Hello. So today I will go with AI healthcare in transforming patient care through predictive analytics. The global healthcare AI market valuation has reached to 15. 1 billion. This presentation explored the integration of artificial intelligence in healthcare systems, focusing on implementing the strategies and outcomes, specifically across diagnostic enhancements and treatment optimization. We will delve into the technical architecture, data management, and predictive health capabilities of AI in healthcare, while also addressing critical implementation challenges and ethical considerations. AI driven diagnosis enhancements have, we can categorize into two parts, increased accuracy and reduced cost. Advanced machine learning algorithms have demonstrated exceptional diagnostic precision with AI power system achieving sensitivity rates between 87 to 98. 3 percent across the diverse medical specialties, significantly outperforming the traditional diagnostic methodologies. The other category is reduced cost. Natural language processing and AI technologies have proven transformative in healthcare administration, enabling hospitals to streamline operational Process and reduce administrative expenditures by up to 3. 8 million annually, while simultaneously enhancing overall healthcare delivery and efficiency. The predictive analytics have the main another goal is identifying the high risk patients in advance. we can categorize these into two parts. The first one is predictive modeling. In the predictive modeling, advanced predictive analytics model leverages machine learning to precisely identify the high risk patients with an impressive of almost close to 90 percent of accuracy, enabling healthcare providers to implement the targeted and preemptive care strategies. Next slide, please. The other category falls under this year is reduced readmissions by intelligently analyzing the patient data. These innovations models have successfully reduced hospitalized readmission rates by 28%. Significantly improve the patient outcomes and streamline the healthcare resource allocation. The next category is we're talking about processing medical data. So we can While processing the data we can how we are how the data volume and how the data is accurate Once while coming to the data volume the contemporary healthcare infrastructure process and Intra 200 structured and unstructured medical data points per patient, generating a massive of 1. 5 petabytes of comprehensive medical information annually. Sustaining a rigorous 99. 2 percent data accuracy rate, mission critical of ensuring the reliability of AI algorithms and safeguarding the patient's safety in an aesthetic, sophisticated, multi layered data management system. The another important thing is for the while generating these models that AI algorithms are processing the medical images predictions is the one of the very important category. This is the revolution category. One of the revolution categories, image processing, conventional neural networks analyzes 400, 000 medical images, leveraging advanced deep learning techniques to detect the Subtle pathological abnormalities with 95. 8 percent diagnostic accuracy. Dramatically accelerating the clinical assessment and early disease identification. Outcome prediction. Sophisticated gradient boosting. assembles models, integrate complex patient data to predict clinical outcomes up to 93. 4 percent precision, enabling clinical clinics to develop the personalized treatment strategies and practically manage their potential health risks. Implementation of challenges. So for this category, I would like to say around Two categories I can say one is the data breach and AI protocol vulnerabilities. So coming to the data breach, healthcare data breaches have escalated dramatically with a struggling 37. 2 percent annual increase, exposing critical patient information and undermining trust in digital health. At the same time, AI protocol vulnerabilities are Alarmingly, 74 percent of healthcare organizations face significant of AI protocol weakness, demanding urgent implementation of comprehensive security framework and strengthening the ethical safeguard. Ethical implementation of accuracy, disparity, and transparency is one of the, categories in the ethical implications. The first one is, we'll go with the accuracy disparities. AI diagnostic algorithm demonstrates significant demographic Variability with accuracy rates flanging up to 34 percent across the different population segments, revealing critical system biases that could compromise equitability healthcare delivery. Transparency and trust. A mere 38 percent of healthcare providers report feeling adequately prepared to articulate the reasoning behind aid driven medical decisions. Understanding the urgent need for an interpretable and accountable artificial intelligence system in clinical settings. So we can divide the whole, future of healthcare into two parts. So we can go with precision medicine and predictive analytics and patienting. These three topics we have already discussed in the previous slide. So we just categorized in a pyramid of triangle. While we go with the precision of medicine, so trial ring treatment to individual genetic profile and clinical data or targeted therapeutic interventions. Predictive analytics, leveraging machine learning to forecast disease progression, potential health risk, and unprecedented accuracy. The last one is the patient engagement. Empowering the patients through personalized health insights, real time monitoring, and proactive wellness strategy. The future of AI healthcare transcends traditional medical paradigms Promising hyper personalized care plans, predictive early disease detection, and dynamically optimized treatment outcomes as AI technologies continuously evolve. Key takeaways. I would like to consider that these, I consider into three parts like Potential A, Potential Implementation Challenge and Collaborative Approaches. While we go with the A, Potential A represents a transformative force in healthcare capable of dramatically enhancing diagnostic patient, personalizing treatment protocols and significantly improving the patient care outcomes through advanced predictive analysis. Successfully integrated AI requires comprehensive strategies, addressing complex data security risks, resolving ethical dilemmas, and ensuring algorithm transparency to maintain patient trust and healthcare integrity. The final one is like collaborative approach. Full potential demand strategy collaboration among the healthcare professionals, technological innovators, and regulatory experts to develop responsible, adaptive, and patient centric ecosystems. Next steps of embracing AI and better future. Develop strategic AI infrastructure by allocating dedicated resource and recruiting specialized talent to design, implement, and continuously improve the AI driven healthcare solution. Create comprehensive software security and ethical framework that ensure the patient data protection, algorithms, algorithmic transparency, and adherence to the highest standards of medical AI governance. Foster AI driven healthcare. Interdisciplinary collaborations through structured knowledge sharing platforms, connecting to healthcare providers, AI researchers and technologists, and policy makers accelerate innovative and responsible AI integration. The last one is launch comprehensive patient education initiatives that demystify AI technologies, demonstrate tangible benefits, and build trust through transparent communication and interactive engagement strategies. Embracing AI transformative healthcare future, all transcend mere two technologies innovation its revolutionary approach to the patient care. By strategically deploying predictive analytics and intelligent treatment protocols, we are reimagining healthcare as a proactive personalized and ecosystem that anticipate in this individual health trajectories. Mitigates diagnostic. Uncertainty, uncertainties and deliver precision medicine tailored and unique genetic landscapes and comprehensive health narratives. Our mission is harness as an empowering collaborative tool that amplifies healthcare professional capabilities. Dramatically improves the patient outcomes and construct a more equitable, intelligent and compensate the medical future. This is all about the today demonstration. Thank you very much.