Ideas. digital. shared.

AI in healthcare is not one category of technology. It spans a wide range of solutions designed for different users, serving different purposes, and creating value in very different parts of the healthcare ecosystem. To make sense of this landscape, I’d like to walk through a structured way of looking at it. Rather than classifying AI by technical architecture or algorithm type, I find it more practical to group solutions based on who primarily uses them and where they create

When the conversation turns to artificial intelligence in healthcare, one of the most frequent concerns from clinicians and hospital leaders is the potential impact on revenue. The fear is that automation, optimization, and predictive insights could somehow replace billable work or lead to decreased patient volume. In reality, the evidence and emerging implementations tell a very different story: AI is not a revenue threat — when used responsibly and as part of a broader conn

Today, AI in is one of the most discussed—and debated—topics in healthcare. Many organizations are exploring its potential, many implementations are already in place, yet, skepticism remains strong – particularly among clinicians. This is not only understandable, but also necessary. 🫱🏻🫲🏻Advisor, Not Decision-Maker Medicine is built on responsibility, accountability, and trust. Any technology that influences clinical decisions must be held to the highest standards of relia

Hospitals around the world are investing heavily in digital platforms, electronic medical records, analytics tools, and connected devices. These initiatives are often described as evidence of digital transformation. But there’s an important distinction that is frequently overlooked: digitization does not automatically mean digital maturity. Digitization is the act of converting processes into digital form—replacing paper charts with electronic records, manual workflows with s