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Data knows everything about us – except how we feel.
Most health apps measure, track, and remind, but they don’t understand. They collect steps and heartbeats, yet miss the subtle dynamics that make us human: how weather, air quality, or a restless night reshape our body’s rhythm. The more data they gather, the more impersonal the experience becomes. That paradox became the starting point of our project, BioFitSyn – a platform designed to make health technology not only intelligent, but aware.

Our central insight was clear: health is not a static metric but an evolving conversation between body and environment. Every heartbeat tells a story influenced by countless variables – the air you breathe, the city you live in, the stress you carry. Yet most digital health tools operate in isolation, detached from the very world users inhabit. BioFitSyn was built to change that.

Powered by Generative AI, BioFitSyn merges wearable data, behavioral patterns, and environmental inputs to simulate how external factors affect wellbeing in real time. Through a 3D digital twin, users can visualize their body’s responses and receive adaptive guidance – not just “what happened,” but “what might happen if.” It’s the difference between being tracked and being understood. The app doesn’t merely inform; it advises, translating complex analytics into natural, empathetic insights that feel human.

This concept redefines how health technology creates value. Instead of competing in the crowded field of retrospective tracking, BioFitSyn opens a new market space where personalization meets prevention;  an application of Blue Ocean Strategy that moves beyond the limits of traditional fitness apps. Its hybrid B2C–B2B model bridges individuals and institutions: users receive tailored recommendations, while insurers and wellness providers access predictive, privacy-safe analytics that support preventive care. Financially, the platform aims for break-even by Year 3, with 60-65% margins and projected revenues of €24 million by Year 5 – a sustainable balance between innovation and scalability.

Conceptually, BioFitSyn embodies the logic of Dynamic Capabilities Theory (Teece, 2007): systems thrive when they sense, learn, and adapt. Each user interaction refines the model’s accuracy, creating a feedback loop where data continuously improves itself. In essence, the platform learns the way people do – by observing context and adjusting behavior.

What we found through this project is that the real frontier of Generative AI in healthcare isn’t about creating more data, but giving meaning to the data we already have. Context turns numbers into knowledge, and knowledge into action.

BioFitSyn represents that shift – from measurement to understanding, from technology that monitors us to technology that truly supports us.

Team 35

Yong Yu Hu

Lucía Torres

Roald Schild

Raghav Modani

Sources:

Davenport, T. H., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future Healthcare Journal, 6(2), 94–98. https://doi.org/10.7861/futurehosp.6-2-94

Kim, W. C., & Mauborgne, R. (2005). Blue Ocean Strategy: How to create uncontested market space and make the competition irrelevant. Harvard Business School Press. Recuperado de https://www.blueoceanstrategy.com/books/Blue Ocean Strategy+1

Ransbotham, S., Candelon, F., Kiron, D., LaFountain, B., & Khodabandeh, S. (2021). The cultural benefits of artificial intelligence in the enterprise. MIT Sloan Management Review & Boston Consulting Group. Recuperado de https://web-assets.bcg.com/85/90/95939185404cbd901aba0d54f1d7/the-cultural-benefits-of-artificial-intelligence-in-the-enterprise-r.pdf

Teece, D. J. (2007). Explicating dynamic capabilities: The nature and microfoundations of (sustainable) enterprise performance. Strategic Management Journal, 28(13), 1319–1350. https://onlinelibrary.wiley.com/doi/10.1002/smj.640

Topol, E. (2019). Deep medicine: How artificial intelligence can make healthcare human again. Basic Books. https://www.basicbooks.com/titles/eric-topol/deep-medicine/9781541644649/

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Some mornings the blank page no longer feels intimidating but expectant: which part of me will meet the machine today? That question has become its own creative spark, my thoughts moving not only toward expression but toward an intimate exchange with a system that answers back. I often wonder: am I still writing, or are my sentences already a merger of my voice and a vast pre-trained model?

This isn’t just personal musing. In early September, Anthropic agreed to a $1.5 billion settlement after authors accused it of training on their books without permission (Fung, 2025). A few weeks later, a UK government adviser suggested that AI companies should never be required to compensate creators for training data, an idea that ignited fierce debate among artists and lawmakers (Hern, 2025). At the same time, Microsoft announced plans for an AI training marketplace, offering publishers a way to sell licensed content (Sullivan, 2025). A new open standard called Really Simple Licensing (RSL) is also emerging to let creators set explicit terms for AI use.

These headlines echo in my own experiments. I’ve used generative tools to restructure arguments, to test business concepts, even to chase metaphors I might not have found alone. The speed is intoxicating. Yet I feel the quiet tug of dependency: when a model anticipates my metaphors or organizes my thoughts before I’ve sat with them, part of the creative friction the hesitation that once made ideas distinct slips away.

That is the real frontier. With what people often call high abilities, I have always relied on an active, restless creativity. Now I find myself working beside another kind of intelligence; statistical, tireless, endlessly associative. The challenge is not choosing one over the other, but making sure they sharpen rather than dull each other. Staying deliberate deciding when to pause, when to let my own ideas lead, is how these two intelligences can coexist without either one quietly erasing the other.

Fung, B. (2025, September 5). Anthropic agrees to pay $1.5 billion to settle author class action. Reuters. https://www.reuters.com/sustainability/boards-policy-regulation/anthropic-agrees-pay-15-billion-settle-author-class-action-2025-09-05/

Hern, A. (2025, September 24). Adviser to UK minister claimed AI firms would never have to compensate creatives. The Guardian. https://www.theguardian.com/technology/2025/sep/24/adviser-uk-minister-claimed-ai-firms-never-compensate-creatives/

Sullivan, M. (2025, September 12). Microsoft to launch marketplace for licensed AI training data. eWeek. https://www.eweek.com/news/microsoft-ai-marketplace-publishers/

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I almost skipped the article the first time: LVMH deploys AI to reinvent luxury design. At first it sounded like a gimmick. Yet the more I read, the more it felt like a quiet master class in next-generation digital strategy.

Inside its private cloud, built with Google Cloud-LVMH has digitized over a century of couture sketches, fabrics and runway photography (Socha, 2024). A generative model trained solely on that archive now proposes silhouettes and textures in seconds. Unlike public models scraping the open web, this one learns only from proprietary data, so the “creative DNA” remains uniquely LVMH.

Around that core they’ve layered a full stack of emerging tech. Computer-vision algorithms authenticate a handbag faster than a seasoned craftsman, while predictive analytics forecast demand city by city, cutting overstock and waste (LVMH, 2024). And the group is edging into Web 3.0, issuing blockchain-backed digital twins and experimenting with immersive virtual showrooms where customers preview or even own a piece before a single seam exists in the physical world (LVMH, 2024).

What fascinates me is the strategic geometry. Cloud infrastructure and off-the-shelf AI are commodities; the scarcity lies in LVMH’s private data and the blockchain provenance layer. Together they create switching costs and a competitive moat that’s invisible but formidable, a kind of luxury IP that can’t be copied or reverse-engineered.

Still, the move raises subtler questions. If an algorithm trained on a century of design heritage suggests the line of a new Dior jacket, where does authorship reside? Is the creative act in the model’s probabilistic leap, in the artisan’s final cut, or in the decision to fuse both?

Perhaps the future of craftsmanship isn’t about rejecting technology but about curating it with taste, treating algorithms the way past designers treated rare fabrics: as raw material demanding judgment.

Would you value a garment differently knowing its first spark came from a private neural network and its proof of origin will live forever on a blockchain?

LVMH. (2024). Annual report 2023. https://www.lvmh.com/investors

Socha, M. (2024, March 12). LVMH taps AI to create a digital couture archive. Business of Fashion. https://www.businessoffashion.com