The race to OS but still lagging on impact

We are already 6 weeks into the new year, and it is clear the direction that we are already travelling in. AI in healthcare is moving at an incredible speed growing at around 35-40% CAGR according to Marketsandmarkets. This growth can be attributed to the increased adoption of AI and many of the tech players out-doing each other on a weekly basis.

In case you have missed the dizzying OS activity in healthcare last month, here is a quick summary:

OpenAI launched a health-focused version of ChatGPT which can integrate medical records, labs and wellness data.

Anthropic rolled out Claude for Healthcare embedding AI into clinical workflows, payer systems and life sciences

Amazon - introduced Health AI inside its One Medical platform using patient data to support Dr’s in primary care.

And this is just the tip of the iceberg. Similar moves are emerging across imaging, diagnostics, remote monitoring, nutrition, and mental health.

What does this all mean?

Tired of complex dashboards and fragmented apps, Confused by conflicting advice and overwhelmed by the pressure to be “always on” in tracking, measuring and optimising.

At the same time, there is a growing thirst for simplicity and personalised, meaningful guidance based on each individual’s data across the continuum from wellness to healthcare.

This is where AI has enormous potential:

• To connect siloed data sources across devices, labs, medical records and lifestyle apps

• To surface patterns and risks earlier

• To tailor recommendations to an individual’s biology, behaviour and context

However, insight alone is not enough. The real value is unlocked only when insight leads to consistent, sustainable behaviour change.

What is still missing in OS solutions?

AI will not lace up your running shoes, cook your dinner or test that new recipe.

This is where many tech solutions still miss the mark. The gap is no longer just about data or algorithms, but about translating insight into human‑centered action.

In our view, the human layer is indispensable:

• Healthcare professionals and nutrition experts help people interpret their data, set realistic goals and make trade‑offs that fit their cultural, social and financial context.

• Coaching and support provide accountability, encouragement and course‑correction over time.

• Trust and relationship make the difference between a recommendation that is ignored and one that becomes a new habit.

While the evidence is clear on the benefits of nutrition, movement, sleep and stress management, AKA the boring stuff we know works, we still lack:

- Seamless integration of behaviour‑change science into AI‑driven tools

- Robust, long‑term outcomes data beyond short‑term engagement metrics

- Funding and reimbursement models that reward prevention and lifestyle support, not just procedures and prescriptions

Where are we heading next?

Looking ahead, we expect to see a shift from stand‑alone AI tools to integrated, human‑in‑the‑loop ecosystems that combine:

High‑quality multimodal data (clinical, lifestyle, nutrition, environment)

Advanced models that can handle nuance, uncertainty and personalisation

Human expertise that ensures recommendations are safe, ethical and practical

Business models that reward prevention and value creation across Food and Health.

For nutrition and metabolic health, this means moving from generic advice to:

• More precise understanding of individual needs, risks and responses

• Nutrition and lifestyle plans that adapt over time as data accumulates

• Interventions that are rooted in real‑world context food environments, budgets, culture and personal preferences

What does this mean for companies in Food, Ingredients, Health & Tech?

For organisations across the metabolic health and nutrition ecosystem, the question is no longer if AI will reshape your category, but how you will respond.

Key questions industry should consider:

For food & retail: How will you use data and AI to create healthier defaults, personalised offerings and clearer guidance at the shelf without overwhelming consumers?

For ingredient companies: How will you build evidence around your ingredients in specific populations, and link that evidence to digital tools and services that demonstrate real‑world impact?

For health‑tech and digital therapeutics: How will you ensure your AI‑driven insights translate into behaviour change, not just more notifications and how will you bring qualified nutrition and health professionals into the loop?

For payers, insurers and employers: How will you evaluate and reimburse solutions that combine AI with nutrition and lifestyle support, and measure ROI beyond short‑term utilisation?

For wellness and consumer brands: How will you navigate the line between empowerment and over‑promising, maintaining trust while leveraging increasingly powerful AI?

Those who can combine credible science, robust data, thoughtful design and a strong human layer will be best positioned to create value and build durable trust.

What is Qina doing differently to evolve with the current Food Health trends?

At Qina, we realize that in this new era it is not business-as-usual any more. We are shifting from a purely B2B approach and stepping up to create a digital ecosystem that combines real-time data, domain expertise and telehealth. In this new ecosystem, our goal is to connect companies and consumers in a way where the value is shared and generates societal impact. With core values that include: accessibility, affordability and longitudinal,quality data, we are creating the future of food.

Interested to learn more? visit us at https://qina.tech