IS THE AI IN PHARMACEUTICAL MARKETING MORE “HUMAN-CENTRIC” THAN IN OTHER INDUSTRIES?

According to Accenture, AI could generate an economic value of more than $150 billion in healthcare by 2026. After discussing and analyzing at length, in the past few months, the applications of AI in medicine in a recent publication of mine with members of the research group of the Observatory on the Clinical Perspectives of Artificial Intelligence at the University of Milan, I thought I would delve into the topic related instead to 'healthcare.

This article was co-written with Manuel Mitola, Partner & Head of AI Consulting at ctcHealth, with a decade of experience as an Innovation Manager in large pharmaceutical companies such as Eli Lilly and Company and MENARINI Group. We explored how advanced technologies are transforming this industry, highlighting both the opportunities and the technical and regulatory challenges.

To begin effectively integrating AI, it is critical to take a rigorous methodological approach early on. Detailed mapping of existing processes that allows for precise identification of where AI can add the most value, avoiding generic solutions and ensuring that innovations are aligned with specific business needs. An in-depth analysis of operational flows enables the identification of key areas for optimization, either through automation or advanced data analytics. According to research by Deloitte, 62% of pharmaceutical companies are already investing in AI technologies to improve operational efficiency and productivity, highlighting how AI has become a key element in maintaining competitiveness in the market.

AI offers powerful tools for predictive analytics, enabling accurate estimates of market trends, demand for specific drugs, and optimal resource allocation. This facilitates supply management. For example, the use of machine learning algorithms can improve demand forecasting by up to 20 percent, according to a McKinsey study.

Generative AI (GenAI) is also emerging as an innovative tool for creating personalized and effective content. Using complex scientific data, AI can generate highly accurate promotional and educational materials, significantly accelerating content creation processes. In the pharmaceutical industry, where accuracy and regulatory compliance are essential, this represents a significant competitive advantage. However, the integration of GenAI presents challenges in the Medical, Legal, and Regulatory (MLR) review process. Although AI can assist in content generation, the legal responsibility for final approval remains human. Current regulations do not allow this responsibility to be delegated to automated systems, making it imperative that qualified professionals be involved to ensure compliance and accuracy. AI also allows vast data sets to be analyzed to create highly personalized experiences for healthcare professionals.

Through AI enhanced hyper-personalization, companies can tailor content and communications to the specific needs of each physician, increasing engagement and improving the effectiveness of marketing strategies. Despite the opportunities presented, the implementation of AI in pharmaceutical marketing involves complexities related not only to regulatory compliance and ethical implications, but also to interpretive complexities introduced by new regulations such as the AI Act. Companies must ensure that data use complies with privacy regulations, such as GDPR, and that AI models do not introduce bias or discrimination.

It is essential to address ethical dilemmas related to the promotion of drugs for complex diseases, ensuring equitable distribution of resources and access to treatments for all patients. The legal liability associated with automated decisions is another obstacle; final approval must, for now, still be done by humans, especially in the MLR process, where compliance with regulations and ethical standards requires extensive human evaluation. I conclude with a quote from Manuel Mitola, MBA during our meeting,

“The future of pharmaceutical marketing will be increasingly driven by data and artificial intelligence. But to be successful, we must ensure that technological innovation is always accompanied by a deep understanding of ethical and regulatory implications, putting health, physicians and patient welfare at the center.”

Edited by Edoardo Ares Tettamanti

Translated from Italian with DeepL