For a hospital considering AI and machine learning in healthcare, the useful question is not whether the technology is exciting. It is whether a specific tool can survive the budget review after the pilot ends: license fees, IT work, clinician time, monitoring, model upkeep, and all. On the 2025 evidence, the answer is selective. The strongest economic cases sit in diabetic retinopathy screening, AI-assisted colonoscopy, and medication management. The broader literature is much less settled, partly because a 2025 systematic review in npj Digital Medicine found that 63% of cost-effectiveness studies used static models, a choice that can overstate long-term value when disease progression, behavior, capacity, and model performance change over time.[1]
That same review is the right place to start because it keeps the purchase conversation tied to economic method rather than product enthusiasm. It examined 19 studies and found promising results in several clinical applications, but it also identified a recurring problem: many evaluations did not fully count infrastructure, integration, and maintenance costs.[1] In a board packet, those missing lines can be the difference between a defensible cost-effectiveness estimate and a savings story that only works because the hard work has been priced at zero.
Where the Economic Case Looks Strongest
The better-supported examples have a common shape. They do not ask an entire health system to reorganize around a general-purpose AI platform. They attach a model to a bounded clinical workflow, compare it with usual care, and measure consequences that can plausibly be costed: a screening visit completed, a specialist referral avoided, a lesion found earlier, a medication error prevented, or a downstream event reduced.
| Clinical use case | Economic signal in the 2025 evidence | Why the estimate is easier to audit |
|---|---|---|
| Diabetic retinopathy screening | 14–19.5% per-patient cost reductions in Singapore and rural China; ICERs reported as low as $1,107 per QALY | Defined screening pathway, measurable referral patterns, and comparison with usual care |
| AI-assisted colonoscopy | Estimated annual savings of $149 million in Japan and $85 million in the United States | Downstream economic value is linked to detection and prevention in a known procedure |
| Medication management | Documented ROI of 12.4:1 | Costs and benefits can be tied to medication review, adverse-event prevention, and workflow substitution |
These figures should not be read as interchangeable procurement guarantees. They come from particular settings, workflows, and assumptions. But they are more useful than a generic claim that AI improves efficiency because they give finance and clinical operations teams something to interrogate: which patients enter the workflow, what usual care costs, which events change, and over what time horizon the benefits appear.[1]
Diabetic Retinopathy Screening Has the Cleanest Cost-Effectiveness Story
Diabetic retinopathy screening deserves the most attention because the economics are attached to a relatively clean operational problem. A health system needs to identify eye disease among patients with diabetes, but specialist capacity is limited and routine screening can be missed or delayed. An AI-enabled pathway can change who reviews images, which patients are referred, and how quickly normal or low-risk results move through the system.
In the 2025 review, diabetic retinopathy screening was associated with per-patient cost reductions of 14–19.5% in Singapore and rural China, and some analyses reported incremental cost-effectiveness ratios as low as $1,107 per quality-adjusted life year.[1] Those are the kinds of numbers that can be discussed seriously in a budget meeting because they are not just broad productivity claims. They connect the intervention to screening costs, referral patterns, and health outcomes.
The key advantage is traceability. If a diabetic eye-screening tool reduces the need for specialist review of normal images, the analyst can ask how many images are screened, how many are referred, what the false-positive and false-negative consequences are, and what staff still need to do. The answer may differ by country, payer, staffing model, and screening backlog. But at least the economic model has a recognizable skeleton.
This is also where AI can look more compelling in low-resource or access-constrained settings, though that claim needs care. A rural screening program and a high-resource urban specialty network are not the same economic environment. If the intervention substitutes for scarce review capacity, the value proposition is different from a setting where the same review capacity already exists and the AI layer mainly adds procurement and oversight costs.
Colonoscopy and Medication Management Are Promising, but Less Portable
AI-assisted colonoscopy has a different economic pathway. The cost argument depends on whether improved detection during a procedure changes downstream cancer prevention, treatment costs, and follow-up patterns. The 2025 evidence included estimated annual savings of $149 million in Japan and $85 million in the United States.[1] These are large system-level figures, which makes them attractive in policy discussions and more fragile in local purchasing decisions.
A hospital cannot simply import a national savings estimate and treat it as its own return. The local result will depend on procedure volume, baseline detection rates, reimbursement, pathology and follow-up costs, equipment arrangements, training, and whether the AI system changes procedure time. Still, colonoscopy is a stronger candidate than many enterprise AI ideas because it starts with a defined procedure and a measurable clinical target.
Medication management is the third area where the economic signal is worth taking seriously. The review reported a documented ROI of 12.4:1.[1] That figure is striking, but it should be handled as a workflow result rather than a universal promise. Medication review, reconciliation, adherence support, and adverse-event prevention can all create value, but they do so through different labor patterns and risk reductions. The same headline ROI may not hold if the local pharmacy team must add manual review time to make the tool safe.
For readers comparing clinical and administrative use cases, broader market and adoption figures can be useful background. ClinicalMind’s AI in Healthcare by the Numbers and AI in Healthcare Administration articles are better places for that wider ROI context. The clinical cost-effectiveness question is narrower: whether a defined intervention improves outcomes enough, or reduces resource use enough, to justify its full cost.
Why Published Savings Can Look Better Than Deployed Savings
The methodological warning in the 2025 systematic review is not a footnote. If 63% of studies use static models, then many published estimates are freezing the system at the very moment when implementation would make it move.[1] Static models can be appropriate for some questions, but they are poorly suited to every situation where disease progression, clinician behavior, patient uptake, capacity constraints, or algorithm performance changes over time.
The omitted-cost problem is just as important. Upfront infrastructure is not abstract. Someone has to connect the AI tool to imaging systems, the electronic health record, identity management, reporting dashboards, cybersecurity review, procurement workflows, and clinical governance. Someone has to validate performance before launch, monitor drift after launch, update protocols, train staff, and respond when the model produces an unexpected result. If those costs are not in the denominator, the ROI has been flattered before the tool sees its first real patient.
This is why cost-effectiveness evidence needs sensitivity analysis, not just a base case. A base case can say an AI screening program is cost-effective under expected assumptions. A useful sensitivity analysis asks whether the conclusion still holds if software fees rise, uptake is lower, staff review takes longer, referral rates increase, or maintenance costs are higher than expected. The question is not whether the model can produce a favorable number. It is how easily that number breaks.
Glaucoma screening is a useful counterexample because it shows why better clinical outcomes do not automatically mean lower costs. In the reviewed evidence, glaucoma screening improved outcomes but added long-term costs.[1] That does not make the intervention a failure. It means the economic claim has to be stated honestly: the tool may buy health benefit at additional cost rather than generate net savings.
Industry ROI Figures Belong in a Separate Box
Industry-compiled ROI figures often circulate faster than peer-reviewed economic evaluations. The commonly cited 3.2:1 average ROI and 12–18 month payback period are useful as market signals, especially when they show what vendors and early adopters believe buyers want to see. They should not be treated as independent proof that a new clinical AI purchase will return three dollars for every dollar spent.
The distinction matters because vendor-aggregated data often blends different use cases, maturity levels, accounting methods, and deployment contexts. A documentation assistant, a scheduling tool, a radiology triage model, and a diabetic retinopathy screener do not share the same cost base or benefit pathway. Pooling them may describe a market mood, but it does not answer whether a specific clinical AI tool is cost-effective in a specific hospital.
For procurement teams, the safer use of those figures is as a prompt for diligence. If a vendor claims a short payback period, ask which costs are included, whether customer labor is counted, whether integration was paid for by the vendor during the pilot, and whether the ROI depends on avoided events that have not yet been observed locally. A purchasing model that cannot answer those questions is not ready for capital approval.
What a Budget-Ready AI Evaluation Should Count
The practical test is simple to state and difficult to execute: count the work the institution actually has to do. For clinical AI, that usually means the evaluation cannot stop at license price and projected downstream savings. It needs to include implementation labor, clinical validation, staff training, workflow redesign, monitoring, model maintenance, vendor management, cybersecurity review, and the cost of acting on AI outputs.
- Define the comparator: usual care, specialist review, manual chart review, existing software, or no screening program.
- Separate one-time costs from recurring costs: integration, hardware, licenses, monitoring, retraining, and support.
- State the time horizon: short pilots can miss downstream benefits, while long horizons can overstate savings if assumptions are static.
- Show who does the work: clinicians, pharmacists, IT staff, data teams, quality staff, or vendor personnel.
- Test assumption changes: uptake, prevalence, referral rates, false positives, staffing time, and maintenance costs.
This is also where clinical evaluation and economic evaluation meet. A model that looks accurate in isolation can still be uneconomic if it increases downstream review without improving outcomes enough to justify the added work. Conversely, a modest model can be valuable if it safely removes a repetitive bottleneck in a high-volume pathway. For readers building that broader evidence file, ClinicalMind’s guide to evaluating AI tools in clinical practice complements the cost-effectiveness lens.
CHEERS-AI May Improve the Next Wave of Evidence
The emerging CHEERS-AI framework, an AI-focused extension of CHEERS 2022, is worth watching because economic evaluations need more consistent reporting on model behavior, implementation context, and cost categories. It is not a cure by itself. A reporting framework can make weak assumptions more visible; it cannot make a short pilot equivalent to mature deployment evidence.
Its value will depend on adoption. If future studies use CHEERS-AI to specify the AI system, comparator, setting, cost inputs, maintenance assumptions, uncertainty analysis, and performance monitoring plan, procurement teams will have a cleaner basis for comparing tools. If it remains a methodological aspiration cited after the fact, the literature will continue producing estimates that look precise without being operationally complete.
The 2025–2026 answer, then, is neither a blanket endorsement nor a dismissal. AI in healthcare can be cost-effective in well-bounded clinical workflows, especially where the task is repeatable, the comparator is clear, and downstream events can be measured. Diabetic retinopathy screening, AI-assisted colonoscopy, and medication management currently offer the most concrete economic signals. The case becomes weaker when studies rely on static assumptions, omit deployment costs, or generalize from one setting to another without showing that the workflow economics still hold.[1]
References
- Cost-effectiveness of artificial intelligence in healthcare: a systematic review, npj Digital Medicine, 2025.
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