Artificial Intelligence Tools for Early Detection of Product Defects in the Pharmaceutical Industry

Current in-process inspection system used at a local pharmaceutical company is manual, outdated and not equipped to perform 100 ^% non-destructive testing to identify critical production defects prior to product release to market. This has resulted in high customer complaints. To address this issue pharmaceutical industry, need to streamline processes and reduce manual intervention. Operator error have been found to be the main contributors to product defect and quality problem. The pharmaceutical industry has also found itself not immune to challenges caused by humans, manual intervention and changes related to the manufacturing environment. Hence, the necessity to be agile and relevant in a dynamic environment. This emphasis has caused industry to advocate for the adoption of technological advancement tools for continuous improvement in quality and process efficiencies. This study aimed at establishing, the most modern and innovative advance technological tools, preferable Artificial Intelligence tools to identify and reduce production defects in real time, with less risk to product. Artificial Intelligence functionality tools. The research methodology executed for this study is the PRISMA approach. The successful implementation of AI is dependent on the accuracy of the input date. The challenges of AI adoption include initial investment cost, data security and AI regulatory framework.