AI Articles and Analysis in Healthcare

Unlike other industrial sectors, many healthcare systems and services are embracing the roadblocks in their technological adoption processes. Thoroughly vetting systems is far more critical for most forms of patient care than having the latest and greatest technology. 

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. 

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.

As technology advances, the lines between mobile applications and medical devices are blurring in ways that have healthcare leaders, software developers, and regulators alike facing new and unprecedented challenges.

Blue Cross Blue Shield (BCBS) is a federation of 35 independent health insurance companies that provide coverage to over 100 million Americans. The organization's national headquarters is located in Chicago, Illinois, while individual BCBS companies operate in various states across the country.

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. 

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. 

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.