ROI from AI in healthcare vol. 2

Numbers matter, but Excel alone will not show the full value of innovation

Assessing artificial intelligence in medicine forces us to redefine the classic understanding of Return Of Investment. Innovative projects in healthcare are not driven by financial profit or the generation of savings. Although these are important elements in every system and sector, they will never be the priority. Medicine follows its own rules.

In my previous article on Medium

[embed]AI in healthcare is not just about the algorithm. How should we calculate ROI? Measuring value in implementation projectsmedium.com

I wrote about this to discuss how should we actually calculate return on investment (ROI) if traditional financial models fail when confronted with a technology as complex as AI in medicine?

Today, in conversations about artificial intelligence in healthcare, the question of ROI appears even more often. And sometimes, however, we need to look at it through a financial lens. A fresh perspective and hard data for this discussion are provided by the latest Philips Future Health Index 2026 report.

https://www.philips.com/c-dam/corporate/newscenter/global/pdf/philips-future-health-index-2026-report-ai-in-practice.pdf

The study shows that:

• 34 percent of healthcare leaders declare real budget savings resulting from the implementation of AI.
• 62 percent believe that the benefits of this investment meet or even exceed its costs.
• At the same time, only 8 percent of leaders admit that they have not invested and do not plan to invest in solutions based on artificial intelligence.

These numbers clearly show that AI is beginning to be assessed

pragmatically as an investment that must have deep organizational sense. At the same time, a key risk appears here, one I have warned about before we cannot reduce the entire discussion about ROI to a simple accounting question how much money exactly was saved, net, immediately after purchasing the tool? In the healthcare sector, such a simplified calculation is simply too flat.

Therefore, continuing the thread from the previous article, ROI from AI in healthcare is worth calculating and should be calculated, in layers. A true assessment should include four dimensions:

• Financial layer Purchase, maintenance, IT integration and staff training costs compared with real, direct savings.
• Process layer Shorter procedure times, fewer administrative tasks, automation of repetitive tasks and smoother information flow.
• Clinical layer Support for clinical decision-making, faster identification of risks, higher patient safety and repeatable quality of interpreting results.
• Organizational layer Team readiness and well-being, acceptance of the tool by real users (no resistance to technology), as well as the ease of scaling and maintaining effects after the pilot phase ends.

In practice, it is precisely these deeper layers that determine whether a technology will stay in an organization for longer or whether it will be rejected. A tool may look phenomenal in a presentation and promise gigantic savings and still fail completely (I have unfortunately experienced this in my own projects…). To sum up- in order to reliably assess the sense of implementing AI, we need to start asking very specific questions about everyday practice. If we want to calculate ROI from AI in medicine honestly and wisely, a spreadsheet alone will never be enough.