Data is Critical to Achieve AI Benefits in MedTech

SECTOR UPDATE - InterSystems 

With artificial intelligence, data is not just a minor component; it’s the foundational layer that amplifies AI’s potency, particularly in MedTech. When we peel back the layers of AI, we uncover elements that depend on data in fundamental ways. A new InterSystems whitepaper explores these relationships making data the bedrock of AI and shaping MedTech innovation.

Examples like a solution for early dementia detection exemplify AI’s potential to improve healthcare with early intervention. While often hailed as revolutionary, AI is not a panacea nor a plug-and-play solution. Beneath its surface, data is the fuel propelling key AI elements forward. 

Consider algorithms – a core element of AI. Without the nourishment of data to activate their learning and adaptive capacities, algorithms stay dormant. This is especially relevant in machine learning, which relies on data for extracting patterns and relationships for the training, testing and fine-tuning of algorithms.

Think about model architectures, another key component of AI. Their true power is only unleashed when data is seamlessly integrated and the models go beyond simple computation and operate more like complex neural networks, comprehending intricate patterns hidden in the data. 

The comparison to the human brain is a good metaphor because algorithms and model architectures represent just a glimpse into the broader spectrum of AI elements that interact with and depend on data. 

Monica Rogati’s AI Hierarchy of Needs underlines the importance of data. The basal tiers in the pyramid represent vital stages in data management, laying the foundation for implementing AI and Deep Learning at the top of the hierarchy.

Since data is the foundational bedrock shaping the essence of AI, resolving data-related challenges early on becomes pivotal for successful and robust AI implementations – and many MedTech developments.

Addressing AI data challenges early is analogous to the early detection of dementia. A proactive approach at the foundational stages has significant advantages in both. The common thread is that addressing challenges at the outset is key to fostering success and enhancing the well-being of individuals and systems.

In the InterSystems whitepaper, Getting the Most from AI in MedTech Takes Data Know-How, we explore examples illustrating the impact of data on AI performance, what data challenges to navigate, and why overcoming them is crucial to unlocking AI’s potential in MedTech. 

Source: InterSystems media release

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