An AI business case: $4.51 return for each dollar invested


Is AI worth it? It is a returning question in radiology. In this study, the authors aimed to simulate the economic return of investment over a 5-year period, when implementing an AI platform (Calantic, by Bayer) with its full AI solution portfolio (14 solutions) in a US hospital.

Financial and non-financial inputs like reading time, triaging time, clinical outcomes, and installation costs, were considered in the ROI calculator.

The total costs for the platform, AI applications and overhead costs were predicted to be $1,780,480 for the 5 years. That is a lot of money, but the estimated revenues generated from the platform applications were $3,560,959! That's 4.5 times as much. The authors state: "the largest economic benefit of implementing radiology AI applications in a hospital comes from downstream effects of diagnoses made".

There was an exception where the ROI was negative. This was the case of implementing Calantic into a diagnostic center, where the calculated ROI was $0.41 for every dollar invested.

Some important notes to consider: the ROI will depend a lot on how your healthcare system is organized, thus results may be different in different countries. Also, the study was co-written and funded by the AI platform vendor Bayer.

Nonetheless, this is the first study so far that has tried to make a comprehensive calculation of the costs and benefits of AI at large. We are looking forward to seeing the calculations tested in real-life!

Read full paper

Unlocking the Value: Quantifying the Return on Investment of Hospital Artificial Intelligence

Journal of the American College of Radiology, 2024


Purpose: A comprehensive return on investment (ROI) calculator was developed to evaluate the monetary and nonmonetary benefits of an artificial intelligence (AI)–powered radiology diagnostic imaging platform to inform decision makers interested in adopting AI.

Methods: A calculator was constructed to calculate comparative costs, estimated revenues, and quantify the clinical value of using an AI platform compared with no use of AI in radiology workflows of a US hospital over a 5-year time horizon. Parameters were determined on the basis of expert interviews and a literature review. Scenario and deterministic sensitivity analyses were conducted to evaluate calculator drivers.

Results: In the calculator, the introduction of an AI platform into the hospital radiology workflow resulted in labor time reductions and delivery of an ROI of 451% over a 5-year period. The ROI was increased to 791% when radiologist time savings were considered. Time savings for radiologists included more than 15 8-hour working days of waiting time, 78 days in triage time, 10 days in reading time, and 41 days in reporting time. Using the platform also provided revenue benefits for the hospital in bringing in patients for clinically beneficial follow-up scans, hospitalizations, and treatment procedures. Results were sensitive to the time horizon, health center setting, and number of scans performed. Among those, the most influential outcome was the number of additional necessary treatments performed because of AI identification of patients.

Conclusions: The authors demonstrate a substantial 5-year ROI of implementing an AI platform in a stroke management–accredited hospital. The ROI calculator may be useful for decision makers evaluating AI-powered radiology platforms.