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Pharma and the ongoing battle against AI drift

The article from pharmaphorum discusses the challenges pharmaceutical companies face with AI drift, where AI models become less accurate over time due to evolving data and environments. It highlights how AI, initially trained on static datasets, struggles to adapt to new data, leading to decreased performance in drug discovery, clinical trials, and patient care. The piece explores various strategies to combat AI drift, including continuous learning, where AI systems are updated with new data in real-time; federated learning, which allows models to learn from decentralized data without compromising privacy; and the use of synthetic data to simulate new scenarios. Additionally, it mentions the importance of human oversight to ensure AI decisions remain relevant and ethical. The article emphasizes the need for a proactive approach in updating AI systems to maintain their effectiveness in the dynamic field of pharmaceuticals.

Read the Full pharmaphorum Article at:
[ https://pharmaphorum.com/deep-dive/pharma-and-ongoing-battle-against-ai-drift ]