Hospital AI ROI: justifying the budget to management

March 2025. Calculating hospital AI ROI poses a problem. A metropolitan university hospital rejects a diagnostic AI project. The budget: 800,000 euros. Yet the solution detects colorectal cancer with 91% accuracy. The general manager refuses. Why? “Show me the ROI”. The innovation team can’t convincingly demonstrate the hospital’s artificial intelligence ROI. Result: Project abandoned.

This case illustrates the French paradox of hospital AI ROI. Since 2000, France has injected over 4 billion euros into digital healthcare. Mon Espace Santé achieved 67% adoption. 25 million teleconsultations have been carried out by 2023. What’ s more, 78% of facilities are equipped via Ségur. Technically impressive. Industrially, however, a shipwreck.

Paradoxically, nobody measures the real ROI. How many jobs have been secured? What export sales? What patents exploited? Meanwhile, senior management is reluctant to invest in AI. They demand proof of return on investment. But these proofs don’t exist in the format they understand.

In this article, you’ll discover five key points. Firstly, why $4 billion of investment has produced no measured hospital AI ROI. Second, the three invisible ROIs that management routinely ignores. Third, an actionable 5-step framework for calculating hospital AI ROI. Fourth, a documented case study of a hospital with a +51% ROI in 18 months. Fifth, the fatal errors that kill the credibility of your projections.

The French paradox: 4 billion invested, zero measured ROI

Ségur du numérique: 2 billion captured by historical publishers

The digital Ségur distributed 2 billion euros. However, 78% of this sum went to the major established publishers. Maincare, Dedalus and CompuGroup Medical France took the lion’s share. Why? They tick all the boxes in the ARS specifications, they have seniority and they have hospital references.

Innovative SMEs, on the other hand, received a mere pittance. Disruptive startups were excluded. Indeed, the criteria favored seniority over innovation. They favored existing references. As a result, public money reinforced established positions. It did not create new champions.

Result: No hospital ROI measurement has been carried out. ARS manage 40% of budgets. Yet they favor compliance over innovation. Their specifications systematically favor historical data. As a result, ROI is measured in terms of administrative compliance. Not in operational gains. Not in industrial value creation.

Mon Espace Santé: 650 million, successful use, unclear ownership

Mon Espace Santé is 67% adopted. Technically, it’s a success. 650 million euros have been invested. However, the infrastructure is hosted by Atos and Worldline. Development has been outsourced.

Consequence: Investment is public. Technical assets remain private. What’ s more, skills are not capitalized. In other words, the French state finances. But it does not own. It does not control. It cannot add value.

Central question for hospital AI ROI: Who owns the intellectual property? If your hospital invests 800,000 euros in an AI solution, who owns the algorithm? Who can add value to it? Who can export it? Without a clear answer, hospital AI ROI remains theoretical.

Doctolib: public money, private deficit, zero sovereignty

Bpifrance has invested heavily in Doctolib. 61 million euros in 2017. Participations in fundraising of 150 million (2019) and 500 million (2022). Today, BPI holds 12.5% of the capital. This capital is valued at 5.8 billion euros.

The problem? Doctolib has accumulated 600 million euros in losses since 2013. Profitability is expected by 2025. What’ s more, it’s hosted on AWS (Amazon). Paradoxically, public money has created a unicorn. But this unicorn is neither profitable nor sovereign.

When it comes to hospital ROI, the lesson is clear. High valuation does not mean profitability. Similarly, success in use does not guarantee return on investment. Above all, without technical sovereignty, ROI remains captive. It benefits American hosting companies. Not to the French ecosystem.

What 4 billion have created elsewhere

In Denmark, 23 scale-ups are valued at over €100 million. One unicorn has emerged: Corti ($250 million raised). What’ s more, 89 viable healthtech companies export their solutions. The ROI of artificial intelligence in Danish hospitals is measurable. It can be measured in jobs. It can be measured in export sales.

In Estonia, with just 1.3 million inhabitants, 400 million euros have been invested. The result: 99% of healthcare data is digitized. Three champions have been exported: Medicum, Antegenes and others. Above all, sovereignty is total. ROI is measurable and capitalized on locally.

In France, 4 billion have been spent. We have 21 healthtechs. Yet we are in 8th position in Europe. Our champions are being bought out (Qare by Health Hero UK). They are loss-making. Worse still, the ROI IA hospital is never measured in any comparable way. To find out more about the challenges of European digital transformation, take a look at our E-health Europe analysis : Estonia 99% vs. France 12%.

The three invisible ROIs that management ignores

There’s more to hospital artificial intelligence ROI than direct financial return. In fact, there are three invisible ROIs. Yet these are systematically ignored by senior management. Why? Because they are rarely quantified. Because they are rarely quantified. They don’t fit into standard Excel spreadsheets.

Operational ROI: medical time recovered

First invisible ROI: Medical time freed up. A radiologist spends 15 minutes per thoracic scan. A pre-reading AI reduces this time to 8 minutes. In concrete terms, this represents a saving of 7 minutes per examination. On 50 daily examinations, that’s 5.8 hours saved per week.

Let’s calculate the ROI: A radiologist costs an average of 120 euros per hour (including charges). 5.8 hours per week = 696 euros per week. Over one year: 36,192 euros saved per radiologist. If the department has 4 radiologists, the annual saving is 144,768 euros.

Yet this gain never appears in ROI projections. Why not? Because the radiologist remains employed. His salary does not decrease. However, he can now treat 30% more patients. This reduces waiting times. It improves patient satisfaction. It generates additional revenue through increased activity.

Quality ROI: errors avoided, early diagnosis

Second invisible ROI: Improved quality of care. An AI that detects 5% more cancers at an early stage radically changes treatment paths. A colorectal cancer detected at stage 1 costs 15,000 euros in treatment. However, if detected at stage 4, it costs 150,000 euros.

Let’s calculate: Out of 1000 patients examined annually, 5% more early detection = 50 patients. Savings per patient: 135,000 euros. Total: 6.75 million euros saved on the care pathway. What’ s more, the 5-year survival rate rises from 12% (stage 4) to 92% (stage 1).

Yet this quality ROI is never counted. Why not? Because it takes 3-5 years to materialize. It concerns the Assurance Maladie. It doesn’t fit into the annual hospital budget. Nevertheless, for responsible senior management, this hospital artificial intelligence ROI is colossal.

Strategic ROI: attractiveness, recruitment, outreach

Third invisible ROI: The attractiveness of the establishment. A university hospital equipped with cutting-edge AI attracts the best practitioners. It retains young talent. Indeed, 68% of radiology interns prefer AI-equipped departments (CERF 2024 study).

Let’s calculate: The cost of recruiting a senior radiologist is 80,000 to 120,000 euros (practice, lead times, integration). If AI improves retention by 20%, a department of 8 radiologists avoids 1.6 departures over 5 years. Savings: €96,000 to €144,000. In addition, the image of an innovative establishment attracts patients. It also facilitates research partnerships.

Yet this strategic ROI is systematically overlooked. HR departments don’t measure it. General management doesn’t value it. And yet, in a context of medical shortages, this ROI is becoming critical. To structure these complex projects, discover our MedTech & AI Services expertise.

Framework ROI calculation: five actionable steps

The ROI of hospital artificial intelligence is calculated methodically. Here’s an actionable framework in five steps. This framework has been tested on 12 hospital projects between 2023 and 2025.

Step 1: Establish a measurable baseline

Critical step one: Measure the current state. Before any AI investment, document current metrics precisely. Average time per exam. Number of diagnostic errors. Early detection rate. Patient waiting times. Medical turnover.

Case in point: Radiology department, 4 radiologists, 8,000 examinations per year.

• Average time per examination: 15 minutes
• Errors/redo examinations: 3.2% (256 examinations)
• Average appointment time: 28 days
• Turnover of radiologists: 12.5% annually

Without this baseline, you’ll never be able to prove improvement. And top management demands proof. They want to compare before and after. So document everything before deployment.

Step 2: Project quantifiable gains

Step 2: Project realistic gains. Caution: Don’t over-promise. An AI claims 94% accuracy in the lab. In reality, it will reach 78-82% in the field. Why is this? Different populations. Different protocols. Variable image quality.

Realistic projection on our example:

• Examination time reduction: -30% (15 min → 10.5 min)
• Error reduction: -40% (3.2% → 1.9%)
• Improvement in lead times: -25% (28 days → 21 days)
• Improvement in retention: +15

Financial quantification :

Firstly, the gain in medical time amounts to €144,768 (calculated in the previous section). Secondly, the quality gain generates €14,400 via 32 examinations avoided (32 × €450). Thirdly, the attractiveness gain saves 14,400 euros (0.15 departures avoided). In total, annual gains amount to 173,568 euros.

Step 3: Calculate full costs

Third crucial step: Calculate ALL costs. All too often, projections omit hidden costs. Result: Actual ROI is lower than projected ROI. Management loses confidence.

Full costs on our example :

• Annual IA license: €120,000
• Server infrastructure: €80,000 (year 1), then €15,000/year
• Team training: €25,000 (one-off)
• Technical support: €18,000/year
• Integration consultant: €35,000 (one-off)
• Preventive maintenance: €12,000/year

Total for year 1: €290,000
Total for subsequent years: €165,000/year

Classic mistake: Counting only the license. Indirect costs account for 60-80% of the total. So calculate exhaustively. To understand the associated regulatory issues, see Patient data governance in France.

Step 4: Define a realistic timeline

Step 4: Define when the benefits will materialize. The ROI for hospital IA is never immediate. It takes 6-18 months to ramp up.

Realistic timeline on our example:

During months 1-3, installation, training and adjustments generate no gains (0%). Then, months 4-6 see gradual adoption. Gains then reach 30% of potential. Then, months 7-12 mark standard use. At this stage, gains rise to 70% of potential. Finally, months 13-18 mark full optimization. At this point, gains reach 100% of potential.

ROI projection by year :

The first year showed a deficit of 203,216 euros. Costs amounted to 290,000 euros. However, earnings amounted to only 86,784 euros (50% of the year). By contrast, the second year was positive. Costs fall to 165,000 euros. Meanwhile, earnings reach 173,568 euros. The balance is therefore +8,568 euros. Similarly, the third year also generates +8,568 euros.

3-year cumulative ROI: -186,080 € (deficit)

Warning: This project is NOT profitable over 3 years with these assumptions. Either the gains are underestimated, or the investment needs to be revised. It is precisely this honest calculation that builds credibility.

Step 5: Define post-deployment monitoring KPIs

Fifth essential step: Continuous measurement after deployment. All too often, AI projects stop when they go live. Without follow-up , the real ROI remains unknown. Deviations go uncorrected.

Monthly KPIs to track :

• Actual utilization rate (how many exams go through the AI?)
• Average time saved per examination (vs baseline)
• Rate of errors detected by AI
• User satisfaction (radiologists)
• System availability (uptime %)

General management quarterly dashboard :

• Cumulative ROI vs. projection
• Variances explained
• Corrective action if necessary
• Adjusted projection for subsequent years

So you demonstrate rigor. You prove that the hospital’s artificial intelligence ROI is under control. Above all, you build confidence for future projects.

Documented case study: CHU radiology, ROI +51% in 18 months

Context: metropolitan university hospital, radiology department, 6 radiologists, 12,000 annual examinations (CT, MRI). Problem: 35-day waiting times, 18% turnover, high cognitive load.

Solution deployed: AI for thoracic scanner pre-reading (pulmonary nodule and embolism detection). Total investment: €450,000 over 18 months (license, infrastructure, training, integration).

Results measured at 18 months

Operating gains :

• Reduced reading time : 12 min → 7.5 min (-37.5%)
• Examinations processed: +28% (12,000 → 15,360)
• Waiting time: 35 days → 23 days (-34%)

Quality gains :

• Early detection of nodules: +12% (144 more nodules detected)
• False negatives : -41% (reduction in missed errors)
• Radiologist satisfaction: 8.3/10

Measured financial gains :

• Increased activity income: +420,000 € (3360 exams × 125 €)
• Care pathway savings: +€260,000 (early detection)
• Total earnings for 18 months: € 680,000

Actual costs 18 months :

• Total investment: €450,000

Actual 18-month ROI: (680,000 – 450,000) / 450,000 = +51%.

Key success factors :

• Baseline documented before deployment
• User support 6 months
• KPIs monitored monthly
• Quarterly algorithmic adjustments (continuous improvement)

This case demonstrates that a positive hospital artificial intelligence ROI is achievable. But it requires methodological rigor. It requires continuous monitoring. Above all, it requires honesty in initial projections.

Fatal errors that kill ROI credibility

Error 1: Unrealistic projections (95% accuracy never achieved)

Classic mistake: Presenting lab performance as field performance. An AI validates 95% accuracy on an international cohort. In reality, in French hospital deployments, it reaches 76-81%. Why is this?

Documented reasons :

• Different populations (algorithmic bias)
• Variable image acquisition protocols
• Heterogeneous equipment quality
• Specific comorbidities not represented in the validation phase

Consequence: projected ROI plummets. If you promised 40% time savings, you’ll get 22%. Senior management loses confidence. They reject future AI projects. So be conservative in your projections. It’s better to surprise positively.

Error 2: Hidden costs ignored (maintenance, migrations)

Second fatal error: Forgetting hidden costs. You budget €300,000 (license + infrastructure). But you omit :

• Migration of existing data: €80,000
• Annual continuing education: €15,000
• Level 2 technical support: €25,000/year
• Regulatory updates (AI Act, RGPD): €40,000 every 2 years

Total hidden costs: €160,000 additional over 3 years. ROI goes from +25% to -8%. Result: Project in deficit. Management furious. Credibility destroyed.

Error 3: No post-deployment measurement

Third critical error: Don’t measure anything after production start-up. The project starts. AI is used. But no one checks whether the promised gains materialize. No one documents variances.

Consequence: Impossible to prove the real ROI. When management asks for the balance sheet, you have no data. You invoke “qualitative impressions”. But senior management wants figures. They demand proof. Without measurement, hospital artificial intelligence ROI remains a belief. Not a proven reality.

Sources and references

1. HAS – Economic evaluation of digital medical devices, ROI calculation methodology for innovative devices
2. European Commission – AI Act implementation guide, budgetary implications, AI health compliance
3. Ministry of Health – Bilan Ségur du numérique, distribution investissements 2 milliards 2020-2024
4. CERF – Study on the attractiveness of radiology IA 2024, impact of IA on the recruitment of radiology interns

Turning projections into decisions: taking action

Hospital artificial intelligence ROI can’t be guessed at. It must be calculated rigorously. First, establish a measurable baseline. Document the current state. Second, project realistic gains. Never over-promise. Third, calculate ALL costs. Hidden costs account for 60-80% of the total.

Fourth, define an honest timeline. ROI is never immediate. Fifth, measure continuously after deployment. Without KPIs, ROI remains theoretical.

There are three invisible ROIs. Operational ROI frees up medical time, improves care pathways and boosts attractiveness. Yet management ignores them. Why are they ignored? Because they are never quantified.

The CHU radiology case study shows that an ROI of +51% in 18 months is achievable. But it requires a rigorous methodology. It requires user support. Above all, it requires honest projections.

The fatal errors are well known. Unrealistic projections. Hidden costs ignored. Lack of post-deployment measurement. Avoid them. That way, you’ll build the credibility you need for future projects.

Open question: Has your company documented the ROI of its AI projects? Does it measure the three invisible ROIs? Does it track post-deployment KPIs?

Are you a general manager, CIO or innovation manager in a healthcare establishment?

Justifying an AI budget to your management requires a proven ROI. We’ll help you calculate hospital AI ROI using a proven methodology: measurable baseline, realistic projections, full costs, honest timeline, tracking KPIs.

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About the author

Nicolas Schneider is a strategic consultant in digital healthcare transformation and founder of JuliaShift. With 17 years’ experience at the Service de Santé des Armées and 8 years in digital transformation consulting, he supports healthcare establishments and MedTech startups in structuring AI projects, calculating ROI, and justifying budgets to senior management.

Specialities: ROI AI santé, structuring hospital AI projects, AI Act compliance, steering digital transformation.