Policy As a Driver of AI in Healthcare I
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 (Figure 1.) Figure 1 This is both a driver and sign of the growth of […]
Increased Data and Investments as Drivers of AI in Healthcare
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 large amounts of data and for the first time in human history, healthcare is producing […]
Key Drivers of AI in Healthcare
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 is available in digital format is the main driver. Without it, we don’t have much […]
AI Talent Shortage in Healthcare
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 on new data sources and doing the requisite analytics to address business problem issues that […]
Health System Governance of AI Solutions II
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 of the design team imprint upon how the system makes decisions and learns from its […]
Health System Governance of AI Solutions I
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 charge of various elements as well as how to avoid patient harm. The governance teams […]
Legacy IT systems Pose A Risk To AI Adoption in Healthcare
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 architecture, managing data more tightly, and establishing new practices. Health care is, if nothing […]
Medical – Legal Barriers to Adoption of AI in Healthcare II
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 of litigation. This is a concern when products are developed without a complete understanding of […]
Medical – Legal Barriers to Adoption of AI in Healthcare I
One key barrier in AI adoption in healthcare is med-legal implications of using AI algorithms that make predictions and provide recommendations. Clinicians could rely on these to make key decisions about patient management. What if there are issues with those recommendations? What if the patient is harmed as a result of the AI recommendations? who’s […]
Provider Workflow Issues in Adoption of AI – II
One of the key issues with implementing AI solutions in healthcare settings is the fact that these algorithms are often in the cloud and the medical center data would need to leave the institution to be examined using the algorithm. Security and privacy of patient data that may be put at risk because of the […]