Nutrition in the digital age: how AI is transforming clinical practice

Juan is admitted to the hospital and they serve him his food. Health personnel spend a smartphone above your tray and, in seconds, an application identifies and quantifies the food it contains, also estimating its nutrients. When he finishes eating, the device analyses the tray again to estimate what he has eaten and evaluate possible nutritional risks.

Can a machine help us better understand how we eat and detect nutrition problems more accurately? Artificial intelligence (AI) is surprisingly answering this question. These technologies are already applied in health centers and laboratories to analyze diet more accurately, design treatments tailored to each person and support research into diet-related diseases.

From algorithms that process thousands of data to devices capable of monitoring what a patient eats in real time, AI is opening the door to more personalized and effective nutrition. This article will look at some of the areas in which AI is changing clinical and scientific practice.

How AI can help measure what we eat

Knowing precisely what and how much we eat is one of the great challenges in nutrition. If the methods that existed until now could lead to errors since they depended on people's sincerity and memory, AI is now capable of making more accurate estimates.

Thanks to computer vision, one simple photography of a plate may be enough to identify the foods it contains. Through the algorithms deep learning, you can even estimate portion sizes and calculate the nutrients present. 

In some hospitals they are already used systems that compare a patient tray hospitalized before and after eating, allowing you to automatically calculate how much you have eaten. Additionally, there are technologies that use acoustic sensors or mandibular movement to monitor when and how much a patient eats.

All of this translates into more objective and reliable data for nutrition professionals, who can then design more personalized dietary plans according to the real needs of each patient.

AI in disease prevention and nutritional risks

Our diet not only influences our weight, but also the appearance of diseases. Therefore, one of the most promising fields of AI in nutrition is its potential to help in prevention and detect risks early.

We find an example in the study of the intestinal microbiota, those bacteria that live in our intestine and that play such an important role in our health. Deep learning algorithms can analyze millions of DNA sequences from those bacteria and discover how diet influences their balance. With this information, patterns associated with obesity or diseases such as inflammatory bowel disease can be identified before symptoms appear.

In hospitals and nursing homes, AI is also beginning to be a great ally. Thanks to the automatic analysis of data such as the patient's weight variation, their intake records, their analyzes and their clinical evolution, Intelligent systems can detect in time who is at risk of malnutrition. This not only improves clinical care, but also reduces costs by avoiding future complications.

Even in imaging tests such as computed tomography, deep learning allows detecting loss of muscle mass. This is very important in hospitalized patients, since early detection again prevents complications and improves recovery.

Diet therapy and clinical nutrition with the help of AI

When we talk about nutrition in a clinical setting, every detail matters. The amount of carbohydrates, proteins or micronutrients could make the difference in a patient's outcome. And this is where AI begins to play an important role: it does not replace the healthcare professional, but it can be an ally to design more precise and personalized interventions.

Diabetes is an example: thanks to computer vision systems, we now it is easier to count the carbohydrates in a dish from a simple photo. This helps people with diabetes to better self-manage their meals. Furthermore, some developments integrate biomarkers in real time, such as glucose levels, to offer dietary recommendations adapted to the physiological situation of each moment.

In the case of oncology patients, they have also been designed chatbots specific for them that accompany them in the different phases of the treatment, offering practical advice on nutrition to improve their well-being.

In hospitals, AI goes further: models of machine learning They even allow you to adjust in real time the enteral or parenteral nutritional support formulas, adapting them to the patient's clinical changes. This optimizes the precision of treatments and reduces risks.

In short, AI does not replace the experience of the healthcare professional, but it does become a fundamental support in decision-making, contributing to more precise, safer nutritional care adapted to the situation of each patient.

Nutrition research: the role of AI

AI is also revolutionizing nutrition research. With it, today it is possible to carry out more solid, personalized studies with a direct impact on the clinical practice of the future.

In epidemiological studies, for example, AI can analyze data from tens of thousands of people to find associations between diet and health that would go undetected with traditional statistical tools.

Another of its applications is in unsupervised learning, which helps segment the population according to their eating patterns. This means that consumer (or patient) profiles can be created and design specific dietary strategies for each group, thus increasing the effectiveness of nutritional interventions.

An especially promising field of current research is precision nutrition. here, AI integrates data of biomarkers, information on food consumption and anthropometric measurements such as weight, height or body composition. Although it is still an approach in development, it is already laying the foundations for future treatments much more tailored to specific genetic or metabolic profiles.

An evolving field

At the end of the day, thanks to these tools Juan receives a diet tailored to his needs, and the medical team can monitor his evolution with precise data in real time. What started as a simple photo of your food tray is integrated into a much larger system, where AI helps personalize nutrition and improve patients' health.

Far from being a tool of the future, AI is already a reality that is transforming nutrition both in clinical practice and in research. Its scope ranges from the automation of routine tasks such as recording intake to complex analyzes that allow interventions to be personalized and advance scientific discovery.

The challenge in the coming years will be to integrate the analysis capacity of AI with the experience and judgment of health professionals, without forgetting the cultural, social and human dimension that characterizes food. Are we prepared?