# Realistic ROIROIReturn on Investment: the ratio of net profit to the cost of an investment. A 300% ROI means each dollar invested returns $3.View full definition → timelines and hidden adoption costs
A radiology AI vendor demos a model that flags suspicious lung nodules on CT scans in under two seconds. The hospital signs. Eighteen months later, the tool is running, but the ROIROIReturn on Investment: the ratio of net profit to the cost of an investment. A 300% ROI means each dollar invested returns $3.View full definition → spreadsheet still shows red. Nobody lied in the demo. The two-second inference was real. What the demo omitted was the other seventeen months: data cleanup, PACS integration, radiologist skepticism, and a monitoring bill that never stops.
This lesson is about that gap. In biotech and medtech, AI usually arrives in years, not quarters, and the reasons are structural, not a sign of a bad product.
AI value in this sector is gated by four things that software demos skip: data you can trust, workflows you must rebuild, humans who must believe the output, and models that drift the moment they meet reality.
Each of these is a cost center before it becomes a benefit. Let us take them in order.
A "trained model" in a demo was trained on someone else's clean data. Your data is messier.
Concrete example: a hospital wants to deploy an AI sepsis prediction tool. Sepsis is a life-threatening response to infection where early detection matters. The model needs labeled historical cases. But sepsis onset time in the electronic health record (EHR, the digital patient chart) is inconsistently recorded, coded differently across departments, and sometimes back-filled by clinicians hours later. Before any model runs, someone spends months defining "onset" and reconciling records.
Rule of thumb, widely cited across data science practice and not a hard number: data preparation consumes 60 to 80 percent of an AI project's effort. In regulated healthcare, assume the high end.
The EHR interoperability standard you will hear about is HL7 FHIR (Fast Healthcare Interoperability Resources), a free open specification. FHIR helps, but it does not make your historical labels correct.
An AI output that does not fit the existing workflow gets ignored.
Example: a diabetic retinopathy screening AI (retinopathy is eye damage from diabetes) is FDA-cleared and accurate. But if the clinic's medical assistant has to log into a separate portal, upload images manually, and wait, the tool adds friction. Adoption collapses. The fix is integration engineering: pushing results directly into the existing chart, triggering the right follow-up order automatically.
That redesign is often the largest hidden cost, and it is rarely in the vendor quote.
Clinicians do not adopt AI because it is accurate. They adopt it because they trust it in their hands, on their patients.
Trust-building is concrete work: shadow deployments (the AI runs silently alongside clinicians for months, its outputs compared but not acted on), champion clinicians who vouch for the tool, and clear escalation rules for when the AI is wrong. Budget staff time for all of it.
A tool that "works" technically can still fail if radiologists override every alert. Alert fatigue, where too many low-value alerts cause staff to ignore all of them, is a documented failure mode for clinical decision supportdecision supportTechnologies and processes that turn raw data into actionable insights via reporting, dashboards and analysis, so teams can decide based on facts rather than intuition.View full definition →.
This is the cost vendors most love to omit. Models drift.
Drift means the model's accuracy degrades as reality shifts: a new CT scanner model, a change in patient population, a new lab assay. In 2026, US regulators expect you to plan for this. The FDA (Food and Drug Administration) has a framework for a Predetermined Change Control Plan, letting manufacturers pre-specify how a model will be updated and monitored. In Europe, AI-based medical devices fall under the Medical Device Regulation (MDR), and higher-risk AI systems are also covered by the EU AI Act, which is phasing in obligations through 2026 and 2027.
Monitoring is a recurring operational cost: staff, dashboards, revalidation. Treat it like a subscription you can never cancel.
Let us make the timeline concrete. All figures below are illustrative estimates for teaching, not vendor quotes or market data.
Imagine a mid-size hospital deploying an AI tool that reduces radiologist reporting time on chest X-rays.
Benefit assumption (illustrative):
Cost side, spread over time:
| Cost item | When it hits | Illustrative share |
|---|---|---|
| Software license | Year 1 onward | Ongoing |
| Data curation and validation | Months 1 to 9 | One-time, large |
| Workflow / PACS integration | Months 3 to 12 | One-time, large |
| Clinician training and shadow period | Months 6 to 14 | One-time |
| Ongoing model monitoring | Year 1 onward | Recurring |
The benefit (833 hours) only starts accruing near month 12, once the tool is live and trusted. The heavy costs land in months 1 to 14. So the cumulative cash position stays negative through roughly year 1, sometimes year 2, before the recurring benefit overtakes the recurring cost.
The lesson: the demo sold you the 30 seconds. The 833 hours are real, but they are back-loaded, and the payback line bends upward only after the hidden costs are paid.
Here is a minimal way to model the timing, not just the total:
# Illustrative only. Numbers are teaching estimates, not real quotes.
hours_saved_per_year = 833
value_per_hour = 150 # illustrative loaded cost of radiologist time
annual_benefit = hours_saved_per_year * value_per_hour # ~124,950
# Costs by year (illustrative)
year1_costs = 300000 # curation + integration + license + monitoring
year2_costs = 90000 # license + monitoring only
year3_costs = 90000
cumulative = -year1_costs + 0 # benefit not live until ~month 12
cumulative += annual_benefit - year2_costs # year 2
cumulative += annual_benefit - year3_costs # year 3
print(round(cumulative)) # negative until benefit compounds past setup costsThe point of the snippet is not the exact output. It is that benefit and cost land in different years, so a single-year ROIROIReturn on Investment: the ratio of net profit to the cost of an investment. A 300% ROI means each dollar invested returns $3.View full definition → number is misleading.
🎬 [VIDEO: "The AI hype cycle in healthcare" - youtube.com - a grounded overview of why clinical AI deployments take longer than vendors promise]
When a vendor shows ROIROIReturn on Investment: the ratio of net profit to the cost of an investment. A 300% ROI means each dollar invested returns $3.View full definition → in "one quarter," ask these five questions:
1. What data did you train on, and how different is it from ours? (Watch for distribution shift.)
2. What is the integration scope, and who pays for it?
3. What does the shadow-deployment period look like before we act on outputs?
4. What is the monitoring and revalidation plan, and what does it cost per year?
5. What is your regulatory pathway, FDA clearance or CE mark under MDR, and does model updating stay within an approved change control plan?
If the answers are vague, the ROIROIReturn on Investment: the ratio of net profit to the cost of an investment. A 300% ROI means each dollar invested returns $3.View full definition → timeline is longer than advertised.
Knowledge check
1. In the radiology AI example, the demo showed a real two-second inference, yet ROI remained negative 18 months later. What does this scenario best illustrate?
2. Why does the lesson argue that AI ROI in biotech and medtech typically arrives in years rather than quarters?
3. The sepsis prediction example highlights that EHR onset times are inconsistently recorded and back-filled. What conceptual point is this meant to demonstrate?
4. Select ALL correct answers. According to the lesson, which factors structurally delay AI value realization in this sector?
Select all the correct answers.
5. Select ALL correct answers. What can be correctly concluded about adopting the FHIR interoperability standard?
Select all the correct answers.
Not all AI use cases have the same ROIROIReturn on Investment: the ratio of net profit to the cost of an investment. A 300% ROI means each dollar invested returns $3.View full definition → shape. Rough patterns worth internalizing:
Faster payback (months, not years): administrative and back-office AI, such as automating prior authorization paperwork or clinical documentation drafting. Lower regulatory burden, fewer trust barriers, cleaner ROIROIReturn on Investment: the ratio of net profit to the cost of an investment. A 300% ROI means each dollar invested returns $3.View full definition →.
Slower payback (years): clinical decision supportdecision supportTechnologies and processes that turn raw data into actionable insights via reporting, dashboards and analysis, so teams can decide based on facts rather than intuition.View full definition →, diagnostic imaging AI, and anything touching a treatment decision. Higher validation cost, deeper trust requirements, heavier monitoring.
Longest and riskiest: AI in drug discovery. Companies like Recursion and Insilico Medicine use AI to identify drug candidates, but the payoff depends on multi-year clinical trials with high failure rates. The AI can shorten early discovery, but it cannot shorten Phase 3 trials or regulatory review. ROIROIReturn on Investment: the ratio of net profit to the cost of an investment. A 300% ROI means each dollar invested returns $3.View full definition → here is measured across the full drug development timeline, often a decade.
Match your ROIROIReturn on Investment: the ratio of net profit to the cost of an investment. A 300% ROI means each dollar invested returns $3.View full definition → expectations to the use-case category. A hospital administrator expecting drug-discovery-scale patience for a documentation tool will underinvest, and one expecting documentation-tool speed from a diagnostic model will be disappointed.