AI Articles and Analysis in Healthcare

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.

Elevance Health is a health insurance provider headquartered in Indianapolis, Indiana. It was founded 20 years ago as a merger between Anthem and Wellpoint Health Networks. The company offers medical, dental, pharmaceutical, behavioral health, and long-term care plans.

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.

Contact centers play a crucial role in delivering exceptional customer experiences (CX) across industries, both B2c and B2b. As customer expectations elevate, organizations increasingly leverage AI to streamline operations and enhance customer service. According to recent reporting in the MIT Technology Review, enterprises generally deploy AI to transform contact centers, usually taking the form of intelligent virtual assistants to obtain predictive customer insights as well as for dispute and fraud management. 

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. 

According to research from the American Society for Biochemistry and Molecular Biology, nine out of 10 drugs fail to make it to market in the clinical trials process. Researchers in the cited studies found that 40-50% of these failures are due to a lack of clinical efficacy, meaning that the drug is not able to produce its intended effect in people, and 30% were due to unmanageable toxicity or side effects.

The applications of AI in the healthcare and life sciences industries are vast, with data-powered algorithms and analytics promising to upend the status quo. Applications, including the use of AI to interpret medical imaging, optimize drug discovery and development, analyze large-scale data sets to identify patterns among patient populations, and streamline provider workflows, are already transforming the industry.