AI Radiology Procurement: The Hospital IT Director Checklist

Hospital IT directors evaluating AI radiology systems face a procurement decision with outsized clinical consequences. A poorly chosen system creates parallel workflows, radiologist friction, and liability exposure. A well-chosen one reduces critical-finding turnaround from hours to minutes and surfaces time-critical findings — Tension Pneumothorax, Aortic Dissection, Intracranial Hemorrhage, Acute Stroke — before they become preventable patient harm events. The questions below map the evaluation criteria that separate those two outcomes.

Category 1: Clinical Validation — The Non-Negotiable Foundation

General accuracy figures are marketing data. What the clinical evaluation team needs is per-pathology performance broken down by sensitivity, specificity, positive predictive value, and AUC — for every condition the system claims to detect. A chest x-ray AI that reports 92% overall accuracy may perform at 71% sensitivity for Tension Pneumothorax — a time-critical emergency where false negatives directly contribute to preventable mortality. Demand the breakdown. Any vendor who cannot produce per-pathology metrics from a published or auditable validation study should not advance past initial screening.

Fractify's chest X-ray analysis module detects 18+ distinct pathologies including Tension Pneumothorax, consolidation, pleural effusion, cardiomegaly, and pulmonary nodules, each with individually validated performance metrics. The bone fracture detection module reaches 97.7% accuracy across the musculoskeletal spectrum. These are structured validation outputs — not promotional estimates. Require the same standard from every vendor under evaluation.

Dataset representativeness is the second critical validation question. AI models trained and validated exclusively on data from academic centers in one country frequently underperform in community hospitals elsewhere due to population-level differences in disease prevalence, equipment generations, and acquisition protocol variation. Request the demographic profile, scanner brand composition, and geographic origin of the validation cohort before accepting any performance claim at face value.

According to the WHO's 2023 Global Health Workforce Statistics report, radiology ai tools deployed without independent peer-reviewed validation contribute to diagnostic inconsistency rather than reducing it. Prioritize vendors with publications in indexed journals — Radiology, European Radiology, or equivalent — or with regulatory clearances (CE Mark, FDA 510(k), or national medical device authority approval) that required independent technical evaluation as part of the approval process.

Category 2: Integration Architecture — Where Deployments Stall

Five integration points determine whether an AI radiology system enhances or disrupts your department's workflow. Each one must be demonstrated in a live technical session — not described in a vendor datasheet.

DICOM conformance: Request the vendor's DICOM Conformance Statement — a formal technical document standardized by the DICOM Standards Committee (PS 3.2) that specifies exactly which Service-Object Pair (SOP) classes the system supports for Storage, Query/Retrieve, Worklist Management, and Structured Reporting. A vendor who cannot provide this document cannot guarantee interoperability with your PACS, modalities, or archive system. The phrase DICOM compatible without an accompanying conformance statement is not technically meaningful.

PACS integration method: The mechanism by which AI findings reach radiologists determines alert latency. Passive listener architectures, where the AI polls for new studies at intervals, introduce delay. Active push architectures, where the PACS triggers AI analysis on study completion, deliver findings faster and more reliably. For conditions where minutes determine outcomes — Intracranial Hemorrhage, Aortic Dissection on CT angiography, Acute Stroke on brain MRI — establish a maximum acceptable time-to-alert in your RFP and require the vendor to demonstrate compliance under simulated peak load before any contract is executed.

HL7/FHIR output: Modern hospital information systems and EHRs exchange structured clinical data via HL7 v2.x messages or FHIR APIs (R4 or later). An AI radiology tool that produces findings only as PDF reports or proprietary notifications creates a data silo. Radiologists must manually transcribe AI findings into the HIS, which eliminates most efficiency gains and introduces transcription error risk. Require that AI findings are available as structured HL7 ORU messages or FHIR DiagnosticReport resources that feed directly into clinical workflows without manual intervention.

Urgency scoring and worklist prioritization: Urgency scoring is the mechanism by which AI findings change radiologist reading order automatically. A system that flags probable Intracranial Hemorrhage — which Fractify classifies across 6 specific subtypes including epidural, subdural, subarachnoid, intraventricular, intraparenchymal, and diffuse axonal injury patterns — must surface that case at the top of the radiology worklist without requiring any manual step. Confirm this behavior with a live demonstration on realistic case volume, not a curated demo dataset.

Prior-study comparison: Longitudinal AI analysis — comparing a current study against historical imaging to quantify nodule growth, lesion volume change, or treatment response — requires access to archived DICOM data. Confirm that the architecture supports automated prior-study retrieval from your specific archive system, and establish the data retention scope the AI system can access during analysis.

Category 3: Security, Governance, and Data Residency

Patient imaging data is among the most sensitive categories of personal health information. Three governance questions are non-negotiable before any AI radiology system reaches production.

RBAC (Role-Based Access Control): Every clinical AI system must implement granular RBAC. Radiologists, referring physicians, technologists, administrators, and IT staff require different access levels to AI findings, configuration settings, and audit data. Require a live demonstration of the permission model — a written description of intended RBAC functionality is not a substitute for demonstrating it in the actual system.

Immutable audit logging: Every AI inference event must be captured in an immutable log: which study was analyzed, what findings were generated at what confidence level, which clinician acknowledged the finding, and whether the radiologist overrode the AI recommendation. These logs support clinical governance programs, regulatory audits, and retrospective quality assurance. Systems that provide only aggregate usage statistics cannot meet this requirement.

Data residency: Where is patient imaging data processed — on-premise, in a specified regional cloud, or transmitted internationally? On-premise inference keeps data within your network boundary entirely. Cloud processing requires specifying the exact data center region and executing a Data Processing Agreement compliant with applicable regulations: Malaysia's Personal Data Protection Act 2010, GDPR in EU jurisdictions, or national health data sovereignty equivalents. Cloud-based AI without a committed data residency specification is a procurement and compliance risk, not a feature.

Category 4: AI Transparency and Radiologist Workflow Design

AI systems that function as black boxes generate clinical liability and radiologist resistance. Three transparency mechanisms determine whether a system integrates into radiology practice or sits underused after go-live.

Grad-CAM heatmap overlays: Gradient-weighted Class Activation Mapping (Grad-CAM) generates visual heatmaps that highlight the pixel regions driving each AI prediction. These overlays must render directly within the radiologist's existing DICOM viewer — not in a separate application that requires window-switching. Fractify renders Grad-CAM overlays inline, allowing radiologists to visually verify the anatomical basis of each finding before accepting or overriding it. Systems that require a separate application for AI overlay viewing consistently show lower radiologist adoption rates.

Confidence scoring and calibration: Each AI finding should carry a calibrated confidence score. Calibration matters as much as the score itself: a well-calibrated system has findings reported at 90% confidence that are correct approximately 90% of the time. Uncalibrated scores are numbers without clinical meaning. Ask specifically how the vendor validates score calibration, and whether calibration holds under distribution shift — images from scanner models not represented in training data.

Override workflow design: Radiologists must be able to disagree with AI findings formally and without friction. Systems that make overriding cumbersome — excessive click sequences, mandatory free-text justification fields, or system alerts triggered when a radiologist disagrees — generate confirmation bias at scale. The override action should be as easy as the accept action, and all overrides should automatically feed into a quality improvement feedback loop.

Category 5: Operational Reliability and Model Governance

AI radiology systems become critical clinical infrastructure within months of deployment. Operational questions that appear administrative during procurement become patient-safety questions after go-live, when radiologists begin relying on automated alerting for time-critical findings.

Uptime SLA specificity: A 99.5% uptime SLA permits 43.8 hours of downtime per year — enough to cover an entire overnight shift's critical-finding alerting. Negotiate uptime guarantees specific to the critical-finding alerting function, with defined financial and operational remedies for breach. General system uptime SLAs that bundle alerting functions with administrative features are insufficient for clinical AI infrastructure.

Model update notification and versioning: AI model updates can change finding sensitivity for specific pathologies without any visible system change. A model update that improves pulmonary nodule detection may simultaneously reduce pneumothorax sensitivity. Require advance notification of all model updates with version changelogs that include performance delta data for every validated pathology category. Negotiate the contractual option to delay updates during high-volume clinical periods or planned quality audits.

Regression testing disclosure: When a vendor updates a model, their internal validation process determines whether your radiologists will be protected from unannounced performance changes. Request the regression testing protocol in writing, and require that test results covering all validated pathology categories are provided to your clinical team before any update is pushed to production systems.

Building Your Evaluation Scorecard

Procurement committees that evaluate AI radiology systems without a structured scorecard consistently report post-deployment regret about integration gaps they failed to assess during the vendor selection process. The ten criteria in the table above provide the framework. Score each vendor 1 through 5 on each criterion. Apply weightings that reflect clinical consequence: clinical validation (×3), PACS integration (×3), and data residency (×2) should carry more weight than operational questions — these are the failure modes that create patient harm, not operational inconvenience.

Pilot deployments should run for a minimum of 8 weeks on live clinical volume — not curated test datasets — before final procurement decisions are made. Measure three outcomes during the pilot: median time from study completion to critical-finding alert delivery in the PACS worklist; radiologist override rate as a proxy for AI calibration quality and workflow fit; and technologist burden, specifically whether the AI creates additional steps or removes them. These three metrics, measured prospectively, provide the objective basis for final vendor selection that no demo or vendor-supplied reference call can replace.