Few weeks ago I wrote about how using a single AI model for diagnostics is fundamentally biased: It is a well established fact that a single-person view is inherently subjective. We all have our blind spots, no matter how much of an expert somebody might be. The solution is
Starting with some “old” news, in March BfArM started reimbursing Class IIb DiGA apps, from reuschlaw: Prior to the Digital Act, DiGAs had been limited to the lower risk classes (I and IIa). This has now also been extended to the higher risk class IIb within the meaning of Art.
This is a continuation of Part 1 and Part 2. All that being said, there is a really good reason why hospitals and other care institutions should be thinkering with AI in-house, even if they shouldn’t necessarily be using it in production, and that has everything to do
You can read Part 1 here. When considering the cases in which AI could run on-premise, we need to understand the clinical workflow from the perspective of computing intensity, speed requirements and collaboration needs. Here is an easy example: An MRI or CT scan requires a massive computing power
Before AI came to dominate the picture, healthcare software was running almost exclusively on premise - on the hospitals’ own local servers. This was working, because the software was deterministic, meaning it followed clear rules. If this, then that; 2+2 equals 4. It’s basically a simple database lookup. The
In January Microsoft published an annual review about their commitment to Europe (translated): Europe is accelerating the use of artificial intelligence and increasingly recognizing its potential to sustainably increase productivity, strengthen competitiveness, and modernize the public sector. At the same time, organizations across Europe are focusing on digital sovereignty and
Germany is the largest EU economy and that is true for the healthcare market as well, where we spend the most among all the member states - around 12%, compared to the 10% EU average. Healthcare regulation in Germany is especially complex, as there are both public and private insurance paths,
On Monday I explained how we are headed towards a world, where clinical work increasingly happens within a single agentic interface, which connects to and interacts with all the other systems and apps. On Tuesday I concluded that despite this unprecedented aggregation of both clinical data and intelligence, AI agents
Yesterday I discussed how AI agents moved from the the back end of the stack to the front end, increasingly becoming the main interface to the healthcare machinery. Today I want to zoom in on how data actually flows through the system. Let’s start with the origins of the
Scott Galloway from Prof G Media wrote about the Oura ring (emphasis mine): Similar to the Apple Watch, the Oura Ring tracks 50+ health and fitness metrics. Unlike the watch, which a wearer often removes at night for charging, the ring goes more than a week on a single charge
“Build it and they will come” is a classic tech advice by Paul Graham, founder of Y-Combinator - Silicon Valley’s most prominent startup accelerator. It defined the last two decades of software development as people could increasingly freely source the solutions they needed: The exciting thing about the Internet
The idea of value-based care is simple: if the purpose of healthcare is to restore our health, then this is how we should measure it and based on the impact is how we should pay for the procedures and services we get. The complexity comes from its implementation. There
