The AI Revolution Series – Introduction to AI Applications in Healthcare

Artificial Intelligence (AI) is the science of making machines that can think like humans. The goal for AI is to be able to do things like recognize patterns, make decisions, and judge like humans. AI has been adapted into many technologies and has already begun to revolutionize how care is delivered and managed. Among the many applications of AI in healthcare, two distinct types have emerged as particularly impactful: generative AI and predictive AI. Understanding the differences between these two forms of AI is crucial for being able to look at how they can both contribute to healthcare.

Predictive AI, also known as predictive analysis, is a subset of AI that focuses on analyzing historical data and machine learning algorithms to analyze patterns and forecast future events or conditions. In healthcare, this means utilizing vast amounts of patient data, including medical histories, lab results, and lifestyle factors, to predict health outcomes. For instance, predictive AI can analyze data from a large number of consumers with diabetes or high blood pressure to identify patterns based on indicators like weight gain or loss, along with dietary patterns to create recommendations for specific individuals. We need to ensure that the privacy of the individual consumer remains intact, which is why using AI to focus on groups might be the best route. This foresight enables healthcare providers to implement preventative measures, tailor treatment plans, and improve consumer outcomes. Predictive AI is invaluable in early detection, population management, and enhancing personalized care. 

Generative AI, on the other hand, involves training models to create original content. Models learn patterns from existing data and generate new data based on these patterns. Using algorithms, generative AI creates new content like images, texts, or data sequences, which can be indistinguishable from real human-generated content. In healthcare, generative AI has many great applications, such as generating synthetic data for research purposes, creating realistic medical images for training or diagnosis, and simulating responses to various treatment plans. Generative AI can also be used to provide insights for consumers. Consumers will be able to ask questions about a diagnosis they may have and receive a personalized progression forecast, then align the progression with the home modifications or assistive technology that could be helpful.

While both generative and predictive AI have their unique functionality, they both have a role to play in enhancing healthcare. As advocates, we see the combination with immense potential in enhancing independence and self-direction, with increasing informed choice. As these technologies continue to evolve, their integration into healthcare systems promises to make healthcare more proactive, personalized, and innovative.