Setting Industry Standards for Applications of AI in Healthcare IV
Major health plans along with health technology companies like Philips and Ginger collaborated to develop a new standard to advance trust in artificial intelligence solutions. Convened by the Consumer Technology Association (CTA), a working group made up of 64 organizations set out to create a new standard that identifies the core requirements and baseline to […]
Setting Industry Standards for Applications of AI in Healthcare III
in 2020, Guidelines for clinical trial protocols for interventions involving artificial intelligence: (the SPIRIT-AI Extension) was released to provide more structure and standards for the increasing number of clinical trials for A-based clinical interventions. Also, updated standards for reporting the results of trials involving AI interventions, Consolidated Standards of Reporting Trials–Artificial Intelligence (CONSORT – AI […]
Setting Industry Standards for Applications of AI in Healthcare II
American Medical Informatics Association (AMIA) has proposed a framework for the regulation of AI decision support. AMIA has postulated that the development and implementation of clinical decision support (CDS) that trains itself and adapts its algorithms based on new data—here referred to as Adaptive CDS—present unique challenges and considerations. Although Adaptive CDS represents an expected […]
Setting Industry Standards for Applications of AI in Healthcare I
In 2021, the World Health Organization issued a report about AI in Healthcare called Ethics & Governance of Artificial Intelligence for Health. It is the product of eighteen months of deliberation amongst leading experts in ethics, digital technology, law, human rights, as well as experts from Ministries of Health. While new technologies that use artificial […]
Improved methodology of AI, powerful computers, cloud computing
Another major driver for the emergence of AI in healthcare is the fact that we now have more powerful computers with stronger Graphic Processing Units (GPU) and cloud computing. You need a lot of computing power to do the type of analytics heavy lifting that AI algorithms do. Cloud computing allows the algorithms to be […]
Policy As a Driver of AI in Healthcare III
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 its inputs, the logic it employs (when possible), the role intended to be served by […]
Policy As a Driver of AI in Healthcare II
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 ensuring safety (as an assistive device) and acceptable efficacy, we can get these technologies in […]
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 […]