The introduction of Artificial Intelligence (AI) into the pharmaceutical industry is a topic that stirs both excitement and apprehension. The excitement stems from the vast new possibilities AI offers, while the fear is rooted in concerns about the unknown, potential errors impacting patient health and business decisions, and the threat of obsolescence for certain professional roles.
From drug discovery to day-to-day operations, the industry can integrate AI through internal development of tailored solutions, partnerships with AI experts, acquisition of specialized business or patents, and licenses for business-specific or general use. Recently, we have observed a surge in concrete initiatives, with industry partners seeking advice on the most relevant approaches, support in co-developing and testing solutions, and assessing AI acceptance within companies and among health authorities.
While significant AI breakthroughs are relatively recent, this is not the first time the industry has faced transformative innovations. Learning from the successful adoption of past disruptive technologies can provide valuable insights. Historical examples, such as the integration of biotechnology and genomics, show how embracing innovation can lead to substantial advancements and improvements in drug development and patient care.
Integrating new AI initiatives requires extensive piloting and onboarding of the stakeholders. Any solution must be developed and tested thoroughly with the experts and users of the field. Encouraging collaborators to welcome the changes brought by AI, and to incorporate them into their work is an important part of the challenge. When considering new AI tools, the primary focus should be on the functions they perform and the problems they solve. Alongside the introduction and training of disruptive tools, other AI solutions can be seamlessly implemented to facilitate everyday work.
A culture of innovation and openness to new ideas is essential for successful AI integration. The pharmaceutical industry, with its core focus on researching and bringing novel molecules to market, is inherently innovative. Based on our practice and the observations of our experts, the early adopters of AI play a crucial role by testing new tools, identifying efficient uses, and sharing best practices with their peers. Clear AI governance, defined expectations, and encouragement to leverage AI whenever possible, along with tool assessments, broad implementation, and targeted training, are essential elements. Collaborating with field experts and end-users during the development phase is crucial to ensure the tools’ relevance and foster their adoption by the users. Addressing common barriers, such as mistrust and perceived inadequacy, can be achieved through a deep understanding of the systems, active engagement in tool development, and seamless integration into existing processes.
Continuous monitoring of AI tools’ usage, accuracy, and impact on processes is essential. Decision-makers should be prepared to make corrective actions in close consultation with field experts and users. This iterative approach ensures that AI tools remain effective and aligned with industry needs.
In an industry where patient safety is paramount, mitigating risk is crucial. Building trust in AI, often perceived as a “black box,” involves demystifying the technology and building trust. Transparency about the data sources used by large language models, their decision-making processes, and the potential development of security and quality certifications by independent bodies can help reassure users and stakeholders.
Empowering the pharmaceutical industry with AI requires a strategic approach rooted in a thorough understanding of processes, team onboarding, and seamless integration. The ultimate goal remains clear: making better decisions to improve healthcare outcomes. By embracing AI thoughtfully and collaboratively, the pharmaceutical industry can harness its potential to drive innovation, enhance efficiency, and ultimately improve patient care.
For more information on AI at Putnam, please visit https://www.putassoc.com/our-expertise/ai-at-putnam/.
Sources:
- Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44-56.
- Davenport, T. H., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future Healthcare Journal, 6(2), 94-98.
