federated learning. So distributed AML training occurs on device aing raw patient data transmission across network. Then we were able to do, maintain the encryption of secure AML computations, execute directly on encrypted healthcare data. Without decryption exposure. Then there's a GO Security library. So cryptographically hardened Go modules provide the foundational security and infrastructure, which our team is able to utilize. Let's talk about the validation framework. So validation framework is based on the four stages. One was the performance testing, second was the accuracy validation. Third was the clinical integration. And fourth is the long-term monitoring. So for performance testing, comprehensive loss testing validates system stability under extreme conditions successfully processing 10,000 plus concurrent request and accuracy validation, so other models undergo rigorous validation against gold standard clinical dataset achieving 97% concordance with expert diagnosis. While continuously improving through feedback loops, then for the clinical integration. So clinical workflow studies with Frontline Healthcare provides, providers ensure seamless adoption with 94% of practitioner reporting enhanced decision making efficiency. And for long-term monitoring, we have the sophisticated telemetric systems, which track 27 critical metrics in production environments, providing a statistical confidence interval of 99.8% for all diagnosis. Passport let looks at the economic impact. So before that, our operating costs were roughly around a hundred. A hundred, which we were able to reduce to 75%. Diagnostic time was also significantly, cut down by 60%. And treatment delays were also, we are able to reduce by, 50%. Let's track about the improved patient outcomes. So 76% decrease in the. chronic heart conditions, with our AML powered predictive care system, enabling patients to proactively manage their condition at home. 92% complaints. So for medical thing, it is very important to have the compliance, so medication adherence served with our AI driven personalized reminder system, representing a dramatic, 39%, improvement from pre-implementation baseline, and we were able to redu increase the compliance to 92%. And after that we did the patient satisfaction scores and we had seen it has reached record high as our ML system delivered faster, more accurate diagnosis and personalized treatment recommendations. Let's look at the implementation challenge. So first thing is the ML model integration. So integrating TensorFlow models with GO service required custom binding solutions. We engineered framework specific wrappers with the optimized memory management to ensure seamless interper interoperability. Then clinical workflow adapt adaptation. So healthcare prediction required minimal disruption to established protocols. We develop intuitive interface that map directly to existing clinical workflows. Then third point is data quality variation. So data quality can vary depending upon which system or how we are processing the data. So clinical data source from disparate systems. Exhibited, significant format inconsistency, so goes robust, goes have a robust passing capabilities, which enable us to implement adaptive normalization, algorithm that standardized inputs while preserving diagnostic significance. Then regularity compliance. stringent healthcare regulations, need, comprehensive audit trails and documentation. So our validation framework implemented, immutable logging of all system divisions, ensuring hyper compliance while supporting streamlined regularity services. Let's talk about the key takeaways from this presentation. So first thing is go excels at, healthcare ml. So go exceptional performance. Seamless concurrency and robust security features help us to utilize, go with the optimal foundation for critical healthcare applications requiring unwavering reliability and instantaneous processing. Second one is the meaningful clinical impact. So our system have, quantifiably enhanced, diagnostic precision by 87%. Isolated treatment delivery by 56% and substantial, improved patient outcome across cardiovascular, oncological, and neurological condition economic benefits. So healthcare institutions implementing our go based ML solution have achieved 28% operational cost reduction while simultaneously elevating care quality metrics, translating to a. 3.2 million. Yeah, that's 3.2 million annual savings for mid-size hospitals. Future opportunities to go rapidly expanding ecosystem position, healthcare organization to leverage increasing sophisticated ML applications. Paving the way for predictive care model, personalized medicine protocols and revolutionary patient engagement system. Oh, we are at the last slide. So thank you for, listening to this presentation. It's really nice to present, this talk here. Feel free to reach out to me if you want to have collaboration or discussion for the related topics. Thank you.