“Digital health” is a frequently-used term these days. It refers to the use of information and communications technologies in medicine and other health professions to manage illnesses and health risks and to promote wellness. And the field of digital health is growing every day. Harvard Business Review brought this topic to our attention in their article, “How to Use Digital Health Data to Improve Outcomes.”
As data about our health continues to accumulate, we should understand it better. Having a lot of data is not enough. Digital health employs more than just technologies and tools, advances in artificial intelligence (AI), big data, robotics and machine learning continue to bring about major changes in digital healthcare.
Figuring out how to develop systems to use a growing quantity and variety of digital information is perhaps the most important, and formidable, health care mission of our time.
Sadly, most organizations have little knowledge of how AI systems make the decisions they do, or how the results are applied in various fields. Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms. Why is this important? Because the results can have an impact on data security or safety.
Melody K. Smith
| Data Harmony is an award-winning semantic suite that leverages explainable AI. |
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Sponsored by Access Innovations, the intelligence and the technology behind world-class explainable AI solutions.
