The first step in the study would be to identify the key quality process parameters for pharmaceutical manufacturing. These parameters could include aspects such as drug formulation, manufacturing processes, packaging, and storage conditions. Once the key parameters have been identified, the next step would be to use semantic and keyword search to identify relevant data sources that contain information related to these parameters.

The data collected through semantic and keyword search would then be analyzed to identify trends and patterns. For example, the study could identify common issues or challenges that arise during the manufacturing process and the steps taken to address these issues. The study could also identify best practices for quality process optimization based on successful case studies or industry standards.

To ensure the accuracy and reliability of the study, it would be essential to validate the results through expert review and comparison with existing literature. Additionally, the study could be enhanced by incorporating machine learning algorithms to identify correlations and predict outcomes based on the data collected.

In conclusion, a quantitative study of AI application in the pharmaceutical industry for quality process optimization using semantic and keyword search has significant potential to improve drug manufacturing processes and ensure patient safety. By analyzing large volumes of data using advanced AI techniques, the study could identify key trends and best practices that can inform decision-making and drive continuous improvement in the industry.