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AI’s Masterstroke: Navigating and Overcoming the Eight Biggest Challenges in Pharma
AI’s Masterstroke: Navigating and Overcoming the Eight Biggest Challenges in Pharma

January 18, 2024

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Highlights:
  • During the pandemic, AI swiftly repurposed drugs like baricitinib for COVID-19, saving time and ensuring rapid treatment access. AI predicts compound interactions, reducing drug discovery timelines and costs significantly.
  • Despite its success within some organizations, widespread adoption faces hurdles from cumbersome pharmaceutical practices, prompting an exploration of the challenges existing in the industry.
  • Delving into the intricacies of the pharmaceutical landscape has revealed eight formidable challenges confronting enterprises today. Yet, with the transformative power of AI, these challenges can now be effectively addressed. 
  • However, it’s crucial for enterprises to understand that data is the lifeblood of AI, necessitating meticulous management and strategic utilization. Within this context, Ethical Data Management and Data Democratization stand out as pivotal pillars, laying the groundwork for responsible AI advancement. 
  • Acting as a central nexus, Unified Data Management (UDM) emerges as the linchpin, seamlessly integrating both Ethical Data Management and Data Democratization and steering the industry towards innovation driven by collective intelligence and a commitment to enhancing patient outcomes.

The urgent demand for effective treatments became undeniable during the pandemic’s grip on the world. However, the traditional route for developing drugs is known for its lengthy and costly process. That is when AI stepped in as a beacon of hope. By sifting through vast amounts of medical data, AI algorithms could pinpoint drugs with established safety records that could combat COVID-19. A notable instance occurred at AstraZeneca, where AI aided in identifying baricitinib, a medication for rheumatoid arthritis, as a potential remedy for COVID-19 patients. This swift repurposing not only saved crucial time but also tapped into existing manufacturing and distribution networks, ensuring speedy access to the drug for patients in need. Although this breakthrough occurred within one organization and led to a breakthrough, its widespread adoption across the industry was prevented due to cumbersome pharmaceutical practices. So, what are the formidable challenges pharmaceutical companies grapple with? How can AI help these companies navigate these obstacles? Let’s deep dive into this.

 


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