Hello everyone. My name is Vamsi Upadhyay and I work as a senior statistical programmer in the biotechnology field. Today I'll talk about how artificial intelligence and machine learning are revolutionizing the statistical programming by enhancing the efficiency, ensuring regulatory compliance, and unlocking the data driven insights that can accelerate the drug development and improving patient outcomes. AI driven automation has transformed the way clinical trials operate, optimizing workflows and reducing clinical trial duration by 45%. For example, instead of manually inputting and verifying patient data, AI systems now automate this process in real time, minimizing errors and accelerating approvals. Patient recruitment is one of the biggest challenges in any clinical trials like this. Finding the rights of patients who meet that study criteria. AI powered predictive analytics and machine algorithms, matching algorithms have reduced screening failures by 37%. For instance, in a cardiovascular trial, AI is used to analyze electronic health records and genetic data to quickly identify eligible candidates and significantly reducing the dropouts due to misalignment with the protocol. Endowment speed has been increased by 45 percent using AI screening tools, ultimately leading to faster, Time, faster time to study completion, improving trial efficiency, optimizing resource allocation, and enhancing the overall patient outcomes across the therapeutic areas. Machine learning algorithms have significantly improved like the safety signal detection by 40 percent, enabling earlier identification of potential risks. Unlike traditional methods that rely on manual review and Predefined thresholds. Machine learning models analyze vast datasets including patient records, clinical trial data, and real world evidence to uncover any hidden patterns like any specific drug interaction and its associated adverse events. Deep learning models have reached 83 percent accuracy in predicting adverse events before they occur, revolutionizing the patient's safety. By analyzing historical patient data, genetic markers, and real time biometrics, these models can flag high risk cases and suggest preventive measures. For instance, in cardiovascular trial, AI based risk models have been used to identify patients at risk of arrhythmia based on the subtle change in their ECG readings, allowing clinicians and doctors to adjust treatment plans before complications arise. Real time monitoring and predictive modeling empower clinical teams to implement preventive safety measures throughout the clinical trial lifecycle. By continuously analyzing incoming patient data, AI driven systems can recommend protocol adjustments, optimize dosing strategies, and trigger early alerts for potential safety concerns. Machine learning data extraction has significantly reduced manual workload by achieving 85 percent accuracy in processing diverse source documents. This includes both structured and unstructured data from clinical trial reports, patient records, and regulatory submissions, allowing researchers to focus on insights rather than data processing. The implementation of automation has reduced the data processing time by 60%, allowing accelerating analysis and regulatory reporting by replacing the traditional manual data entry. With the AI driven automation, clinical teams can make faster data driven decisions. Ensuring data integrity is crucial in any clinical trials, and the machine learning powered quality control have improved this anomaly detection by 92%. AI continuously monitors data streams to flag inconsistencies and errors before they impact study results, ensuring compliance with the regulatory standards. Monitoring tools provide machine learning, detect protocol deviations in real time, reducing incidents by 30%. Automated compliance checks and predictive analysis ensure that the clinical trials remain aligned with regulatory and protocol requirements, minimizing costly delays and the need of corrective actions. The risk based monitoring algorithms. Optimize site visits by identifying which location requires closer oversight and which can be monitored remotely. This targeted approach has led to 25 percent reduction in site monitoring expense while also maintaining high data quality. and regulatory compliance. Natural language processing and automated validation tools review clinical documentation for completeness and accuracy by screening documents for inconsistencies and missing information. These systems have reduced the document deficiencies by 45 percent ensuring smooth regulatory submissions and fewer compliance issues. AI powered automation ensures the consistent application of CEDIS standards across all the study data. By standardizing the data formats and structure, automation can enhance submission readiness, reducing the time and effort required for regulatory approval. Deep learning models have Achieved 95 percent efficiency in automated data mapping to the regulatory standards. This reduces the need of manual data validation while ensuring the compliance with evolving regulatory requirements. AI driven automation checks and error detection systems have reduced the data validation time by 65%. This efficiency gain allows for faster data verification and submission. Ensuring that the regulatory agencies require, receive accurate and high quality data. Machine learning algorithms have been used to scan the clinical trial data for inconsistencies, automatically flagging potential issues before submission, leading to faster approvals and fewer revision cycles. Advanced natural language processing algorithms enhance the compliance by identifying the potential 94 percent accuracy. These AI driven systems analyze large volumes of regulatory text, flagging inconsistencies and missing information, which significantly reduce manual review efforts. By automatically detecting potential discrepancies before submission, AI powered compliance Tools help streamline the regulatory process, minimize the risk of delays, and ensuring high data accuracy. Smart automation accelerates the regulatory document preparation, reducing the submission timelines by 70 percent, while maintaining high quality standards. By automating the document generation, formatting, and compliance checks, AI ensures faster and more reliable submission. AI driven document automation tools have enabled regulatory teams to compile and format submission packages in a fraction of usual time, reducing the reliance on manual process, and ensuring alignment with regulatory expectations, which are evolving. Advanced AI algorithms now process an unprecedented 10, 000 data points per second, allowing for real time decision making and immediate protocol adjustments. during clinical trials. This capability ensures that the clinical trials remain adaptive, responding to emerging trends in patient's data without any delay. Machine learning models achieve over 95 percent of accuracy in data processing, surpassing the traditional analytical methods. This high level of precision minimizes the human errors, improve the data integrity, and enhance confidence in clinical trials. Automated analytics have reduced the trial analysis time by 82%, allowing the research teams to make faster and data driven decisions. By automating the complex statistical computations and integrating real time monitoring, AI enables quicker insights and more efficient trial execution. Advanced AI systems are transforming clinical trial decision support by enhancing data analysis and generating real time insights. Machine learning algorithms have improved clinical decision accuracy by 35 percent by detecting complex patterns in patient data and trial outcomes, enabling more precision decision making. The implementation of automated decision support workflows have reduced Critical decision making time by 60%, allowing clinical trial teams to respond more quickly to emerging trends and the trial events. AI continuously processes incoming trial data, enabling researchers to make faster, evidence based adjustments. Enhanced risk detection and early warning systems powered by AI has led to 45 percent reduction in serious adverse events. These systems proactively monitor patient health indicators flagging potential risks before they escalate, ensuring improved patient safety. AI and ML have significantly accelerated statistical programming in clinical trials, leading to 45 percent reduction in analysis timelines. Automated data processing and validation have eliminated time consuming manual tasks, allowing researchers to generate insights more effectively. Machine learning safety signal detection has improved by 45%, enabling early identification of potential risks. AI algorithms analyze large datasets, identifying subtle patterns that might indicate emerging safety concerns before they become critical issues. With the integration of AI, data extraction accuracy has increased by 85%. Machine learning algorithms can process and interpret complex clinical data sets with minimal human intervention, reducing errors and improving efficiency. AI powered automation validation checks have improved regulatory compliance by 95%, ensuring that the trial data aligns with global submission standards. Standardization, documentation, and intelligent data mapping reduce errors and minimize regulatory delays. To successfully integrate AI and ML into the clinical trials, organizations need to have a strategic roadmap that includes infrastructure setup, workflow training, and phased deployment approach. Ensuring seamless adoption requires Collaboration across multiple teams, clinical teams, I. T., regulatory bodies to optimize AI powered workflows. Maximizing the effectiveness of AI in clinical research requires adherence to evidence based best practices. This includes maintaining high quality data. continuously validating AI models for accuracy and bias, and ensuring compliance with evolving regulatory requirements. The future of clinical trials is being shaped by advanced AI applications such as federated learning, real world evidence integration, and automated trial design optimization. These innovations aim to enhance data privacy, improve trial design, and create more adaptive and patient centric research models. Thank you for listening to the session. Thank you.