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The Yuan Retrospective on 2022 AI - Better data is better healthcare
By Kirk Borne  |  Oct 13, 2022
The Yuan Retrospective on 2022 AI - Better data is better healthcare
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Last year, The Yuan contributor Kirk Borne predicted the evolution of AI in 2022. He takes a look to see whether 2022 has represented another year of growing macro trends in the global AI sphere, and whether it has continued the trend of opening up many new ways of thinking, surprising innovations, and more stable solutions to critical problems.

COLUMBIA, MARYLAND – Artificial intelligence (AI) adoption has been rapidly expanding in many sectors over the past few years. Many more organizations and enterprises jumped into AI in 2021, driven both by internal factors - improved efficiencies and effectiveness of processes - and by external factors - competitive advantage, market positioning, and fear of missing out.

Surveys and reports have revealed an approximately 50 percent increase in the funding of AI initiatives year over year for each of the past several years. The healthcare, pharma, and medical sectors are right there with other growth sectors, like manufacturing, retail, consumer engagement, entertainment, gaming, logistics and supply chain, government, finance, design, events and exhibitions, and scientific research. These macro trends were predicted to continue into 2022, and they have. However, more interesting are the different subfactors contributing to those trends, and this article will dissect and explore some of the different dimensions of these healthy AI growth trends.

First is the most significant dimension of growth: AI applications are sprouting everywhere as organizations are demanding more productivity and value from their data, machine learning (ML), and AI assets and investments. This trend held up strongly in 2022, though the technical skills talent gap continues to impose increasing headwinds that are an impediment to this increasing momentum. Consequently, organizations will likely continue to opt for more low-code/no-code (LC/NC) deployments (such as AutoML) to harness and maintain the growing AI/ML momentum. Although these LC/NC deployments are often considered less perfect than manually intensive models that have been hand-tuned by AI/ML experts, nevertheless this LC/NC trend will further differentiate the AI leaders from the laggards, supporting the old saying that ‘getting things done is better than getting things perfect.’

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