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How Biopharma Can Use Advanced AI and ML

The process of researching, developing, and ultimately commercializing a treatment for patients, regardless of the therapy area, is a long and costly one with slim chances for success.  For example, the average R&D investment in developing a new drug treatment is $1.3 billion, the median development time is 5.9 to 7.2 years for non-oncology and 13.1 years for […]

AI and Machine Learning Trends to Watch in 2023

This article highlights 10 of the biggest trends triggered by technological advancements in artificial intelligence (AI) and machine learning (ML). These trends have collectively revolutionized the way businesses approach everything from education and economics to the environment.  The broad AI and machine learning trends include the provisioning of cloud platforms for data activities – accelerating the use […]

Health Care AI: A Failure of Ambition

Few fields are as aligned with technological development as medicine. It’s fair to say that medicine as a practice has been transformed by technology and now completely relies on it across all its facets, like drug development, medical diagnosis, and augmentation with prosthetic limbs. It’s been the source of new technology developments, such as MRI […]

Data Quality Best Practices to Discover the Hidden Potential of Dirty Data in Health Care

Dirty data – data that is inaccurate, incomplete, or inconsistent – costs the U.S. $3.1 trillion per year, according to IBM. Along with the staggering cost, it prevents health care stakeholders from realizing the enormous potential value that they could be realizing from downstream analytics, including population health management, value-based care, and digital health. Health plans will […]

AI Trends for 2023: Sparking Creativity and Bringing Search to the Next Level

2022 was a big year for AI, and we’ve seen significant advancements in various areas – including natural language processing (NLP), machine learning (ML), and deep learning. Unsupervised and self-supervised learning are making ML more accessible by lowering the training data requirements. Large language models (LLMs) have shown impressive capabilities not just in natural language […]

MLOps: Why Now for Open Source

According to Gartner, a whopping 47% of machine learning experiments fail to reach experimentation. This number is stunning on the surface, but it is even more troubling when you consider the deluge of demands the MLOps workforce is facing in the wake of the COVID-19 pandemic. With the MLOps market size set to balloon to over $6 […]