Precision nutrition is a comprehensive approach towards understanding and implementing a diet plan that fits different body types. It takes into account a multitude of factors such as an individual’s genetics, physiology, body type, eating behaviors, microbiome, health conditions, stress levels, social influences, and food environment. However, it’s not an easy task to keep track of all these factors and their interactions, considering their variance in different individuals and over time. This is where the power of artificial intelligence (AI) becomes advantageous.
Artificial intelligence, an immensely popular term nowadays, could be a valuable ally in the pursuit of precision nutrition. It’s not merely a buzzword. AI essentially means any computational method that imitates human cognitive functions beyond following a set of instructions. AI has numerous branches, each with distinct approaches and tools, which keep expanding every year.
In precision nutrition, AI could be leveraged in various ways. For instance, AI could help monitor multiple variables, combine different datasets, and assess how they interweave. Such methods could also figure out how the findings from a singular nutrition study can be relevant to diverse conditions and situations, thereby enhancing the study’s utility and value. Most importantly, various AI techniques can do these quickly, much faster than humans.
Artificial intelligence methods fall on a continuum, based on their design and operation. On one extreme, there are completely data-driven AI approaches, which begin with a bulk of data and attempt recognizing patterns, associations, and trends. These techniques analyze data faster and work on multiple datasets simultaneously.
For instance, a data-driven AI method might analyze various datasets and single out that consumption of a specific food item tends to increase lifespan duration. However, the challenge is that such methods cannot justify why such an association exists. They cannot really distinguish whether that specific food item has some actual beneficial nutritional effect or it’s just a coincidental correlation.
On the opposite end of the spectrum lies the mechanistic or explainable AI methods. These approaches try to imitate how things actually happen by recreating the mechanisms behind a process or decision. They provide a specific explanation for a result.
For instance, the mechanistic AI tool can simulate why a person may choose to consume a particular food item, the nutrients it contains, how they are metabolized in the body, how these nutrients affect different organs, and how this ultimately impacts the person’s lifespan. This AI model can then analyze what would happen over time if different people consume that food item and decide who derives benefits from eating that item and in what manner.
Ideally, both these AI methods from opposite ends of the spectrum should be interlinked. A purely data-driven approach can suggest associations that can guide the creation of more mechanistic AI approaches. Similarly, a mechanistic AI approach could specify where data-driven approaches are required.
However, like the imperfections of a clinical trial or observational study that can lead to misleading results, a poorly-designed AI approach can also cause similar errors. Therefore, it’s vital to verify the methodology used in an AI approach and understand its relative benefits and drawbacks. Nevertheless, don’t let the quest for perfection deter the application of AI in probability estimation.
The integration of AI in generating precise recommendations is not a fresh concept and has been employed in several fields like meteorology, finance, and aerospace engineering. These fields have long utilized computational methodologies to combine and analyze complex data from various sources to make accurate deductions and predictions.
In conclusion, while AI might not contradict established nutritional guidelines like the significance of consuming fruits and vegetables, it certainly has the potential to revolutionize the field of nutrition. The goal towards achieving precision nutrition is difficult, but AI could be a powerful tool in this endeavor. However, as a tool, it’s important to know what AI can and can’t do and use it correctly. Just like any useful tool, it needs to be handled with care.
