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AUTOMA+ 2025 Conference to Highlight AI, Machine Learning, and Digital Twin Applications in Pharmaceutical Technology - GeneOnline News

2025-05-26 04:07:21 英文原文

作者:Mark Chiang

AUTOMA+ 2025 Conference to Highlight AI, Machine Learning, and Digital Twin Applications in Pharmaceutical Technology

The AUTOMA+ 2025 conference is set to showcase advancements in pharmaceutical technology, with a focus on the integration of artificial intelligence (AI), machine learning (ML), and digital twin technologies. The event will bring together industry leaders, researchers, and innovators to discuss how these cutting-edge tools are transforming drug development, manufacturing processes, and patient care. Scheduled for later this year, the conference aims to highlight practical applications of these technologies in addressing challenges within the pharmaceutical sector.

Key topics at AUTOMA+ 2025 will include the use of AI and ML in accelerating drug discovery timelines, optimizing production workflows, and enhancing predictive analytics for personalized medicine. Digital twin technology—a virtual representation of physical systems—will also feature prominently as experts explore its role in simulating manufacturing environments and improving operational efficiency. Attendees will have opportunities to engage with case studies, panel discussions, and presentations from leading organizations that are leveraging these innovations to drive progress in healthcare delivery.

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Source: GO-AI-ne1

Date: May 20, 2025

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摘要

The AUTOMA+ 2025 conference will focus on the integration of AI, machine learning, and digital twin technologies in pharmaceutical technology. Set for later this year, it aims to bring together industry leaders to discuss advancements in drug development, manufacturing processes, and patient care through these cutting-edge tools. Key topics include using AI and ML to accelerate drug discovery and optimize production workflows, as well as exploring the role of digital twins in enhancing operational efficiency and predictive analytics for personalized medicine.