Hi, all greetings. thanks for joining with me for this session. Artificial Intelligence for the Life Sciences and the Salesforce benefits for the CTMS system. To introduce myself, I am ula a software professional with the 17 years of experience in the IT industry, and my field of expertise is a cloud computing digital transformation document management with a focus on artificial intelligence with generated AI capabilities. Artificial intelligence is revolutionizing in the life sensors industry and generative. A automates the creation of the regulatory documents, reducing the administrative burden, and ensuring the compiling the generative. A also enhances the workflows efficiency by securely. Connecting the Salesforce data with the large language models to create a relevant customized report. Salesforce also emerged as a pivotable force in this transformation, introducing sophisticated solutions that fundamentally reimagined that rail operations across the patient recruitment and the supply chain optimization and the data management. And the, finally, the healthcare head cp, the integration, this presentation. Explores how the integration of the advanced remission learning algorithms, the natural language processing and predictive analysis. The predictive analysis has a fundamentally changed how the trails has been redesigned, executed the monitored, addressing the critical challenges, enhancing the patient engagement, and reducing the operational cost, and maintaining the quality standards. The impact on, the impact of, AA on, CTMS is like, enrollment pattern prediction, error reduction, and subject MA matching the enrollment pattern prediction. The mission learning algorithms have demonstrated a remarkable accuracy in predicting the subject enrollment patents while reducing the site collection timelines by. 40% through the automated data analysis. This technology advancement has proven the, particularly the crucial as the industry grapples with the increasing the trail complexities and raising the operational cost and coming to this error, reduction traction. The AA power, trail management systems has achieved a major reduction in data entry errors under decreased protocol deviation rates. Above 50% across the phases between like first phase to second phase and third phase of the trail. The platform predictive analysis capabilities have revolutionized the site collection process with the machine learning models, analyzing over thousands of variables simultaneously, and the subject matching the Salesforce AI driven. Patient matching algorithms process electronic health records above 90% specificity in identifying the eligible trail candidates. And this sophisticated matching resulted in reduction of the screen failures, rates and above percentage improvement in the patient retentions and the operational excellence and operational excellence through the Salesforce. Yay. In this section, we'll deal about this instant analytic integration and workflow optimization and patient experience. Instance, a instance, a power analytics have demonstrated nearly 50% of reduction in the trail timeline deviations while improving the outcome prediction accuracy while across the phases of, from the phase two, two, phase four trails. And this platform has also predictive, analysis. For the trail outcomes and have achieved high accuracy rate forecasting the steady completion timelines while reducing the operational in. Inefficiencies workflow optimization. Workflow is nothing but like the task and how this automated task is helping the CTMS system and the workflow Automation. workflow optimization features have improved the resource utilization by 50% while reducing the manual task processing time and the real time performance monitoring capabilities continuously analyze over thousands of metrics per site, identifying the potential issues. Patient experience enhancement. The implementation of 24 7, yay. Power, patient support has increased the patient satisfaction. Crosses for above 50% while reducing the average query responses from hours two minutes. And this is a big drastic change from, for the patient experience enhancement as it is changing the response time from the hours two minutes. This intelligence support system successfully manages the subject inquiries automatically and maintaining a participation participant satisfaction rate above the 90%. Apart from this, the Salesforce AA also helps the, the business users in, drafting the subjects and the sites through the instate and the automated AA capabilities in the CTMS system, and as well as in the Salesforce CRM system data management. And. And this data management and the compiles are the very, the crucial steps in the, in any industry, and particularly it's very important in the life sensors industry. And here in this, in this section, we'll talk about the security framework and the threat detection and the natural language generation. This, this a has the capability as we have seen in many ways, like it enhances the system and again, reduces the time and how it is helping the business users in the real time scenarios. But however, still we, we have, we had a thoughts of like how it'll handle the security framework and threat detection and through the natural language generations. Yaya public, data protection systems has demonstrated about 90% of the accuracy rate in identifying the potential. Security vulnerabilities while processing an average of million data points per study. And these studies are very important, and this and the security of the framework needs to be maintained at high vulnerability date. So this AI powered data protection systems will help, will help in demonstrating about 90% of the accuracy rate and, the platform automated it. H-I-P-A-P compiling, monitoring systems, that has assured ac accuracy rate in detecting the trail violations and the threat detection. And this place a, plays a important role in the, data management and the compiling section and the realtime security threat analysis capabilities process. Approximately hundreds of thousands of security events per second. This, AA has a capability and it has a sophisticated system that successfully prevented unauthorized access, incidents while maintaining an average response, time to just a milliseconds for threat detection and mitigation. And this is the greatest achievement, that we can see from the AA power. And a language natural language generation. The system achieves about 95% accuracy rate in this section by in generating the standard clinical trail reports while reducing the documentation time by half compared to the manual process. And implementing of NLG, which is also like a natural language generation technology, which has excelled in reg regulatory submission process. Automating about 70% documentation task while reducing the, preparation time and intelligent subject recruiting and matching. This plays a very important role in the life sciences. especially for the CTMS systems when we are working with the CTMS systems and how this a will help us in subject recruiting and matching. The first one is, comes as like the recruitment transformation, the recruit. The second one is like a EHR integration. The third one is the predict two analysis will go one by one and how it is. Helping the CTMS system and how the Salesforce is directly impact on CTMS system and how it is getting benefited for the, subjects and as well as the business users Recruitment, transformation. The, a driven recruitment strategies have a two, a reduction in the subject identification time, and while maintaining the accuracy, while maintaining the accuracy rate in matching the patients. Patients to appropriate trails. The traditional recruitment, where the, without, without aid driven approaches, the manual approaches and the traditional follow approaches, methods that typically result in just a very minimal of, eligible patients being enrolled. And it was like really challenging for the life census industry domain way to match with this, recruiting process of the transformation like this. E with the help of this yay driven under the Salesforce EAA and this met, this, a preferred approaches increases this rate to 18 to 20%. It is just like the. Or five times more than that of the manual, traditional, recruitment methods and EHR integrations. EHR is nothing but like the electronic health record, things and how this are integrated with the CTMS system and with the Salesforce, the CRM and the document management and how the Salesforce AI is helping this, EHR integration. The natural language processing algorithms have demonstrated about 90% accuracy in extracting the relevant clinical, information from the unstructured medical records. It is always a challenging to, to get the information from the unstructured medical records, with the a, a, with the aa. and this can be p and this can be. A hundred percent possible and we can achieve 90% of accuracy in extracting the relevant, clinical information from this unstructured medical records. The artificial intelligence facilitates seamless data exchange between the different, different EEHR systems. re it means like the breaking down of the data sales, and enabling the. Comprehensive patient insights and the intelligent subjects recruitment and matching plays. It also plays a critical role in the CTMS system and, the Salesforce a is helping the business, at more than a 90% with accuracy rate coming to the, supply chain optimization. This is one of this, important area in the life sensors, industry. And, You see the how the overall impact and how the inventory management and how the distribution optimization, is helping this, yay and helping this, supply chain, inventory. And overall the impact is like the yay, a power supply chain solutions. How achieved a reduction in overall logistic cost and while improving the. inventory accuracy to across the multi multicenter trail sites and this deep learning models. The deep learning models that were implemented, for the supply chain optimizations have demonstrated, demonstrated improvement in the resource utilization, compared to, conventional, management systems. And, next coming to this, inventory management and through machine learning algorithms and how we will see how the inventory management, have transformed the, inve, Have transformed and achieving a high accuracy rate. I can say like more than 80% of the accuracy rate in predicting the site specific supply needs. the system has resulted in a significant reduction in stockout instance while maintaining the optimal inventory. Inventory levels are around, 30% below the traditional. management methods, the distribution optimization reinforcement, reinforcement learning algorithms have reduced, have reduced average delivery times and transportation costs and, across the international rail networks that this system and, and this system, predictive maintenance, scheduling, and has. 99 percentage and temperature compliance rate for the cold chain products while reducing the, temperature, exclusions. And the next section, we will see how any, artificial intelligence enhanced the HCP integration. HS. CP is healthcare, professional integration. And here, we will see the different section like enhanced deficiency, data model architecture. And, clinical decision, support and the data validation. So when it comes to the enhanced efficiency, the, artificial intelligence enhanced HCP integration has demonstrated reduction in data entry time, and, while achieving the data accuracy rate, or nine or 90% across diverse, clinical settings. And it comes to the data model architecture. this custom objects, this custom objects for, Trail management has, resulted in a major reduction in data redundancy while improving the query responses times by double. And coming to the clinical, decision support, the realtime analysis capabilities also have the, improved the patient outcome predictions with the decision support and the processing complex and data with within minimal of seconds, just like a few seconds, we can, the clinical decision support can be taken. The realtime analysis capabilities have improved the patient outcome, predictions with the decision support and data validation. And automated protocols exhibit high accuracy in real-time verification across multiple, data sources. By reducing the manual validation around 70% the development and the development and the CTMS. Simulation here In this section we'll talk about the machine learning, machine learning applications, the protocol optimizations and the resource allocations, the machine learning applications, what are the machine learning applications and how it is going to impact this, a powered trail simulations, how demonstrated reduction in early phase development costs. And while achieving the, while achieving the above 80% accuracy rate in predicting the trail outcomes, the A driven, virtual trails have achieved accuracy in, predicting the subject responses while reducing the required patient enrollment In early studies around 25%, and it can be more. These numbers are just based on the research and the PVOs that perform and how the, a has given the results. And these results may. Vary when working with the real time, and it'll be definitely, and it'll be more accuracy rate than of the assumptions of the rate and the protocol optimization. Machine learning algorithms have improved protocol, design efficiency by above 50%, reducing the amend, amendment compared to traditional design methodologies, the system's patient burden assessment capabilities have analyzed approximately, 300 protocol specific factors around, like near to 300, protocol specific factors to predict, patient dropout risk with accuracy and resource allocation. This predictive methods achieve around 80% of accuracy in forecasting the site specific resource requirement. And this enhanced, precision has resulted, has resulted, in resource wastage. And significant in improvement in the site productivity across multi-center, trails, particularly in, complex, phase three studies. And when you come to this, future, I'll make look this and the future implications. And in this future implications we can or we can see enhance prediction accuracy and, Reduced recruitment timelines. This is one of the most important. and, through AA we can achieve this and we can reduce up to 40% acceleration in patient identification and enrollment process. And the thought is like the protocol optimization. Optimization is like a. Decrease in costly and time consuming protocol amendments. And within the fourth section, we can check about the improved subject, 30 to 35% of enhancement in the participant rotations. Across all the clinical trail phases, we see the current implementation results, it varies between 81% enhancing these things. The integration of Salesforce a into the CTMS systems represent a paradigm shift for the life census information research. which are deliverable like, measurable impacts, across, multiple operational, dis dimensions. This platform also, this platform also demonstrate a high boost, high percentage increase in the boost in the operational efficiency, translating the more resource effective resource utilization and a remarkable, increase in the trail success rates. And throughout the phase two. to phase four levels of, of the CTMS lifecycle, fundamentally, transfer meaning how a pharmaceutical company design and execute, clinical studies. Coming to the conclusion. And here in this section, all the section we have, discussed about how the Gen, generatively helps and how the artificial, helps in supply chain, optimization and CTMS systems. And we also discuss about the data management and the compiling, to give the conclusion, to give the conclusion that the integration of the sa Salesforce in. CTMS systems represent as we are telling the shift in life sciences research and the development. And it has the enhanced, enhanced analytics and efficiency. The AA power tools have improved, operational efficiency by around 40%, the clinical success rate, which will be more than 35%, using this AI power while reducing the, trail timelines by around 30%. Optimize the patient engagement through a algorithms, the patient matching precision. Or we can say like the subject matching precision increased by 80% with the significant reductions in the protocol, deviations, creating more, Patient, centric trail experiences, the future ready compliance, the data entry error, the data entry errors. the data entry definitely, can be reduced by 90%. ensuring the regulatory compliance while the platform continues to evolve with the advanced i a capabilities and, protocol optimization. The real tempation support and this platform as the platform continues to evolve. The future developments in the AI capabilities to further revolutionize the CTMS system, ultimately contributing to more efficient and effective clinical trails in the life census industry. And thank you. Thank you for joining the session with me. I hope you had a great information on this. yay. A for the life sciences and the Salesforce. yay. Tools that, how it helps the, and how it helps and how it benefits for the CTMS systems. And thanks for joining with me. If you have any questions, please reach out to me. I'm happy to help you. Thank you.