AI Articles and Analysis in Pharma

Procurement Data
for Business Intelligence in Life Sciences@2x

Procurement Data for Business Intelligence in Life Sciences – with Jennifer Sieber of Gilead, Len DeCandia of Johnson & Johnson, and Edmund Zagorin of Arkestro

From intelligent sourcing and predictive analytics to automated contract analysis and risk mitigation, AI is enabling procurement teams to focus on strategic activities while delivering significant cost savings and improved supplier relationships.

Artificial Intelligence at Geico-2x-2-min

Artificial Intelligence at Johnson & Johnson

In 2020, we wrote about the early but fertile stage of AI development at Johnson and Johnson. Since then, the COVID-19 pandemic and improvements to machine learning have forced important changes in how J&J is using artificial intelligence. 

Winning Buy-In for Life Sciences Manufacturing and Supply Chain Management-1x-min

Winning Buy-In for Life Sciences Manufacturing and Supply Chain Management – with Shreyas Becker of Sanofi

As life sciences organizations increasingly adopt AI to enhance productivity, streamline workflows, and improve quality, aligning AI initiatives with business objectives and evolving traditional return on investment (ROI) metrics have become essential strategies. These practices not only secure executive buy-in but also ensure sustained success in AI adoption.

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Artificial Intelligence at Lilly

Lilly and Company is a global pharmaceutical corporation founded in 1876 and headquartered in Indianapolis, Indiana. The company has offices in 18 countries and sells its products in approximately 125 countries worldwide. As of 2024, Lilly employs over 38,000 people globally and has an annual revenue of $34.12 billion. 

Driven Approaches 
to Infrastructure

Driving Patient Experiences Through Data Science-Driven Approaches to Infrastructure – with Xiong Liu of Novartis

Explainable AI models are essential in pharmaceutical R&D because they provide transparency and understanding of how AI-driven predictions are made. In drug discovery and development, stakeholders, including researchers, regulatory bodies, and healthcare professionals, need to trust and understand AI models' outputs to make informed decisions. Without explainability, AI models can be seen as "black boxes," leading to skepticism and reluctance to adopt these technologies in critical decision-making processes. 

V.1 – How AI Drives Drug Development Workflows and Value Chains Across Life Sciences Enterprises-1x-min

How AI Drives Drug Development Workflows and Value Chains Across Life Sciences Enterprises – with Leaders from Benevolent and Takeda

This article/interview analysis is sponsored by BenevolentAI and was written, edited, and published in alignment with our Emerj sponsored content guidelines. Learn more about our thought leadership and content creation services on our Emerj Media Services page.

Artificial Intelligence at AstraZeneca-1-min

Artificial Intelligence at AstraZeneca

AstraZeneca is a global biopharmaceutical company that researches, develops, manufactures, and markets prescription drugs and vaccines. Its key therapeutic areas include oncology, cardiovascular, renal, metabolism, respiratory, and immunology. In 2022, the company reported revenue of $42.67 billion and a profit of $4.08 billion. The company has a significant global presence, employing around 89,900 people across more than 60 countries as of 2023.

Adopting Generative AI in Healthcare Organizations-1-min

Adopting GenAI in Healthcare Organizations – with Prashant Natajaran of H2O.ai

As stakeholders in the life sciences and healthcare industries rush to adopt GenAI and integrate it into their projects and processes, they are walking a tightrope. GenAI will play a vital role in the future of healthcare in ways that we can hardly yet imagine; on the other hand, concerns like patient data privacy and potential inaccuracies have led many to take a cautious approach.