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AI in Healthcare


Today’s consumers – including those in healthcare increasingly expect choice and personalization. These forces are creating huge disruptive pressure on the healthcare ecosystem.

AI classifies data based on the relationships between many different interconnected factors. Unlike traditional software, which follows rules defined by software engineers, AI automatically formulates the rules from the data it is trained with. So, an AI model fed large numbers of images of different skin rashes can learn to spot each type based on their unique combination of characteristics without being told what a particular rash looks like.

This whitepaper has highlighted the potential for patient-centric healthcare and both the critical roles of AI and data science in delivering it, in addition to the questions we should be asking ourselves. Exploring the potential of AI in an organization comes with significant challenges, as does industrializing it. In the final section of this whitepaper, we provide a guide to building and implementing AI, to support the journey to patient centricity.

Thanks to AI diagnostics, new levels of speed and accuracy are possible, helping doctors make correct diagnoses and expanding the likelihood that they will spot more unusual conditions. The upshot will be faster interventions and increased accurate diagnoses. This will reduce the costs of readmissions and increase the chance of initial treatments being successful. Most importantly, it will save lives.

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