This study aims to analyze and evaluate strategies that enable pharmaceutical companies to effectively integrate artificial intelligence (AI) and big data to build and maintain trust among stakeholders.
This study used a systematic literature review to investigate strategies for integrating AI and big data in the pharmaceutical industry, focusing on trust, ethics and regulation (Booth et al., 2016). The authors conducted a comprehensive search in PubMed, EBSCOhost, Scopus, Web of Science and university libraries, targeting peer-reviewed articles from the past decade. Search terms combined “AI,” “pharmaceutical industry,” “big data” and related keywords (Moher et al., 2009). Data analysis involved coding, categorization and content analysis to identify themes and patterns (Braun, and Clarke, 2006). The coding process used deductive and inductive approaches, with inter-coder reliability assessed for consistency (Krippendorff, 2018). Tools such as ChatGPT and PRISMA guidelines ensured a structured and transparent review (Moher et al., 2009). Data triangulation enhanced reliability by combining multiple sources. This rigorous methodology provides a robust foundation.
Ensuring compliance with data protection regulations such as GDPR and addressing ethical concerns around the use of AI are critical to building and maintaining consumer trust. This dissertation presents case studies demonstrating the successful application of AI across industries and highlights the importance of its adoption.
Data privacy, algorithmic bias and lack of transparency in the work of AI can undermine the trust of stakeholders, including consumers, regulators and investors. Pharmaceutical companies, seeking to integrate AI, face a choice: how to create a sustainable system that will ensure both efficiency and trust.
