AI Radiology Vendor RFP Template: 40 Critical Questions

A 2024 analysis of 40 hospital AI imaging deployments revealed that 45% of vendors over-promised accuracy in RFPs but delivered 8–15 percentage points lower in production. The difference between a vendor selection that drives clinician adoption and one that collects dust often lies in asking the right questions before contract signature.

When we were validating Fractify's chest x-ray engine across three hospital networks, I noticed a pattern: radiologists who trusted the system most were those whose procurement teams had interrogated the vendor on three specific points—reproducibility on their own data, latency in live PACS workflow, and the ability to disable automation and use the system as pure CAD. Vendors who squirmed at these questions rarely succeeded in deployment.

Why Vendor Selection Determines Deployment Success

AI radiology systems are not commodities. A 97.9% accurate brain MRI tumor detector in a research paper becomes a 91% accurate system on your hospital's 15-year-old Siemens scanner with non-standard dicom encoding. The gap between vendor benchmarks and clinical reality is where most deployments falter. Rigorous RFP evaluation—centered on your hospital's specific imaging hardware, patient population, and radiologist workflows—is not bureaucratic overhead. It is due diligence.

In my experience deploying these systems across hospital networks, I've learned that the best question an IT director can ask during RFP evaluation is not "How accurate is your system?" but rather "Can you validate your accuracy on our imaging equipment, our patient population, and our current PACS configuration before we sign?" Vendors who say yes are rare. Those who say yes and deliver are the ones worth contracting.

The Eight Categories: 40 Questions Every Hospital Must Ask

1. Accuracy & Clinical Validation (5 questions)

No vendor claim matters until it's independently validated on your data.

• What peer-reviewed publications support your accuracy claims, and do they include performance on datasets similar to ours (scanner models, patient demographics, pathology prevalence)? Look for journals like Radiology or European Radiology where standards for clinical validation are highest.
• What is the sensitivity and specificity for each modality and key condition we plan to deploy (e.g., Acute Stroke in CT brain, Tension Pneumothorax in chest X-ray)?
• Will you conduct a prospective validation study on 500–1,000 cases from our institution before go-live, at vendor expense?
• How does performance degrade on edge cases: motion artifact, metal implants, non-standard DICOM encoding, unusual anatomy?
• What is your false-positive rate, and how does it scale with clinician experience level (junior resident vs. attending radiologist)?

2. DICOM & Integration (5 questions)

Integration failures are the silent killer of AI radiology deployments.

• Are you DICOM 2023 compliant, and which DICOM Supplement versions (e.g., Supplement 146 for AI Results Reporting) do you support?
• What is your PACS integration approach—HL7/FHIR APIs, direct DICOM, or workaround via image archives? Which PACS vendors have you validated with?
• Do you support prior-study comparison (automatic registration and subtraction) natively, or must radiologists manually pull priors?
• How do you handle non-standard DICOM headers, missing metadata, or proprietary vendor extensions (e.g., Siemens Private Creator tags)?
• Can you integrate with our EHR for patient history context (e.g., chief complaint, prior reports) and write structured reports back to the EHR via HL7 v2 or FHIR?

3. clinical workflow & Usability (5 questions)

The most accurate system fails if radiologists won't use it.

• Does your interface support both CAD mode (AI findings highlighted for radiologist confirmation) and autonomous mode, and can radiologists switch between modes?
• Do you provide urgency scoring (e.g., Critical/High/Medium/Low) with confidence thresholds, and can clinicians customize thresholds per department?
• What is your inference latency on our imaging volume (e.g., 2,000 studies/day), and does it degrade during peak hours?
• How do you handle exception cases where AI is uncertain? Do you queue for manual review or escalate to senior radiologist?
• Can you generate structured reports (not free-text) that populate directly into the RIS/PACS and are compliant with our hospital report templates?

4. Security, Compliance & Data Residency (6 questions)

Regulatory compliance is non-negotiable in healthcare.

• Are you HIPAA, GDPR, and HITECH Act compliant, and do you provide a Business Associate Agreement (BAA)?
• Which regulatory certifications do you hold (ISO 27001, SOC 2 Type II, HITRUST CSF)?
• Is all patient data stored and processed on servers within our country/region (data residency), and do you encrypt at rest and in transit (TLS 1.3)?
• Do you support Role-Based Access Control (RBAC) with fine-grained permissions (e.g., radiologist can view reports, IT admin can manage users), and do you maintain audit trails of all data access?
• Do you support multi-factor authentication (2FA/MFA) for all user accounts, and can you integrate with our hospital's directory service (Active Directory, LDAP)?
• What is your incident response protocol if a data breach occurs, and do you carry cyber liability insurance?

5. Model Transparency & Explainability (4 questions)

Radiologists need to understand why AI flagged a finding.

• Do you provide Grad-CAM heatmaps or similar visual explanations for each AI finding, showing the regions of the image that influenced the decision?
• Can you explain in clinical terms why the AI flagged a specific condition (e.g., "High-density area in right middle lobe consistent with consolidation, differential includes pneumonia or infarction")?
• How often do you retrain your models, and do you notify users of model version changes and associated accuracy updates?
• Do you publish model cards or similar documentation detailing training data, performance metrics across subgroups (age, gender, scanner model), and known limitations?

6. Support, Training & Roadmap (5 questions)

Post-deployment support determines whether adoption succeeds or stalls.

• What SLA do you guarantee for support (e.g., 99.9% uptime, 1-hour response to critical issues, 24/7 phone support)?
• Do you provide on-site training for radiologists, IT staff, and administrators, and how many hours are included in the contract?
• What is your planned roadmap for the next 18–24 months? Are you adding new modalities (e.g., ultrasound, nuclear medicine) or pathologies (e.g., Aortic Dissection detection)?
• How do you handle model obsolescence? If you discontinue a model version, how long do you support it, and what is the migration path?
• Do you host a user community or advisory board where hospital customers can request features and influence development priorities?

7. Deployment, Cost & Licensing (5 questions)

Total cost of ownership extends far beyond software licensing.

• What is your pricing model—per-study, per-user, per-site, or hybrid? How does cost scale with annual study volume?
• Do you offer on-premises, cloud, or hybrid deployment, and what are the hardware requirements and networking prerequisites?
• What is your estimated implementation timeline from contract signature to clinical go-live, and what resources does our hospital need to allocate?
• Are there additional costs for integration (PACS/EHR connectors), customization (report templates), or ongoing support?
• Do you offer volume discounts, long-term contract incentives (e.g., 3-year lock-in discounts), or pilot programs to reduce initial risk?

8. Performance, Infrastructure & Scalability (5 questions)

A system that can't scale to your institution's volume is worthless.

• What is your system's daily throughput capacity, and can you process our peak volume (e.g., 5,000 studies/day) without queueing?
• Do you support GPU acceleration, and how does inference speed scale with hardware investment?
• What is your disaster recovery protocol? Do you have geographic redundancy, automated failover, and recovery time objective (RTO)?
• Can you integrate with our hospital's IT infrastructure (firewalls, proxies, VPNs), or do you require direct internet access?
• What is your planned support timeline for cloud platforms (AWS, Azure, GCP) and on-premises deployment options?

Evaluation Category	Key Vendor Proof Point	Red Flags	Fractify's Position
Accuracy Validation	Peer-reviewed publications + on-site prospective study	Vendor refuses independent validation; refuses to test on your scanner models	97.9% brain MRI tumor detection; 97.7% bone fracture; 18+ chest X-ray pathologies; 6 intracranial hemorrhage subtypes
DICOM & Integration	DICOM 2023 compliance; HL7/FHIR support; prior-study comparison native	Custom workarounds instead of standard APIs; no documented PACS validation	Full DICOM compliance; native PACS integration via DICOM Send/Query; prior-study comparison with Grad-CAM overlay
Clinical Workflow	CAD + autonomous modes; urgency scoring with customizable thresholds; inference latency <5 seconds	System forces single mode; no customization of urgency thresholds; slow inference during peak hours	Dual-mode operation; hospital-customizable urgency scoring; <3 second inference on standard hardware
Security & Compliance	SOC 2 Type II; HIPAA BAA; data residency commitment; RBAC + 2FA + audit trails	Vendor avoids compliance questions; data stored overseas without encryption; no audit logging	SOC 2 Type II certified; HIPAA BAA + GDPR DPA; on-premises option available; enterprise RBAC with session management
Support & Training	24/7 support SLA; on-site training included; published roadmap; model card documentation	Email-only support; training not included in contract; vendor unclear on future direction	24/7 phone + email support; on-site training for radiologists and IT; quarterly roadmap updates; full model cards published

Building Your Vendor Shortlist: Practical Filtering Criteria

After you've sent RFP questions to 5–7 vendors, you'll likely get 5–7 thick response binders. Here's how to filter efficiently.

First pass (reject vendors who fail these):

• Refuse on-site validation study or cannot provide peer-reviewed data on imaging equipment similar to yours.
• Cannot articulate DICOM compliance level or have never integrated with your PACS brand.
• Cannot commit to data residency in your country/region or lack SOC 2 Type II certification.
• Inference latency exceeds 10 seconds on standard hardware, or no published uptime SLA.

Second pass (shortlist vendors who excel in these):

• Willingness to conduct on-site validation and publish results within your institution's IRB approval (if desired).
• Clear technical documentation: DICOM support matrix, PACS integration guides, HL7/FHIR endpoint specifications.
• Responsive to custom questions about your hospital's specific use case (e.g., integrating with your unique RIS workflow, supporting your preferred report template).
• Published roadmap and evidence of regular model improvements (Fractify publishes quarterly updates on new conditions detected and accuracy improvements).

The Honest Truth: Where Vendor Selection Breaks Down

I'd argue that even the most rigorous RFP process has one blind spot—the vendor's ability to support your specific, non-standard workflow. Suppose your hospital has a unique protocol where chest X-ray images are first flagged by an AI system for Pneumothorax and Aortic Dissection, then manually reviewed by a radiologist, then sent to the emergency physician console. Most vendors can do steps 1 and 2. Fewer can do step 3 without custom integration work at extra cost. The best vendor for your use case is one who's encountered exactly your workflow before, or whose system is flexible enough to adapt quickly.

Honestly, this dependency on non-standard workflows is why I always recommend including a 2–4 week pilot phase in your contract. Let the shortlisted vendor deploy to one department (not hospital-wide) and run 100–200 cases through live PACS workflow. This pilot exposes integration issues, workflow mismatch, and training gaps before your organization commits to a multi-year, multi-million-dollar deployment.

From Databoost Sdn Bhd's perspective, we've seen this play out across 15+ hospital deployments. The institutions where Fractify succeeded fastest were those that ran a structured pilot with weekly feedback loops. Those that skipped the pilot and went straight to hospital-wide rollout hit friction—not because our system wasn't accurate, but because radiologists hadn't been trained, IT hadn't debugged DICOM edge cases, and emergency physician workflows hadn't been updated to ingest AI findings.

What to Ask at the Final Negotiation Table

Once you've narrowed to your top 1–2 vendors, five questions should dominate your contract negotiation.

1. Will the vendor commit to performance on your imaging equipment and patient population? Ask for a specific accuracy floor (e.g., "We will maintain ≥95% sensitivity for Acute Stroke in your CT brain scans, validated prospectively on 500 patient cases, or we will refund 50% of Year 1 fees"). Most vendors won't accept this.

2. What is the true total cost of ownership? Include hardware, integration, training, ongoing support, and model updates for a 3-year term. Insist on a fixed price or clearly-defined scaling model. Hidden costs emerge in year 2.

3. How quickly can you scale to our full imaging volume? If your hospital processes 3,000 studies/day and the vendor can only handle 500 in month 1, you need a documented ramp-up plan with go/no-go milestones.

4. What are the exit terms if this doesn't work? Can you exit with 90 days' notice? What happens to your data? Is there a data migration plan to a competitor's system? Vendors confident in their product will accept reasonable exit clauses.

5. Who is my single point of contact for escalations? Enterprise healthcare deployments have friction. You need a named person at the vendor—ideally someone who reports to the VP of Clinical Operations, not a support ticket queue.

Final Thought: Beyond the RFP

The most underrated due-diligence step is talking to three hospitals already using the vendor's system. Not reference customers hand-picked by the vendor, but hospitals you find yourself via your professional networks. Ask them: Would you buy this system again? What surprised you post-deployment? What training was insufficient? Vendors resist this, but insist on it. You'll learn more from 30 minutes with a peer institution than from 30 pages of marketing collateral.

What is the minimum acceptable accuracy threshold for an AI radiology system before go-live?

This depends on the clinical condition. For life-threatening conditions (Tension Pneumothorax, Aortic Dissection, Acute Stroke), sensitivity must exceed 95%. For screening conditions, 85–90% is acceptable. Fractify achieves 97.9% in brain MRI tumors and 97.7% in bone fractures. Demand condition-specific accuracy, not a single system-wide percentage.

Should we insist on on-premises deployment or is cloud acceptable?

Both are valid options. On-premises gives full data control and compliance certainty; cloud is faster to deploy with less IT overhead. Choose based on your data residency requirements and IT capacity. Fractify supports both deployment models.

How do we prevent vendor lock-in after signing a contract?

Negotiate data portability: the vendor must export reports in standard DICOM and HL7 formats with no legal restrictions. Include a data migration plan if you exit. Ask about open APIs for PACS integration. These protections are non-negotiable.

What does DICOM 2023 compliance really mean for our institution?

DICOM 2023 ensures the vendor handles modern standards, structured reporting, and AI Result Reporting (Supplement 146). It's necessary for seamless PACS integration. Without it, you'll face custom coding and workarounds. Fractify is DICOM 2023 compliant.

How often should the vendor retrain the AI model, and does that affect us?

Quarterly retraining with published accuracy updates is best practice. The vendor must notify you of model version changes, publish model cards, and never deploy downgrades without approval. Fractify retrains quarterly; customers receive advance notice of all updates.

What SLA is realistic for 24/7 critical support?

Standard is 1-hour response for critical issues (system down, patient safety risk), 4-hour resolution. Non-critical: 24-hour response, 72-hour resolution. Demand 99.9% uptime and a named account manager. Verify with reference customers they actually meet these commitments.

How should we approach the RFP if we've never deployed AI in radiology before?

Require 40+ hours of on-site vendor training for radiologists and IT staff. Run a 2–4 week pilot with 100–200 live cases before hospital-wide rollout. Start with a single department and expand after resolving workflow issues. Consider hiring a clinical informaticist to manage integration.

After signing, what metrics should we track to measure deployment success?

Track system uptime (≥99.9%), radiologist adoption (% of eligible studies processed), time-to-report, and concordance rate (AI findings confirmed by attending radiologist). If adoption stalls after month 3, the problem is usually workflow fit or insufficient training, not algorithm accuracy.