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Digi-Bridges, Zürich (2026)

31/07/2026

What people ask a chatbot about health may tell us something about the healthcare system around them, not only about their interest in AI.

A new study in Nature Health analysed 1.7 million health-related conversations with Microsoft Copilot across 109 countries and regions. One of its most interesting findings was that countries with lower confidence in hospitals tended to have a higher proportion of AI conversations about health.

The types of questions also differed. In lower-income countries with younger populations, conversations were more often about broad health information and education. In wealthier countries with older populations, people were more likely to ask about symptoms, care pathways and how to navigate the healthcare system. Countries with more structured health systems also had more questions about medical paperwork.

This is a useful reminder that people do not use health technology in a vacuum. They use it in response to the access, trust, complexity and gaps they experience in their local healthcare environment.

At the same time, the findings need careful interpretation. This was a country-level analysis, so it cannot show that the individuals who distrust hospitals are the same people turning to AI. It covers one platform, whose users may not represent the wider population in each country. It also identifies associations rather than causes. A dataset can be very large and still be affected by who is - and is not - represented in it.

Perhaps the most important question is therefore not whether conversational AI is replacing or supporting healthcare. It may be doing both, depending on the person and the context.

For some people, it could improve health literacy, help them prepare for an appointment or make a confusing system easier to navigate. For others, particularly where trust or access is low, it could become a substitute for professional care—even when the information provided is incomplete or wrong.

That makes context-sensitive design essential. Health chatbots need reliable safety boundaries, culturally and linguistically appropriate information, clear routes to human care and ongoing monitoring of how they are used across different populations.

The study offers an important starting point. The next step is to connect these broad usage patterns with individual experiences and health outcomes, so we can understand where AI is genuinely closing gaps, and where it may quietly be creating new ones.

https://www.nature.com/articles/s44360-026-00174-2

17/07/2026

Google’s launch of a $9.99-per-month AI health coach is another sign that health support is moving beyond simply counting steps or tracking sleep.

The new service promises to combine information about fitness, sleep, nutrition, menstrual cycles, mental well-being, environmental factors and, where users permit it, medical records. In principle, this could help people make better sense of health information that is currently scattered across different apps and devices.

The appeal is understandable. Many of us collect far more health data than we can meaningfully interpret. A conversational coach that connects the dots, adapts to personal circumstances and offers practical guidance could make this information more useful.

But the important question is not only how personalized the advice feels. It is whether that advice is reliable, safe and genuinely helpful over time.

How will the system respond when wearable data are incomplete or misleading? Will it recognise when a person needs professional medical care rather than another lifestyle suggestion? How transparent will it be about why it has reached a particular conclusion? And how carefully will highly sensitive information about health, reproductive cycles, mental well-being and medical history be governed?

There is also a risk that a confident and friendly AI coach may appear more authoritative than the evidence behind its recommendations justifies. Personalization can increase relevance, but it can also increase trust, even when the underlying advice remains uncertain.

The subscription model raises another question. If these tools become an increasingly important route to preventive guidance and health literacy, will their benefits mainly reach people who already have compatible devices, digital confidence and the ability to pay?

This development has real potential. But success should not be measured only through engagement, subscription numbers or how often users return to the app. We also need evidence that the coach improves meaningful health outcomes, supports informed decisions, protects sensitive data and knows where its role should end.

The future of digital health may well include AI coaches. The challenge is ensuring that they become trustworthy companions rather than simply persuasive ones.

https://techcrunch.com/2026/05/07/googles-9-99-per-month-ai-health-coach-launches-may-19/

08/07/2026

A recent survey found that 57% of healthcare professionals had either encountered or used unauthorized AI tools at work. That figure is concerning, but perhaps not entirely surprising.

Healthcare professionals are under constant pressure to work faster, manage growing administrative demands, and make sense of increasingly complex information. When approved tools are unavailable, difficult to access, or do not fit everyday workflows, people may turn to familiar public AI tools to fill the gap.

This does not make “shadow AI” harmless. Using unauthorized tools can create serious risks around patient confidentiality, data security, inaccurate outputs, and unclear accountability, particularly when AI is used to support direct patient care.

However, simply introducing stricter rules or blocking access is unlikely to solve the underlying problem. Unofficial use may also be a signal that staff needs are not being adequately addressed.

One result I found particularly important was that administrators were three times more likely than healthcare providers to be involved in developing AI policies. Policies designed without sufficient clinical input may look strong on paper while remaining disconnected from how work is actually carried out.

The survey itself should also be interpreted cautiously. It included 518 professionals, relied on self-reported behaviour, and was commissioned by a company that provides healthcare AI solutions. It nevertheless raises a useful question: are organisations creating AI policies around their staff, or with them?

Responsible AI adoption requires more than access controls. It requires clear guidance, approved tools that genuinely support real work, practical training, safe opportunities to experiment, and meaningful involvement from the people expected to use these systems.

The goal should not be to eliminate curiosity. It should be to create an environment where curiosity can be explored safely, transparently, and with appropriate safeguards.

https://www.fiercehealthcare.com/digital-health/nearly-fifth-healthcare-professionals-use-unauthorized-ai-tools-work

23/05/2026

Smart pills that can diagnose, monitor, and even treat disease from inside the body sound like science fiction. But research into ingestible electronics is steadily moving this idea closer to clinical reality.

The promise is easy to understand. For people living with gastrointestinal conditions, diagnosis can be slow, invasive, uncomfortable, and expensive. A swallowable capsule that could detect inflammation, monitor gut health, deliver treatment locally, or even collect a small tissue sample could make care more precise and less burdensome.

But as exciting as this is, the article is also a reminder that innovation in healthcare is rarely just about what is technically possible.

For these devices to improve care, they will need to prove not only that they work in controlled settings, but that they are safe, reliable, clinically meaningful, acceptable to patients, trusted by clinicians, and accessible within real healthcare systems. Questions around power, biocompatibility, data quality, device retention, regulatory approval, reimbursement, and responsibility for interpreting the data are not small details. They will shape whether these technologies become useful tools or remain impressive prototypes.

There is also a broader point here. The future of digital and AI-enabled health will not only be software-based. Some of the most interesting developments may come from the combination of sensors, microelectronics, materials science, robotics, and medicine. These systems could generate new kinds of health data from places we previously could not easily access.

That is both exciting and challenging.

The real test will be whether these technologies are developed around clear clinical needs, meaningful patient benefit, and careful evaluation rather than technological possibility alone. A pill that can “sense and act” inside the body is a remarkable achievement. Making sure it acts in ways that genuinely improve care is the harder, and more important, task.

https://spectrum.ieee.org/ingestible-electronics

21/05/2026

The more AI becomes part of everyday health information seeking, the more we need to be honest about both its promise and its limits.

A recent article in The Guardian discusses a study in Nature Medicine evaluating ChatGPT Health in simulated medical scenarios. The findings are concerning: in many cases where urgent care was needed, the system reportedly failed to recommend immediate medical attention. It also appeared inconsistent in recognising suicidal ideation when additional contextual information, such as normal lab results, was included.

This does not mean AI has no place in health. Quite the opposite. AI tools can help people prepare for consultations, understand medical terminology, organise questions, and access information more easily. For many people, especially those who feel dismissed, overwhelmed, or unsure where to start, this can be genuinely valuable.

But there is a major difference between supporting understanding and making triage decisions.

The risk is not only that AI may give wrong advice. It is that it may give advice with confidence, in moments when people are anxious, vulnerable, or looking for reassurance. A false sense of security can be dangerous, especially when symptoms require urgent medical assessment.

This is why independent evaluation, clear safety standards, transparency, and strong guardrails are not optional extras. They are essential if AI is going to be used responsibly in health contexts.

The discussion should not be reduced to “AI is good” or “AI is dangerous”. The more useful question is: what should AI be allowed to do, under what conditions, with what evidence, and with what safeguards?

For now, I think the key message remains: AI can support health literacy, but it should not replace clinical judgement, emergency care pathways, or human support in moments of crisis.

https://www.theguardian.com/technology/2026/feb/26/chatgpt-health-fails-recognise-medical-emergencies

15/05/2026

Google’s announcement that Fitbit will soon allow users to store and share medical records is an important signal of where consumer health technology is heading.

On the positive side, this could make health information more accessible. Many patients still struggle to find, understand, and share their own medical records. Bringing lab results, wearable data, glucose data, and medical history into one place could help people prepare better questions for their clinicians and engage more actively in their care.

But it also raises important questions.

Medical records are not just another data stream. They are sensitive, complex, and often difficult to interpret without clinical context. AI-generated explanations or coaching may be helpful, but they also need to be safe, transparent, and clear about their limits. A reassuring summary that misses uncertainty, or a confident answer based on incomplete records, could create real risks.

There is also the issue of trust. “User control” over data sounds good, but meaningful control requires more than settings and consent screens. People need to understand what data is being shared, with whom, for what purpose, and what happens if they later change their mind.

For me, the most interesting question is not whether big tech can technically integrate medical records into consumer apps. It probably can.

The harder question is whether we can build the governance, safeguards, evidence, and accountability mechanisms needed to make this genuinely useful for patients and clinicians, rather than just more health data moving through another platform.

This is a promising development, but one that deserves careful evaluation. Convenience alone should not be the benchmark for success in digital health.

Link to article: https://www.mobihealthnews.com/news/google-unveils-medical-records-integration-fitbit-app-check-event

25/02/2026

2025 was a defining year for digital health. Reading this piece, I am struck by how clearly 2026 will test whether data driven health businesses can balance innovation and regulation.

On one hand, hyper personalisation is no longer aspirational. AI driven decision support is moving tailored care into the mainstream. The direction of travel is obvious. Patients increasingly expect proactive, personalised and seamless digital services.

On the other hand, the regulatory environment is crystallising fast. The European Health Data Space Regulation, the EU AI Act, updated ICO guidance, and the UK Data Use and Access Act 2025 all signal the same message. AI in health is welcome, but only with robust governance, interoperability standards, clear lawful bases, and demonstrable operational readiness.

What I find particularly interesting is the growing clarity around pseudonymisation and secondary use. The relative approach confirmed by the CJEU, alongside updated UK guidance, opens meaningful opportunities for research and AI training. But it also raises the bar on governance. Claims that data is no longer personal must be backed by rigorous risk assessment and technical safeguards.

The tension is clear. Data is the engine of hyper personalised care and scalable AI. Yet it is also the primary source of legal, ethical and reputational risk. Competitive advantage in 2026 will not come from AI capability alone. It will come from embedding compliance, transparency and interoperability into product design from day one.

For digital health leaders, the question is no longer whether to invest in AI and data infrastructure. It is whether their governance models are mature enough to sustain trust at scale.

https://www.osborneclarke.com/insights/data-driven-digital-health-businesses-challenged-balancing-ai-advances-and-tighter

19/02/2026

This new report from Graphite Digital should make pharma pause.

65% of clinicians say they have reduced or stopped engaging with pharma companies because of poor digital experiences. More than a third say digital interactions now shape their perception of a company as much as, or more than, in-person meetings.
That is a big shift.

The message from 225 senior clinicians across the UK, US and Germany is surprisingly clear and consistent. The problem is not digital per se. It is irrelevance, overload and friction.

52% say they receive too many messages. The same proportion find communications overly promotional and not useful enough. 58% feel most digital content is repetitive or irrelevant. Many would rather search for information themselves when they need it.
Three themes stand out: relevance, utility and experience.

Clinicians want practical, evidence-based content that supports real clinical tasks. They value tools for exploring treatment options, professional development resources, patient education materials and adherence support. They want concise summaries with the option to go deeper. And they expect fast, simple access with no unnecessary barriers or technical glitches.

This is more than a UX issue. It is a trust issue.

If digital becomes synonymous with hard sells and noise, it undermines credibility. If it becomes a frictionless source of high-quality, objective and clinically relevant information, it can strengthen relationships.

In a world where clinicians are time poor and digitally fluent, pharma digital strategy can no longer be volume driven. It needs to be value driven.

Less push. More pull. Less promotion. More evidence.

https://www.healthtechdigital.com/poor-digital-experiences-drive-65-of-clinicians-away-from-pharma-companies/

17/02/2026

A new study from the University of Oxford raises an important red flag about using AI chatbots for medical advice.

In a simulated experiment, people who used AI to interpret symptoms such as severe headache or postnatal exhaustion received a mix of accurate and misleading responses. Many struggled to know what to ask, and small changes in wording led to different answers. When chatbots offered several possible conditions, users were left guessing which one applied to them and whether they should see a GP or go to A&E.

This matters. More than a third of UK residents reportedly use AI for mental health or wellbeing support. The appeal is obvious: instant access, no waiting room, no judgment. But this study highlights a key challenge. AI may provide information, yet that does not automatically translate into actionable, safe advice.

There is also a deeper issue. AI systems are trained on existing medical data, which means they can reproduce long-standing biases and gaps in care. And while clinicians are not perfect either, they operate within professional, ethical and legal frameworks that chatbots currently do not.

Health-dedicated AI tools are emerging, and they may perform better than general chatbots. But improvement alone is not enough. We need robust evaluation, regulatory guardrails and clear communication about what these systems can and cannot do.

AI can support health literacy. It should not quietly replace clinical judgment.

https://www.bbc.com/news/articles/c3093gjy2ero

03/02/2026

AI in cardiology is no longer a future promise, it is already shaping daily practice. What stands out in this piece is the emphasis on responsibility alongside innovation.

The European Society of Cardiology makes a clear case that better algorithms alone are not enough. Safe and meaningful AI in cardiology depends on data quality, shared evaluation frameworks, and close collaboration between clinicians, researchers, regulators, and policymakers. Without this, even the most advanced tools risk being unreliable or misaligned with real clinical needs.

I appreciate the strong focus on governance. Europe’s regulatory momentum, from AI regulation to the European Health Data Space, creates opportunity, but also leaves open questions around liability, validation, and accountability. Scientific societies stepping in to bridge evidence, practice, and policy feels not just helpful, but necessary.

If AI is to really empower clinicians and improve cardiovascular outcomes, it must be built on high-quality data, transparent methods, and continuous dialogue across disciplines. Innovation is moving fast, but trust and safety still need careful, collective work.

https://www.escardio.org/public-health/priorities/responsible-ai--digital-health-new/

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