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[ Fri, Dec 06th 2024 ]: SciTech Daily
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[ Fri, Dec 06th 2024 ]: MyBroadband
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[ Fri, Dec 06th 2024 ]: justthenews
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[ Fri, Dec 06th 2024 ]: MSN
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[ Fri, Dec 06th 2024 ]: MSN
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[ Thu, Dec 05th 2024 ]: Firstpost
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[ Thu, Dec 05th 2024 ]: University of California
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[ Thu, Dec 05th 2024 ]: The New York Times
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[ Thu, Dec 05th 2024 ]: pharmaphorum
Pharma and the ongoing battle against AI drift
[ Thu, Dec 05th 2024 ]: Arab Times
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Pharma and the ongoing battle against AI drift

As The Economist reports, pharma "has used computational models for decades, but AI is changing drug discovery in several ways," permitting analysis of huge quantities of disparate data and identifying promising molecules,
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 ]