AI Adoption: Healthcare and Non Healthcare Drivers

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Based on research by Leo Petersen-Khmelnitski, LinkedIn

The first health-related problem-solving software was developed in the 1960s but such solutions did not enjoy widespread use by health practitioners until the late 1980s, when personal computers arrived with connectivity ensured. In recent years, there has been a proliferation of AI use within healthcare. AI adoption has been driven by a mixture of drivers, both from within and beyond healthcare.

Drivers outside the Healthcare

The explosion of AI adoption has not been limited to the healthcare sector by any means. There are three key drivers behind this growth: technological advancements, computing power and the availability of data.

AI technology is advancing at an exponential rate, especially when it comes to the power of neural nets. This significant technological advancement has given rise to the proliferation of AI applications
AI technology is advancing at an exponential rate, especially when it comes to the power of neural nets. This significant technological advancement has given rise to the proliferation of AI applications
The increase in computing power has facilitated the possibilities of AI, and thus its adoption. The cloud offers virtually unlimited capacity to instantly scale up or down the processing and storage of massive amounts of data, which AI runs on
The increase in computing power has facilitated the possibilities of AI, and thus its adoption. The cloud offers virtually unlimited capacity to instantly scale up or down the processing and storage of massive amounts of data, which AI runs on
AI commonly relies on, and is needed to handle, large datasets. Therefore, the huge volume of data that is increasingly available to train AI models and algorithms, particularly in the realm of machine learning, has been a huge factor in the proliferation of AI – both its development and adoption
AI commonly relies on, and is needed to handle, large datasets. Therefore, the huge volume of data that is increasingly available to train AI models and algorithms, particularly in the realm of machine learning, has been a huge factor in the proliferation of AI – both its development and adoption
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Drivers related to healthcare

The three external drivers above have contributed to the adoption of AI in healthcare, but there are also some specific developments within the health sector that have advanced AI use.

The rise in the volume of health-related data has increased the potential of and need for AI technologies. In particular, advances in genomic sequencing and the widespread implementation of electronic health record systems have increased the availability of large datasets as well as the need for AI-powered analytics
The rise in the volume of health-related data has increased the potential of and need for AI technologies. In particular, advances in genomic sequencing and the widespread implementation of electronic health record systems have increased the availability of large datasets as well as the need for AI-powered analytics
Technological improvements in natural language processing and computer vision have also contributed to AI adoption in healthcare, as they have allowed computers to simulate human perceptual processes and thus created wider possibilities for applying AI
Technological improvements in natural language processing and computer vision have also contributed to AI adoption in healthcare, as they have allowed computers to simulate human perceptual processes and thus created wider possibilities for applying AI
Improvements in deep learning and enhanced precision of robot-assisted surgery are also recent developments in healthcare that have and will continue to contribute to AI adoption in the healthcare sector
Improvements in deep learning and enhanced precision of robot-assisted surgery are also recent developments in healthcare that have and will continue to contribute to AI adoption in the healthcare sector
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