The gaming industry ceased to be something unserious or just for children long ago. It is a huge market with pharma-level budgets, top-tier development teams, advanced R&D units, and extremely fine-tuned work with human attention, motivation, and behavior. It is only logical that medicine is looking more and more in this direction – if games can keep people engaged for hours, why not use the same mechanics when a patient needs help getting through treatment, rehabilitation, or complex learning?
Author: Dzmitry Korsak
Dzmitry Korsak
Experienced journalist and editor with over 25 years in the field. His work focuses on medical technologies, social issues, and innovation. He values an evidence-based approach, thorough work with primary sources, and the ability to communicate complex topics in a clear and accessible way.
As the year draws to a close, major analytics firms traditionally share their view on where the medtech industry is heading and what to expect in the near future. We have gone through these reports and distilled the essentials: from the explosive growth of AI in medical devices and the portable tech market to regional regulatory specifics and shifting investment priorities. This article brings together the key figures, insights, and directions that will shape medtech over the coming years.
Another unusual “side effect” has been observed with semaglutide — the drug better known as Ozempic and Wegovy. Originally developed for type 2 diabetes, it proved effective for weight reduction, and now appears to benefit treatment of certain cancers. A University of California San Diego study reports that among people with colon cancer who were concurrently taking semaglutide, five-year mortality was 15.5%, whereas among those not taking it the figure was 37.1% — more than twice as high.
AI now shows up in everyday mental-health chats—from sleep tips to suicidal disclosures—driven by poor access, stigma, and the lure of anonymous, free help. Yet LLMs are unreliable: they err, lose context, miss non-verbal cues, and can reinforce distortions. Crisis-performance evidence is thin, and the red lines remain contested.
Diagnosis of respiratory diseases through lung sounds remains one of the most complex clinical tasks. Even experienced physicians admit that auscultation results are often subjective: one specialist hears a pathology, while another does not. Yet these judgments influence critical decisions – whether to hospitalize a patient, prescribe antibiotics, or assess the severity of their condition.
The digital health market is expanding fast – IQVIA’s Digital Health Trends 2024 estimates there are roughly 337,000 health-related apps today. Behind that impressive number lies a worrying statistics: around 90% of startups never reach a market release, and of those that do, roughly 20% shut down within the first year. For developers and investors, this is a landscape full of hidden traps, where success depends not only on the strength of the idea but also on the ability to navigate dense regulatory filters.
Innovent Biologics announced that its GLP-1 drug, Mazdutide (6 mg), achieved better results than Novo Nordisk’s Semaglutide (1 mg) in a study involving patients with type 2 diabetes and obesity. In the DREAMS-3 trial, Mazdutide reduced HbA1C by an average of 2.03% compared to 1.84% for Semaglutide, and patients lost 10.29% of body weight versus 6% with Ozempic.
In mid-October, the World Health Organization released a report on digital health and the role of technology in national systems. It warns that without proper coordination and government support, digital solutions may unintentionally deepen inequality — a reminder that innovation’s impact depends on the context in which it’s applied.
In recent years, more and more mental-health care startups have pinned their hopes on AI, promising “therapy without the therapist.” Yet thoughtless automation often backfires: clients receive shallow support, and in crises, AI therapy apps simply don’t cope. Algorithmic errors erode trust not only in digital therapy but in AI itself.
magine that there is a model that predicts an individual’s risk of thousands of diseases based solely on their medical history. What product would you like to create based on this model? This is not a hypothetical question – such a model already exists. It is described in a recent article in Nature, is publicly available, and is waiting for a team that can put it into practice.