AI in Healthcare Blogs

The concerns  around regulation of AI-based solutions in healthcare is real. Given the unique challenges of labeling for AI/ML-based devices and the need for manufacturers to clearly describe the data that were used to train the algorithm, the relevance of … Read More

In conversations with Baku Patel,  former Chief Digital Health Officer Global Strategy and Innovation at the FDA, he indicated that the FDA’s lighter touch for AI solutions  is informed by the fact that these are not static technologies and by … Read More

One of the key drivers of AI in healthcare has been the shift in policy and regulatory approaches in this sector. There has been significant increases in the number of AI-enabled solutions with FDA approvals or clearance in recent years … Read More

Before anything can be discussed about why the time for AI in healthcare has arrived, we need to say that if we did not have an increasing amount of digitized data, none of the other factors would matter. AI needs … Read More

There are a number of major drivers for the development and adoption of AI in Healthcare solutions (Figure.) I would like to think that before we speak about anything else, we start by saying that the fact that more data … Read More

One of the most challenging aspects of AI deployments has been the recruitment and retention of data science talent. Creating an AI-ready enterprise will require organizations to build a team of data custodians: experts at blending information sources, providing feedback … Read More

Many of the concerns around AI revolve around how the technology reinforces bias within the healthcare system. There are two entry points at which bias can seep into an AI tool. The first is in the design itself: The biases … Read More

Health systems and care providers must be vigilant in ensuring that the models they implement foster better care and promote health equity and are not biased. Efforts must include a legal, regulatory and compliance review to decide who is in … Read More

Although enterprise-wide AI will begin in the traditional IT department, few departments, however, are prepared to take on the complexity and challenges that becoming AI-enabled will pose. Much like the rest of the organization, AI will require upskilling, modernizing legacy … Read More

The mutability and opaque nature of AI makes it difficult to determine liability for malpractice claims and professional regulatory standards. Health systems that choose to implement AI before the case law on these issues is established might increase the risk … Read More