Welcome everyone. My name is Ush K and I am here ready to present one of the most exciting and transformative shift happening in modern healthcare, the integration of artificial intelligence into precision medicine. As we all know, healthcare is becoming increasingly complex. We have dealing with growing patient population. Rising cost, clinical burnout, and a massive influx of data from electronic health records to genomics and variable devices. This is where AI steps in. It gives us the tool to make sense of all the data and use it to deliver better, faster, and more personalized care. Today, I will walk you through the key domains. Where AI is making a difference in clinical decision making, administrative efficiency, pharmaceutical safety, diagnostics, research, and of course in enabling vision of precision medicine, I will also highlight some ethical considerations and offer an implementation roadmap for those of you who are bringing AI into their organizations. So let's begin by looking at the promise AI holds across the entire healthcare spectrum. So here we are, the promise of AI in healthcare. Sorry for the technical difficulty. So first of all, AI is not a distant concept. It is actively driving miserable change across three core areas, clinical impact, administrative efficiency, and research advancement. Let's start with a clinical impact. One of the most critical challenges clinicians face in this time is spent on documentation, AI assisted documentation tools. Have helped reduce medication errors by about 30%, which is a huge improvement in patient safety. Even more notably, these tools are saving healthcare providers around six hours a week. That's valuable time that clinicians can now spent with patients rather than trying into electronic health records. Next, administrative efficiency. AI is streaming claim processing. We are seeing turnaround time go from several weeks to just few days. More importantly, AI is helping reducing the denial rates of the claim by 25%, which means fewer claims are rejected, fewer reimbursement cycles, better financial health for provider organizations and return on investment is typically realized within four to six months, making these solutions not only effective. But also financially sustainable. Finally, research advancement in the world of clinical research is speed is critical. Machine learning algorithms are accelerating the discovery of new treatment options up to 200 times compared to conventional research methods. These systems can shift through massive database, detect hidden patterns, and even identify new s. Paving the way for more effective and personalized therapies. Clinical documentation improvement. So very important. You can see the big numbers here, right? 37% error reduction, 4.2 hours time saved from the clinical documentation burden, 93% accuracy rate. Here we are. AI powered documentation systems are now capable of achieving 93% accuracy rate. That means they can organize clinical nodes with very little human corrections. This reduces the burden on the clinicians. These systems can crosscheck medications, highlight discrepancies, and even suggest corrections based on clinical guidelines. Time saving is another major benefit. This doesn't just improve job satisfaction. It directly impacts the quality of patient care by allowing doctors and nurses to focus more on what they do best caring for people. Administrative workflow here, we need to see that. Starting with the clinical submission, the claims, the AI system now pre scan claims before they are submitted, identifying coding errors, missing documentation, and non-compliance issues. This proactive review insurers that claims are accurate and complete, dramatically improving first pass resolution rates. Second automated processing. Machine learning algorithms can now handle routine claims without human intervention. This has led 65% reduction in staff workload to, for claim departments freeing up those resources for more complex tasks. Denial prevention, AI system analyzes historical data and flag claims that are likely to be rejected. They help administrators correct issues before the claims even leave the door. And what's the result of all of this? It is faster processing time by 42% and a 28% reduction in costly denials. That means a smarter cash flow, fewer disputes and better relationship between payers and providers. The next is AI in pharmacy applications. AI's role in pharmacy is equally transformative. Let's look at how it's reshaping. One second, let's look at how it's reshaping medication safety and personalization. So we are first covering the prescription verification. AI Systems now performing initial reviews of prescriptions with 99 by 2% accuracy. That is significantly more reliable than manual clerk Check work alone. Next, interaction detection. An area where AI really shines. These systems can instantly scan a patient's full medical record, including prescriptions, lab results. An allergy history to detect harmful drug interaction. This level of rapid accurate detection simply isn't possible with manual review alone. AI also supports dosing optimization. It takes into account a range of patient specific factors such as age, weight, renal functions, and even genetic markers to suggest personalized dosing. This minimizes side effects and maximizes therapeutic impact. Finally, outcome tracking is where AI become a long-term partner. These tools continuously monitor how patients respond to medications and alert clinicians in real time for any adverse event. This helps ensure early intervention and improves. Overall treatment outcomes. Moving down to personalized medicine, p Precis medicine is all about tailoring care to each individual's unique genetics, environmental, and lifestyle factors. AI is accelerating this vision in several key ways. Let me move it a little bit here so you can look at the presentation. So the first second is bio discovery. Traditional analytics often misses SubT patterns in complex data, but deep learning models can uncover new RS that reveal disease pathways and help identify novel therapeutic targets. Time to treatment and another area of improvement. Clinician decision support system powered by AI are helping reduce the time it takes to get the right treatment in place. 35%. That means patient starts effective therapy sooner, recover faster, and spend less time in the hospital. Lastly, AI enhances outcome predictions. By analyzing a patient's full data profile, labs, imaging history, and even variable data, AI can forecast how they will respond to treatment. This enables clinicians to adjust the care plan early, increasing the chances of success. This is where is a very interesting thing I want to share with all of you. It's diagnostic imaging breakthrough through artificial intelligence. AI system trained on large imaging data sets, like CT scans, MRIs, x-rays are now capable of identifying abnormalities with high level of sensitivity and specificity. This means that they can detect both the presence of disease and accurately rule it out in many cases. Matching or even exceeding the performance of experienced clinician for certain conditions. One key advantage of AI in imaging is its consistency. Unlike human observers, AI does not suffer from fatigue, bias, or variations in judgment. It sees every scan with the same level of detail and attention. And with the right training, it can spot subtile patterns that might be missed during a manual review. AI is also reducing the radiologist workload by 28%. That's not because it's replacing them. It is because it is filtering and prioritizing routine task. For example, it can automatically sort normal scans from those requiring urgent attention. Helping Radiologists focus their time where it matters the most. Another exciting application is early detection. AI is already being used in identifying early signs of diseases like breast cancer, lung nods, and even neurological disorders. Early diagnosis leads to earlier intervention and often have far better outcomes. Ultimately, AI doesn't replace radiologist. It augments them acting as a second set of eyes and helping them work more efficiently and effectively. Workforce adoption very important. Why? Because without this, all AI is adjusted, accommodated, understood, and applied by the workforce. It'll not give its fruit to us. One of the most important and sometimes overlooked component of AI adoption in healthcare is preparing the workforce. We begin with AI literacy. This means ensuring everyone in the organization, from frontline clinicians to IT staff, executives, they all understand what AI is, how it works, and how it applies to their specific roles. Establishing a common vocabulary and baseline knowledge is critical for cross-functional collaboration. Next role, specific training. A one size fits all approach Want to work here? Clinicians, for instance, need to understand how AI can support clinical decision making and documentation. Administrators may focus on claim processing, operational analytics, or scheduling optimization. Technical staff need to understand how to maintain, monitor, and troubleshoot AI systems. Then we move to hands-on implementation. Theoretical knowledge is only the beginning. Simulated environments like AI powered EHR system or virtual diagnostic tools allow staff to get comfortable using AI in low risk settings before applying it to the real world scenarios. And finally. There must be a commitment to continuous development. AI tools and practices are evolving rapidly. A structured programs that includes peer learning, regular updates, workshops, and even certification track help ensure that knowledge stays current and still stays sharp. When organization invest in people alongside technology, the results are a smoother adoption. Higher trust in AI system and ultimately better outcomes for our patients. Next is ethical considerations in AI implementation. With all the benefit AI offers, it is essential that we take a thoughtful and ethical approach to how it's implemented in healthcare. First, we must address fairness and equity algorithms are only as good as the data they are trained on. If the data lakes diversity or if it reflects historical biases, then the AI can unintentionally spread those differences. It's critical to ensure that AI systems are tested and validated across diverse population. So that everyone receives high quality care regardless of race, gender, age, or geography. Second, privacy protection healthcare data is among the most sensitive types of information. Organizations must implement strong enterprise grade security protocols to safeguard patient data. At the same time, we must find ways to enable data sharing for AI training and development in ways that respects privacy. Then there is transparency. Clinician needs to understand how AI system arrives at their recommendations. If an AI suggests a diagnosis or flags a risk, there must be an. Interpretable explanation behind that output. Transparency builds trust, which is essential for adoption and also aligned with the regulatory requirements. And finally, we can't forget about human whole oversight. AI is a tool, not a replacement of clinical judgment. Doctor and nurses should always be in the control of decision. Using AI as an assistant, not an authority. In fact, some of the best performing systems are those that blend AI suggestions with human experience makes the most informed choices. Ethics must be embedded into design, implementation, and monitoring of every AI system we deploy in healthcare. Now, let's talk about financial aspects. Because at the end of the day, AI adoption tools be sustainable. It must deliver value here. Yeah. So one of the most promising sign is that the healthcare institution begins seeing a positive return on investment within nine to 14 months. That's relatively short timeframe, especially for hospitals or healthcare systems that are used for a long ROI. The saving come from multiple areas. For example, documentation cost drops significantly by about 1.2 million annually for a mid-size hospital. This is due to automation and reduced errors that would otherwise require rework or lead to billing issues. Medication related errors are dropped by 42%. These kind of errors can lead to costly adverse events. Legal actions for extended hospitality preventing them is both a patient safety and a financial imperative. Another benefit is a staff productivity. By automating routine task, clinical teams can handle more patient and some number of with the same number of staff. In fact, institutions have 9.3% increase in patient throughput, meaning more patient treated. Shorter wait time. Improved care without hiring additional personnel. The key takeaway here is that AI, when implemented strategically, is not just a cost, it's an investment, and it pays dividend in financial savings, quality of care, and operational capacity. Implementation roadmap for healthcare leaders. So now if you're wondering how to get started with AI in your organization, this roadmap outlines a practical and phased approach is step one is assessment and planning. Look for high value opportunities that align with your strategy is your biggest pain point is documentation or claim processing or diagnostics. Identifying the right starting point is crucial. Next is stakeholder engagements. Build clinical and administrative champions at all level and how you do that, get buy-in early and include end user in the design and rollout process. The more ans they are, the smoother, easier implementation will be. Then comes phased implementation approach. Don't try to deploy all AI everywhere at once. Start with focused pilot in one department or workflow. Use those to refine your system, train your teams, and work out any issues. Once you are proven success on a smaller scale, you can expand with confidence. And finally measure and optimize. Define clear metrics from the start, like error rates, time savings, return on investment metrics. Track them continuously. Use this data to improve the system. Fine. Tune the algorithm and ensure you are delivering the result you expect. Following this roadmap does not just reduce risk. It builds a foundation for sustainable, scalable success with AI in healthcare. I hope this talk has given you a clear sense of both the opportunities and the practical steps involved in leveraging AI to transform healthcare. Thank you.