wrangler AI in nutrition - the perks and pains

AI in nutrition the perks and pains

Jan 12, 2024 8:21am

Artificial intelligence also known as AI for short, has been around for decades already but it is only this year that we really say that its application has gone mainstream impacting every industry and every corner of the world. In this article we dive into how specifically AI has and is impacting nutrition and what it means for the future of health. We dive into the etical risks and assess readiness of companies to deal with the challenges.blog post note 

 

 

What is Artificial Intelligence (AI) in Personalized nutrition

Artificial Intelligence (AI) can be used in personalized nutrition in several innovative ways that can make data collection easy, lower the burden for users as well as make the use of devices and platforms more enjoyable.

Some examples:

  • Understanding Eating Habits: AI can help understand why people eat in a certain way, how they cook, and the decisions they make about food. This is done through the analysis of monitored diet data.

  • Computer Vision: Using computer vision, it is possible to take a photo of the dish and get an estimate of the portion size and the ingredients, providing a nutritional analysis of the meal.

  • Natural Language Processing: Instead of writing down everything you eat, you can talk on the phone and describe the meal. AI then associates this description with a food database to provide a nutritional analysis.

  • Data Integration: Different data sets, such as information from digital scales and wearables, can be integrated into the individual’s electronic health record.

These technologies represent a significant advance in nutritional research and the ability to provide personalized dietary recommendations that can continuously update instead of a static document or report.

  

What are the benefits of AI in nutrition?

The integration of ChatGPT and similar AI technologies into the field of nutrition is revolutionary, offering potential to enhance personalized nutrition and health outcomes. 

  • Personalized Nutrition: AI can analyze individual dietary patterns, genetic makeup, and lifestyle choices to provide tailored nutritional advice, potentially leading to better health outcomes and disease prevention.

  • Accessibility: With AI, nutrition advice can become more accessible to a wider audience, breaking down barriers of cost and location that traditionally limit access to professional guidance.

  • Data-Driven Insights: AI’s ability to process vast amounts of data can uncover new insights into nutrition science, leading to more effective dietary recommendations and interventions.

  • Complementing Professionals: Rather than replacing nutritionists, AI can serve as a tool that complements their expertise, automating routine tasks and allowing them to focus on more complex cases and human-centric aspects of care.

  • Continuous Learning: AI systems can continuously learn and improve their recommendations over time, adapting to new research findings and individual health changes.

    Embracing AI in nutrition does not mean eliminating human roles but rather evolving them to work synergistically with technology, enhancing the ability to support health and well-being on a global scale. The rapid advancements in AI necessitate staying informed and adaptable to leverage these tools effectively and ethically.

  

 

 Where AI can really make a difference in Personalized nutrition

 

Artificial Intelligence (AI) holds transformative potential in the realm of nutrition, particularly for those who are not yet fully engaged with digital technology.

  • Bridging the Digital Divide: While current users of nutrition-related apps, platforms, chatbots, and recommender systems are typically digitally savvy, a significant opportunity lies in reaching those less familiar with such technologies. By doing so, we can generate a more diverse and inclusive data set that reflects a wider range of dietary habits and health needs.

  • Targeting Underserved Populations: The true promise of AI in nutrition lies in its ability to research and improve the health of the most vulnerable and underserved populations. These are the groups that stand to benefit the most from personalized nutrition advice and interventions, yet they are often the hardest to reach.

  • Learning from Diversity: By leveraging AI to engage with these hard-to-reach communities, we can gain valuable insights into unique nutritional challenges and solutions. This knowledge can then inform broader strategies to enhance public health and well-being.

In essence, AI’s capacity to analyze complex data and provide personalized recommendations can democratize nutrition, making it more accessible and effective for all segments of society. This is where AI can truly shine, by serving as a tool for equity and health improvement on a global scale. The potential for AI to make a meaningful impact in nutrition is immense, and it is an area ripe for innovation and growth. Engaging with diverse populations will not only help those in need but also enrich our understanding of nutrition and health.

 

AI can serve as a tool for equity and health improvement on a global scale , but it's up to humans to make sure nobody is left behind or discriminated against.

 

 
 

Can we trust anything that uses AI?

The misuse of the term “AI” is indeed a significant issue. There are numerous instances where the term “AI” is used or applied inappropriately, often because it sounds more appealing than stating that a rule-based system has been used. This can pose a major risk, particularly in fields like personalized nutrition, which have the potential to greatly improve health and facilitate access to health information.

However, if AI is used predominantly as a marketing tool, we risk eroding consumer trust in solutions that could make a significant difference and have a positive impact on health.

One of the main concerns is how to capitalize on the increased awareness of nutrition and health post-COVID without compromising the accuracy and scientific basis of personalized nutrition.

It’s crucial that personalized nutrition remains rooted in science and continues to generate accurate results. Otherwise, we risk going in the opposite direction and undermining the potential benefits of these advancements.

 

Can AI drive behaviour change in Personalized nutrition solutions?

 

The potential of AI to drive behavior change is significant, especially as the adoption of new technologies grows. AI and machine learning are pivotal in analyzing vast datasets to glean insights that can promote better health behaviors.

However, it’s crucial to differentiate between various techniques under the AI umbrella, as some may be more effective than others in fostering behavior change. At this critical juncture, we recognize that behavior change is fundamental to personalized nutrition solutions. Digital twins are just an example of how advice can be provided in real-time to nudge behaviour change.

There’s little value in collecting diverse data sets from wearables, smartphones, digital scales, or kitchen robots if they don’t lead to actionable insights. The real challenge lies in identifying the key data points that can truly impact an individual’s health.

AI and machine learning are instrumental in this endeavor, but there’s still much to learn about the specific factors that will have the greatest impact on different population groups. The focus should be on leveraging AI to facilitate meaningful behavior change that enhances health outcomes.

 

Artificial intelligence has the potential to drive sustainable behaviour change, but  we need a lot more research to ensure this is representative and accurate

 

 

Is AI regulation good enough for nutrition?

 

Europe has often led the way in terms of ethics and privacy. Currently, we are entering a new phase. In the last decade, the focus was primarily on individual companies providing specialized advice on diet, physical activity, or lifestyle recommendations. However, there’s a growing realization that no single company can deliver everything to a high standard without making the product prohibitively expensive, leading to consumer reluctance.

The industry’s advancement now hinges on ecosystems and partnerships. These networks necessitate data sharing, which must be conducted ethically and securely to maintain consumer trust. Consumers need assurance that their data will not be sold, leaked, or mishandled.

We are at a stage where, due to the novelty of the situation, both public and private entities must collaborate. Since people spend most of their time in the community, at home, or outside rather than in hospitals, there is a shift towards a preventative care system. This system requires individuals to either self-monitor or be monitored, demanding better interaction between pharmacists, supermarkets, food retailers, and gyms.

For this to be feasible, data protection is paramount to preserve consumer trust. Ensuring data security is a significant concern that must be addressed to maintain confidence in these evolving health ecosystems.

 

Is personalised nutrition regulated enough? 

 Personalized nutrition is an emerging area that falls uniquely under a number of regulations as it covers the spectrum from prevention to medical nutrition. AI can therefore be integrated in different ways which can include:

  • Privacy Regulations: Europe has strong privacy regulations in place namely GDPR (2018)
  • AI act : new regulation that imposes restricions on how AI systems collect and use data
  • Medical nutrition vs. Wellness: There is a distinction is made between medical nutrition, which is advice provided by regulated professionals such as registered dietitians, and wellness, which is a largely unregulated area tat offers lifestyle advice
  • Nutrition & Health claims: such as EFSA which restricts the use of unsubstatiated claims that can be made on products and ingredients 

The discussion on AI regulation in nutrition highlights Europe’s leadership in ethical and privacy standards. The industry is evolving from single companies providing isolated advice to a collaborative ecosystem approach. This shift necessitates secure data sharing to maintain consumer trust and protect privacy.

The new industry phase involves both public and private entities, reflecting the reality that people spend most of their time in the community, not in hospitals. To transition towards a preventative care system that allows for self-monitoring or external monitoring, there must be secure interactions among pharmacists, supermarkets, food retailers, and gyms.

The primary concern is ensuring data protection to preserve consumer trust. As the industry moves forward, establishing robust security measures is essential to support the integrity of data sharing within these ecosystems. This will be crucial for the advancement of AI in nutrition and the broader health sector.

 
 

The discussion on AI regulation in nutrition highlights Europe’s leadership in ethical and privacy standards. The industry is evolving from single companies providing isolated advice to a collaborative ecosystem approach. This shift necessitates secure data sharing to maintain consumer trust and protect privacy. 

 

 
 

How representative are AI systems

There are a number of challenges with regards to AI systems which include a lack of transparency, a lack of inclusive data training sets as well as algorithmic bias that perpetuate historical biases and practices.

To address the issue of non-inclusive food databases in AI systems, it’s essential to:

  • Diversify Data Sources: Incorporate a wider range of foods from various cultures, particularly underrepresented ones like African and South American cuisines.
  • Increase Transparency: Provide clear explanations of how recommendation systems operate, moving away from the ‘black box’ approach.
  • Promote Equity: Ensure that all types of foods are represented fairly and without bias.
  • Engage Communities: Involve diverse groups in the development process to better understand and integrate their dietary practices and preferences.
  • Adopt a human in the loop approach where an expert can analyze and explain algorithms

 

Are businesses concerned about data ethics of AI?

 

Business Concerns on Data Ethics The topic of data ethics is crucial, especially in considering whether it exacerbates societal inequalities. Based on recent discussions with company executives, it appears that data ethics is not a high business priority, as it does not directly impact the bottom line. However, there is a growing recognition that placing the individual at the heart of new solutions and business models necessitates elevating data ethics as a key component of business strategy and priorities. This means that at the very least companies should have a data strategy, a AI ethics policy as well as various initiatives to ensure that all stakeholders have access to bias training and awareness of unintentional impact of AI systems.

 

 

Artificial intelligence nutritionist in your pocket?

The rapid adoption of AI in healthcare opens up new ways to deliver information and care in novel ways. AI opens up the opportunity to deliver personalized health by unravelling hidden patterns and trends as well as providing a deeper understanding of an indiiviudla' health taking into account their social health status as well as budget, preferences and health goals.

Secondly a shift towards prevention is opening up ways for AI to deliver interventions, cheaper, faster and more effectively. This cold translate into providing nudges based on real-time data to the right person at the right time. The future will be different because advice will be in real-time based on non-invasive data that is collected passively in the background.

Thirdly AI could be the "nutritionist in your pocket" that could provide advice, give recommendation, coach and support when needed for example during cooking, shopping, after a workout or social outings.

Finally, AI can change the future by ensureing that new scientific data is generated by population groupd historically excluded from clinical trials.


Conclusion

We are standing at the door of history, a place that our children will read about in the history books and name this year as the year of AI. Despite the fact that the integration of AI is at its infancy, itt is already enough for regulators to be concerned about the potential risk of widening inequality. AI has the potential to imporve access and affordability but not without human oversight. We should be cautiously optimistic whilst continuously ensure equity and accuracy of personalized nutrition solutions that can benefit all.