In healthcare, outcomes are the measure that technology, human resources and financial investments are decided against. As providers continue to target improved patient outcomes, more organizations are utilizing artificial intelligence (AI), data analytics and machine learning. It is important, however, that organizations follow ethical standards to ensure positive results when implementing new technologies. Health IT Analytics brought this interesting topic to our attention in their article, “Ethical Artificial Intelligence Standards To Improve Patient Outcomes.”
From clinical applications in areas such as imaging and diagnostics to workflow optimization in hospitals to the use of health apps to assess an individual’s symptoms, many believe that AI is going to revolutionize healthcare. With this growth comes many challenges, and it is crucial that AI is implemented in the healthcare system ethically and legally.
Unfortunately, most organizational leaders have little knowledge on how AI technologies make the decisions they do. Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms. Why is this important? Because explainability becomes critical when the results can have an impact on data security or safety.
Melody K. Smith
Sponsored by Access Innovations, the intelligence and the technology behind world-class explainable AI solutions.