OVERVIEW

3rd Annual Pharma AI Summit is organized by Global Business Solutions Group (GlobalBSG) and will be held from Apr 07 - 08, 2022 at Zurich, Zurich, Switzerland.

Who should attend:
Medical doctors, researchers, scientists, CXOs, VPs, seniors, managers, directors, department heads, engineers in the pharmaceutical industry specializing in:
• Artificial Intelligence
• Big data solutions
• Research and development
• Data sciences and analytics Machine learning
• Drug research
• Drug discovery and development
• Predictive analytics
• Pre-clinical and clinical trials
• Medical imaging
• Biomedicals
• Bio-informatics and technology
• Clinical innovation Scientific computing
• Drug design Computational screening & cognitive computing
• Real-world data and evidence
• Information systems
• Digital health
• Pharmaceutical companies
• Healthcare companies
• Biomedical companies
• Biotech companies
• AI and machine learning companies
• Data analytics companies

Conference Overview
The 3rd Annual Pharma AI Summit will be taking place with more interactive sessions and leading companies in the pharmaceutical industry. Business leaders will talk about one of the most significant topics for the sector, Artificial Intelligence, which offers vast opportunities for drug discovery and clinical trial processes to produce quality health solutions in a timely and efficient manner.

In the 3rd edition of the Pharma AI Summit, GlobalBSG will help you dive into a world of knowledge and real-life experiences in AI, machine learning, and data science shared by well-endorsed speakers in the field. In this manner, GlobalBSG serves the overarching need of the pharma experts to stay updated, informed, and connected in this digital transformation era.

This event is a platform that provides a comprehensive view of AI’s role in the pharma industry, fosters industry relationships, providing the opportunity for the development of innovative business solutions.

Key topics:
• Post-pandemic growth of AI in the pharmaceutical industry: opportunities, lessons learned, challenges
• Disease identification, diagnosis and drug discovery with the help of AI: Data-driven approach
• Accelerating research and development timeline in drug discovery with AI
• Multi-functionality of AI in the pharmaceutical sector: quality control, sustainability, production reuse, predictive maintenance of supply chain
• Personalised patient care and monitoring with AI
• AI’s role in medication safety
• Investing in the right AI solutions to deliver the right drugs for the needs of the post-pandemic decade
• Creating beneficial networks with suppliers and contract manufacturers to improve end results