Machine learning and artificial intelligence (AI) hold the potential to transform healthcare and open up a world of incredible promise. But we will never realize the potential of these technologies unless all stakeholders have basic competencies in both healthcare and machine learning concepts and principles. This interesting information came to us from Cosmos in their article, “Are machine-learning tools the future of healthcare?

Machine learning models and algorithms can inform clinical decision-making, rapidly analyzing massive amounts of data to identify patterns. However, human beings can only process so much information. Machine learning allows more processing capability than what an individual human could imagine.

However, even though humans can’t process at the volume and speed as emerging technologies, it is still important to understand how the results are being applied in the various fields. Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms. Explainable AI is used to describe an AI model, its expected impact and potential biases. Why is this important? Because explainability becomes critical when 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.

Sponsored by Access Innovations, the intelligence and the technology behind world-class explainable AI solutions.