AI in healthcare offers transformative solutions for health equity, enhancing diagnostic accuracy, administrative efficiency, and personalized care. Leveraging AI can address disparities, improve access, and deliver tailored treatments, making it a crucial tool for achieving equitable healthcare outcomes across all demographics, fostering a fair and just healthcare system.

Change management requires substantial resources, dedication, and persistence. They cannot simply be put on executives’ “to do” list. The line organization should be supported with systematic planning and a programmatic organization. These measures require investment and there are calculated trade-offs required. Yet if organizations neglect to take these steps, their digital investments will fail to yield a payoff, and competitors and incumbents continue to siphon away customers, and start to extend their competitive advantages at accelerating rates.

Classical Machine Learning techniques have achieved incremental improvements over time and are fundamentally limited in certain areas. In contrast, neural network-based Deep Learning techniques like CNNs, RNNs are unique in that their accuracy scales almost infinitely with the amount of available data. One of the most well-known applications of Deep Learning, autonomous vehicles, would not be practically feasible without modern Deep Learning techniques.