Good afternoon everyone. It's a real pleasure to be here to share insights on platform engineering for AI forward healthcare native mobile architecture. For the last decade, healthcare has shifted from being hospital centered to being increasingly mobile and patient center. Mobile health devices are now essential for remote monitoring, telemedicine, and personal health management. Along with the and with the YAY integration, these platforms are becoming intelligent companions that help doctors empower patients and make more, and make care more proactive. My name is Tri, and in this session and in this session, I will, I'll walk through you how platform engineering native, mobile architecture, and embedded AI combined to deliver scalable, secure, transformative healthcare applications. For instance, during COVID-19 triage apps help hospitals manage patients, manage patient flow by flagging urgent use cases remotely before patients even arrive 80 years. The mo, the movement showed how digital platforms can save, can directly save lives when internet for scale and intelligence. Let's go to the agenda. So in this in this agenda we'll be covering about we'll be having a brief introduction. In this introduction we'll cover about the convergence of platform engineering and healthcare technology. The second slide will be covering about native mobile architecture, the foundation of healthcare platform engineering. Embedded AI integration, transforming AI health, healthcare delivery through intelligent platforms, HIPA complaints and security. Why architecture must embed privacy and trust real time health monitoring is one more aspect. Engineering platforms for country. For continuous care. And the other aspect is clinical decision support, enhanced care, quality and outcomes. And the last point is the future directions. Emerging technologies drive healthcare's Next evolution. Along the way we will also highlight areas like patient engagement, scalability, and strategy will benefit. So in the, in this slide, we'll be talking about the convergence of platform engineering and healthcare technology, healthcare systems healthcare systems worldwide face major challenges like aging population, rising chronic diseases, illness, and worst stage capacit. Traditional models of care can't keep up with these demands. This is where platform engineering plays a pre pivotal role. Instead of building isolated apps, we design platforms reusable, scalable, and secure foundation that can serve multiple healthcare needs. This shift means developing reusable components instead of reinventing the wheel. And also using standard standardized interfaces so hospitals, devices, and insurance can connect seamlessly. And the other aspect is building scalable architectures that rely that reliably handle millions of patients. This mindset prioritizes reliability, observability and maintainability, all of each align perfectly with healthcare. Healthcare's needs for consistent, safe, and auditable systems. So a UK hospital demo, a UK hospital network demonstrated this when need built a single shared platform for appointments, telemedicines, and vaccine, and vaccination track tracking. By using components across apps, they cut development time significantly and improved system scalability. So in this slide, we'll be talking about the native mobile architecture, MO foundation for healthcare platform engineering. Why is native architecture so critical in healthcare? Because here, performance and reliability can literally mean the difference between life and day. Native mobile health, native mobile development enables real time responsiveness for for alerts and monitoring. Latencies are unacceptable when the patient's heart rate drops. Enterprise grade security is also one more aspect through enterprise grade security enhances deeper o integration with encryption and secure storage. The other aspect is hardware integration. With glucose monitors, ECGs and blood pressure cuffs, offline capabilities that allow critical features to work in low network and rural areas. This makes native architecture the better for safe and dependable healthcare platforms. For example, an Android based diabetes care app connected directly with the Bluetooth enabled glucose monitors. Patients receive instant alert for abnormal readings. While doctors get accurate and secure data streams, that immediacy would not have been possible with a hybrid app approach. So in this slide, we'll be talking about. Embedded. Yay. Integration, transforming healthcare delivery. Yay, yay. Transform healthcare platforms from reactive tools to be to prior to tools instead of taking the addition instead of taking some actions after something has happened. It's like a proactive thing. We try to stop, we try to predict the things and stop the things before happening. So with frameworks like tensor Floor Light, we can now run machine learning models on mobile and mobile devices themselves, keeping sensitive data private, while enabling faster analysis. Some of the most powerful capabilities include predictive analytics to start to spot early warning signs of healthcare de deterioration, personalized care recommendations, tune to each patient data, clinical condition support. Offering evidence-based suggestions for doctors this shift, this shifts healthcare from waiting for problems to arise to anticipating them and intervening. Early in India, a mobile iCare app used embedded air to detect diabetic pathy from smartphone images. Rural clinics often lacking eye specialists could suddenly screen and refer patients early, preventing available blindness. In this slide we'll be talking about the HIPAA con HIPAA compliance. In this slide, we'll be talking about HIPAA complaints and security. Architecture in EA powered healthcare platforms in healthcare compliance isn't optional. It's foundational. Every platform must guarantee data encryption, both in rest and in transit. Access control with role-based permissions, biometrics, and multi multifactor authentication are they trace that log every data interactions securely. Some YA models here are even trained to operate on encrypt data. Through advanced methods further enhancing privacy, the most effective platform, build these compliance measures into the architecture itself. Ensuring every application develop developed generates them automatically. We saw this with the US tele telemedicine provider that restricted test result access strictly to physicians, not administrative staff. This not only helped them meet HIPAA standards, but also standardize, but also increase patient trust in the platform. The next slide we'll be talking about real time health monitoring engineering plan. Engineering platforms for continuous use, healthcare is no longer confined to hospitals. Patients expect 24 7 monitoring where they are when wherever they are. Platforms must support sensor integration with variables and implantables edge computing to analyze signals locally without waiting for the cloud. Intelligent alerts that adapt to patient baselines reducing false positive. This approach enables continuous. Proactive care and ensure issues are caught early for instance, cardiac patients in Japan used to connect TCG patches that detected abnormal rhythms instantly, doctors could intervene before emergencies escalated, reducing hospitalization, and improving patient outcomes. The next slide talks about the clinical vision support. Yay. Enhanced care quality. CL clinicians, clinic clinicians faced overwhelming amounts of data. AI helps by augmenting the decision making, which supports evidence based. The recommendation aligned with clinical guideline guiding guidelines. Risk str risk stratification to identify high risk patients, medication management to detect drug interactions, diagnostic assistance with analysis of labs, imaging and symptoms. Yeah, it doesn't replace doctors, but it frees them to focus more on, on patients. For instance at Memorial Sloan ing Oncology. Yay. Yay based system. Suggested chemotherapy options based on thousands of clinical guidelines. Doctors still made the addition but the CO, but the AEA showed critical time and reduced error risk. Here in the next slide, we'll be talking about the patient engagement and accessibility. A strong platform must empower patient directly. This means inclusive design with large fonts, wise input, and high contrast moves. AI powered translation for multilingual support, adaptive content tailored to patient literacy levels. When patients can understand and manage their health outcomes improve. So like in Brazil, a multi-lingual chatbot, Delaware medical advice. In Portuguese, Spanish, and local ECTs by breaking language barriers it gave undeserved communities better access to care. So even people who don't understand the language, because of this multilingual cha chat board, they can understand what the gadget is telling them kind of stuff. So in this slide we'll be talking about the scalability and performance engineering from healthcare integration. As adoption growth, healthcare platforms must remain reliable at scale. This requires. MI Microservice architecture for independent scaling cloud, cloud native deployment for elastic capacity, database optimization for fast, massive data handling, robust APIs to integrate diverse healthcare systems. Performance monitoring to issues proactively sca, scalability issues. Platforms can serve populations, not just individual clinics. So for instance, during, cOVID-19 vaccine dollars. Scheduling platforms scaled overnight to manage millions of appointments across entire countries without any downtime. In the next slide, we'll be talking about implementation strategies from concept to clinical deployment building. Building platforms is only part of the challenge. Deploying them safely into clinical environments is equally critical. This means using agile methods, adapted for regulatory cycles, conducting clinical validation with diverse patient populations, running pilot programs to test safely in controlled settings, supporting change management through staff training and gradual. Success depends not just on technology, but also on the adoption. Adoption and trust. A US Children's Hospital piloted its asthma management app with just 50 families. After refining the experience, they safely expanded it to 10,000 patients. That's a great big breakthrough. So in this slide we'll be talking about the future directions of emerging technologies and healthcare platform evolution. Evolution. The future of healthcare platforms is being shaped by emerging technologies like a JA computing to run advanced models locally. Federal learning pertain. Federated learning, retain models collaboratively without sharing raw data, augmented reality to support doctors during surgery or patient care wise. First, attendance for natural hands-free interaction. These advances will make more intelligent, accessible, and personal. For example, surgeons in Toronto use the air headsets to overlay imaging on live anatomy. Reducing surgery times and improved accuracy. Here we talk about the strategic benefits of AI powered healthcare platforms. AI powered platforms create values across the healthcare system like clinical efficiency. By automating this task patient engagement through personalized insights. Care coordination by connecting providers preventive care, using predictive analysis, resource optimization for staff and supply chains, regulatory compliance via automated reporting. The impact extends beyond patients to the entire healthcare system in Germany, and AI scheduling system optimized nurse shifts reducing overtime by 20% while maintaining the quality of healthcare. So this is a conclusion. The strategic Im imperative of this one is to wrap up platform engineering use of the foundation for healthcare, use of the foundation healthcare system requires, and also native mobile development provides respon, responsiveness, security, and device integration. And apart from that embedded AI transforms platforms into intelligent companies. Together these approaches make healthcare safer, smarter, and more scalable. Singapore has already shown this with the national AI powered health apps, which unify patients records, monitoring and services. This demonstrates how platform engineering can be. It can be a true strategic imperative, not just a technical choice. Thank you all for your time and attention. The convergence of platform engineering, native mobile design and AI is not just about apps. It's about team engineer healthcare itself. I hope this session gives gives you a clear perspective on now. The foundation's enable platform that truly make a difference in people's lives. I would like to take your, I would like to thank you all for giving me an opportunity to provide, to present this topic. Thank you. Have a good day.