Artificial intelligence (AI) plays a growing role in pharmaceutical development, but several barriers stand in the way of the technology reaching its full potential. A new Fast Company article lays out three key barriers that hold back implementation of AI, including siloed datasets and projects, the security weaknesses of centralized data pools, and the unintended bias that creeps into AI from training data.
According to Paul Hudson, “We first need to tackle how we interact with the innovation ecosystem. Pharmaceutical companies generate large datasets based on their preclinical and clinical trials data. Interacting with innovative startups and their specific AI functions further optimizes data generation and analysis, and capabilities are increasing rapidly. The average funding round size for startups in the sector has increased from $10.7 million in 2015 to $51.7 million in 2020. Traditionally, collaborations are licensing in a specific technology or focused on a siloed project team, but to be effective, startup activities need to be better integrated into in-house teams. Closer integration is essential to truly embedding AI into the discovery process and as a core transformative tool. ”
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(Source: Fast Company, February 2nd, 2022)
