Let's look at the three fundamental concepts in AI implementation and the benefits and challenges associated with each.
Generative AI is a branch of artificial intelligence that can operate autonomously, utilizing machine learning algorithms, neural networks and large language models to create original creative content. It involves training models to generate new data that resembles human-like output, enabling the production of unique and innovative content without direct human input.
However, while generative AI is paving the way for a variety of revolutionary benefits, there are still challenges associated with the technology that companies must understand before rushing to implementation. In this instance, organizations"running before they can walk" could be a perilous gamble. AI systems are only as good as the data they operate on. The relevance of the data is paramount, as irrelevant or outdated data can lead to inaccurate predictions and insights. Businesses must ensure they have robust data management practices in place, including regular data updates, data cleansing and data validation processes.
Interoperability is also a concern here, particularly as the stakes in making key decisions are often so high. The healthcare sector tends to be quite disparate in most countries, with siloed resources and disconnected patient journeys. Any AI system would therefore need to be interoperable with multiple systems, including systems for digital health records and prescriptions.
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