AlphaFold AI has profoundly reshaped health data research in a short time: since 2020, DeepMind’s technology has produced protein models with unprecedented accuracy and has made them widely accessible to the community in the spirit of open science—a milestone recognized in 2024 with the Nobel Prize in Chemistry awarded to Demis Hassabis, John Jumper, and David Baker [1–3].
By the end of the decade, AI in 2030 will look less like a single product and more like an invisible utility - embedded across homes, workplaces, and public services. Experts agree that the real AI impact won’t come from isolated breakthroughs, but from countless improvements that strengthen reasoning, reliability, and real-world integration. As a result, productivity will rise, decision-making will be more data-driven, and creative collaboration between humans and machines will be routine.
Artificial intelligence (AI) is reshaping healthcare across diagnostics, prevention, operations, and research, yet the magnitude of impact depends on high‑quality, interoperable data and careful evaluation in clinical settings.
Current evidence underscores both promise and constraints: near‑term gains cluster where data are structured and outcomes can be measured, while broader transformations require robust governance and data standards that support reproducibility and equity. [1], [2]
AI mental health chatbots – also referred to as AI therapy chatbots or mental health chatbot apps – use conversational AI, often drawing on principles of cognitive behavioral therapy (CBT), to provide low-threshold support.
Systematic reviews and randomized controlled trials (RCTs) report significant but moderate improvements in symptoms of anxiety and depression, while expert bodies emphasize the importance of transparency, data protection, crisis protocols, and human oversight [1][2][3][4].
Clinical documentation has become one of the biggest time drains in modern care. Across primary and speciality settings, clinicians spend hours each day turning conversations into structured charts — time that could otherwise go to diagnosis, counselling, and shared decision-making. Against this backdrop, an AI medical scribe — sometimes called an artificial intelligence scribe — has emerged as a practical way to automate note creation and reduce after-hours workload. By converting ambient speech into accurate, structured AI medical notes, these systems help teams focus on what matters most: patient care.
Artificial intelligence is deeply embedded in modern healthcare, enabling diagnostics, treatment recommendations, and patient engagement. But alongside its significant benefits, the challenge of protecting sensitive patient data is also growing. The threat of unintentional data leaks through AI tools poses a threat, requiring new, improved data protection methods. How do we best protect sensitive health data in an age where algorithms learn from vast amounts of personal information?
Artificial intelligence, especially in combination with machine learning, is fundamentally transforming modern medicine in medical diagnostics. The strength of AI lies in its ability to process large amounts of data and thus provide medical information significantly faster.
Scientific innovation has always been a key concern for the pharmaceutical industry, and the integration of artificial intelligence into pharmaceutical research is rapidly expanding the possibilities. At every stage of drug development, AI accelerates the identification of therapeutic targets, the development of new molecules, and the optimization of clinical trials. Through greater accuracy, shorter work processes, and lower costs, AI is transforming the way new drugs are discovered, tested, and delivered.
Treatment plans, lab results, MRI scans—clinics, practices, and research institutions generate enormous volumes of data every day. Big Data refers to these complex datasets and the technologies used to store and analyze them. Its defining characteristics are the three Vs: Volume, Velocity, and Variety — large, rapidly growing, and diverse data (Springer, 2021).
We all want to live healthy and long lives. This is a good reason to support medical research, as it is a decisive factor in the prevention, early detection and successful treatment of diseases. Health data plays a central role in this, as patient data helps to gain insights that in turn enable advances in treatment. In this way, we all help to extend our own lifespan and that of our fellow human beings.