A New Approach to Health Checks
Swedish startup Neko Health, co-founded by Spotify CEO Daniel Ek, is introducing an innovative health check service. In just a few minutes, Neko’s system scans millions of data points from the body, which are then analysed by Artificial Intelligence (AI). The results are discussed with a physician, offering insights into the user’s health for approximately €240 (2,750 SEK). However, the service is currently only available in Stockholm and London, with a long waiting list.
Expansion and Funding
Neko Health recently secured $260 million in funding, bringing its valuation to $1.8 billion, according to TechCrunch. The company plans to expand to the U.S., where demand for preventive healthcare is particularly high, given the costly healthcare system.
This is not Daniel Ek’s first investment in tech-driven solutions; he has also funded Helsing, a Munich-based startup using AI for military applications.
What Does Neko Health Offer?
Neko Health offers a comprehensive health check that includes examining skin abnormalities, blood pressure, blood sugar levels, cholesterol, and other metrics, such as potential indicators of heart arrhythmias. While these are standard in routine doctor visits, Neko claims its scans provide deeper insights by leveraging additional data points and proprietary technology.
On its website, Neko compares its service to routine car maintenance, advocating for annual scans to preempt health issues. Users can even gamify their health data by comparing metrics like blood sugar levels to others in their age group.
How Does It Work?
Neko’s scanning technology is entirely proprietary and does not use conventional methods like MRIs. The process includes traditional elements such as blood sampling, and follow-up procedures like long-term ECGs may be recommended for certain findings. While AI plays a key role in analysing the data, a physician always oversees the process to ensure accuracy and reliability.
Neko Health emphasises that user data is not sold, but it is utilised internally. Specific details about the systems or algorithms employed remain undisclosed.