Choose Radiology AI by Workflow Evidence, Not Vendor Hype
The best radiology AI product is not the one with the longest feature list. It is the one that solves a defined clinical problem, is authorized for the intended use, performs acceptably on representative local data, fits the imaging workflow, and can be monitored throughout its operating life. Treat procurement as a clinical and operational evidence process. Define the decision first, require comparable proof, test the complete workflow, and make ongoing performance part of the contract and architecture.
Why a vendor landscape is the wrong starting point
Radiology AI is not one market with one meaningful leaderboard. Products perform different jobs: acquisition support, reconstruction, detection, triage, segmentation, measurement, quantification, reporting assistance, quality control, or workflow orchestration. They may address different modalities, anatomies, findings, users, and points in the care pathway.
A long vendor list can create the appearance of diligence while hiding the real decision. Before reviewing products, write a one-page use-case brief that names the patient population, modality and protocol, clinical task, current workflow, intended user, action triggered by the output, unacceptable failure, and outcome the organization expects to improve.
The FDA's AI-enabled medical device list is a useful U.S. market inventory and links to authorization records. It is not a head-to-head ranking, a guarantee of fit for a particular hospital, or proof that a product will improve a local workflow. Procurement begins when the organization translates an authorized intended use into its own clinical environment.
Build the evaluation brief before meeting vendors
Define the clinical decision
State whether the tool will assist detection, reprioritize a worklist, generate a measurement, improve image quality, draft report content, or perform another bounded task. Identify who sees the output and what they may do differently because of it. A product that changes worklist order creates a different risk from one that offers an optional measurement after a study is opened.
Establish a baseline
Measure the current process before promising improvement. Relevant baselines may include time to interpretation, time to notification, repeat imaging, measurement variability, report turnaround, false-alert burden, technical failure, or staff effort. Without a baseline, a pilot can produce activity without evidence of benefit.
Write failure boundaries
Describe the errors that matter: missed cases, excessive false alerts, biased performance across relevant groups, delayed images, duplicate notifications, lost results, silent downtime, incorrect measurements, or automation that distracts from the primary read. Pair each failure with a detection method and response.
A 2024 multi-society statement from ACR, CAR, ESR, RANZCR, and RSNA frames radiology AI as a lifecycle responsibility spanning development, purchase, implementation, and monitoring. That is the right procurement boundary: the product, integration, people, governance, and post-deployment evidence must be evaluated together.
Evidence to request from every radiology AI vendor
Regulatory and intended-use evidence
Request the exact product name, version, regulatory status, submission number where applicable, intended use, indications, contraindications, target population, modality, acquisition requirements, and user instructions. Read the public authorization summary instead of accepting a slide that says only “FDA cleared.” Confirm that proposed deployment matches the authorized use and current product version.
Clinical performance evidence
Ask for study design, reference standard, participating sites, sample size, inclusion and exclusion criteria, prevalence, scanner and protocol coverage, demographic and clinical subgroup results, confidence intervals, missing-data handling, and known limitations. Then inspect whether those conditions resemble the buyer's own population and workflow.
Metrics should match the task. Detection may require sensitivity, specificity, predictive values, and false positives per study. Quantification may need agreement, bias, repeatability, and failure rates. Triage needs time-to-result, queue effects, and the consequences of both false negatives and false positives. A single area-under-the-curve value cannot describe every operational tradeoff.
Human factors and workflow evidence
Require evidence about how radiologists, technologists, and other users encounter the output. Can the user see the source image and confidence or limitation information? Is the alert interruptive? Does the tool create automation bias or extra reconciliation work? Does it preserve normal reading when unavailable? A model can perform well in isolation and still make the clinical system worse.
Run a local validation that can fail
A useful pilot has a written protocol, representative cases, predefined metrics, acceptance thresholds, and a decision rule. If every outcome leads to purchase, the pilot is a demonstration rather than validation.
- Freeze the candidate configuration. Record product and model version, settings, interfaces, modality sources, routing logic, and user presentation.
- Build a representative sample. Include normal cases, target findings, difficult acquisitions, scanner and protocol variation, relevant subgroups, and known failure modes. Use properly governed data and expert reference standards.
- Test silently first. Shadow mode can compare output with the existing process without changing clinical care. It also exposes missing studies, interface delays, and unexpected routing.
- Measure clinical and operational performance. Evaluate task metrics, technical failure, latency, alert burden, user agreement, and downstream effects. Report uncertainty rather than presenting one point estimate as certainty.
- Review errors qualitatively. Examine why failures occurred and whether they cluster by protocol, device, anatomy, pathology, subgroup, or workflow state.
- Decide against thresholds. Approve, modify, extend, or stop based on the protocol. Document exceptions and who accepted them.
Local validation does not replace regulatory review or vendor evidence. It answers a different question: whether the configured system behaves acceptably in the buyer's actual environment.
Score integration and security as clinical requirements
Radiology AI typically touches imaging devices, DICOM routing, PACS, RIS, electronic health records, worklists, identity systems, and notification channels. Each connection can affect availability, privacy, data integrity, and clinical timing.
Map every data flow and control: where images and metadata go, which identifiers leave the organization, how data is encrypted, who can access it, what is retained, which subprocessors are involved, how results return, and what happens during downtime. Validate interface acknowledgments, duplicates, out-of-order events, retries, timeouts, and failure queues.
Require a software bill of materials or equivalent component transparency when applicable, vulnerability and patch processes, incident-notification terms, access logging, recovery objectives, penetration-test evidence, and a tested method to disable the tool without breaking ordinary imaging operations. Security review is not an administrative appendix; it protects the reliability of the clinical workflow.
Make lifecycle control part of the purchase
Models, scanners, protocols, patient populations, integrations, and clinical practice change. A product that passed validation at go-live can drift away from the conditions that supported the decision.
The FDA's guidance on predetermined change control plans describes how planned AI-enabled device modifications, validation methods, implementation controls, and impact assessment can be documented in a marketing submission. Buyers still need their own change-notification and revalidation rules. The contract should state which model, software, data, threshold, interface, or intended-use changes require notice and what evidence must accompany them.
Define a monitoring plan before launch. The ACR Assess-AI registry is designed for real-world monitoring of deployed imaging AI. Whether a site uses that registry or its own controls, monitoring should have named owners, review frequency, thresholds, incident handling, rollback authority, and a documented path to retire the product.
Track technical availability, missing inputs, output distribution, processing time, alert volume, clinician agreement or override, confirmed errors, subgroup performance where feasible, workflow outcomes, and vendor or local version changes. Monitoring should distinguish model performance from integration failures and changes in the underlying case mix.
A practical radiology AI scorecard
Score every candidate against the same evidence categories. Weighting should reflect the use case and consequence, but the categories should remain visible.
- Clinical fit: intended use, population, modality, protocol, and action match.
- Evidence quality: representative studies, transparent metrics, limitations, and subgroup analysis.
- Local validation: access to a testable product, exportable results, and support for predefined acceptance criteria.
- Workflow usability: clear presentation, manageable alert burden, fallback behavior, and meaningful human control.
- Interoperability: standards support, interface reliability, latency, monitoring, and data portability.
- Security and privacy: data boundaries, access control, component risk, incident response, and recovery.
- Lifecycle governance: version transparency, change notice, revalidation, post-market evidence, and retirement support.
- Commercial durability: complete cost, service levels, implementation effort, data rights, audit rights, and exit terms.
Do not let a strong score in one category erase an unacceptable failure in another. A high-performing model without reliable routing is unsafe. A smooth integration without adequate clinical evidence is not ready. A validated product without change control can become an untracked product after an update.
Where custom engineering creates leverage
Health systems do not always need another model. They often need the integration and control plane that makes an appropriate model usable: governed DICOM routing, identity and access controls, workflow orchestration, audit logs, monitoring dashboards, human-review interfaces, and a vendor-neutral layer that reduces switching cost.
Hexagon IT Solutions can support that surrounding work through custom AI applications, API integration services, and custom software development. The objective is not to replace clinical leadership or regulatory diligence. It is to make the approved workflow observable, interoperable, and operationally controllable.
The decision rule
Select radiology AI only when the exact use case is clear, the regulatory and clinical evidence supports it, local validation meets predefined thresholds, the complete workflow is secure and resilient, users retain appropriate control, and the organization can monitor and reverse deployment over time.
A vendor landscape tells you who may be available. Evidence tells you what belongs in your clinical system.
Frequently asked questions
Is FDA authorization enough to select a radiology AI product?
No. FDA authorization addresses the device and its intended use under the applicable regulatory pathway. A health system still needs to determine whether the product fits its patients, modalities, protocols, workflow, infrastructure, and operating controls.
What is local validation for radiology AI?
Local validation tests the deployed product with representative local data and workflow conditions before clinical reliance. It should use predefined metrics and acceptance thresholds, include important subgroups and failure cases, and document the product version, configuration, and test environment.
Which metrics should a radiology AI buyer request?
Metrics must match the clinical task. Depending on the use case, buyers may need sensitivity, specificity, positive and negative predictive value, calibration, false-alert burden, processing time, technical failure rate, subgroup results, and workflow outcomes. A single aggregate accuracy number is rarely sufficient.
How should radiology AI be monitored after launch?
Monitor technical availability, input quality, output distribution, alert volume, clinician agreement or override, subgroup performance where feasible, version changes, incidents, and downstream workflow effects. Define owners, thresholds, escalation, rollback, and revalidation triggers before go-live.
How can a health system compare radiology AI vendors fairly?
Give each vendor the same use-case brief and evidence request. Score intended-use fit, local performance, integration, security, usability, lifecycle controls, monitoring support, commercial terms, and exit provisions. Compare evidence and operating burden, not demonstration polish.
