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11/02/2018

Embracing Artificial Intelligence and Machine Learning for Healthcare

Orlando Agrippa, CEO, Draper and Dash: Embracing Artificial Intelligence and Machine Learning for Healthcare

Emerging trends like artificial intelligence (AI) and machine learning (ML) look set to provide early adopters and forward-thinking firms deep insights and advanced analytics that could radically transform and revolutionise healthcare into a new digital age. Integrating such technologies with existing systems may seem challenging to those responsible, but the benefits could be unbounded. Potential benefits could include predictive and prescriptive analytics, driven by rules-based and structured algorithms that are designed to deliver better business outcomes, streamlined processes, reallocation of resources and improved patient care. It could also include reaction, diagnosis or prescription to serious health issues based on numerical reasoning.

This new wave of transformation is being driven by technology and digital data that some might describe as the industrial revolution 4.0. That is, in short, the entire combination of digital, technological, and repeatable methods of producing work through machines or robotics. The question on everybody’s lips is, can these futuristic looking trends provide the real value promised, and do the technology partners have the expertise and computational power to scale and deliver whilst protecting valuable and sensitive data? Orlando Agrippa, CEO, Draper & Dash, outlines the incredible rise of AI and ML and discusses how these mega-trends are set to impact healthcare.

What are AI and ML?

In its simplest form, AI is the development of computer systems that can perform tasks normally requiring human intelligence, such as decision-making based on non-emotional factors. ML allows data derived from AI to learn by focusing on prediction-making and prescriptive outcomes and continues to learn. Both are intrinsically linked, and the significant rise of ML is due to speed and scalability of computational power – in other words – the cloud and the growing power of on-premise machines.

This means, automation of mundane tasks or streamlined workflows can be quickly and easily configured by data scientists as long as digital data aligns and frameworks are in place. One striking and prominent feature of AI and ML is that a machine has the potential to tell a person something they didn’t know. This means, rather than humans teaching computers everything they need to know, computers will indeed be able to impart wisdom to humans.

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