AI Radiology Vendor RFP Template: 40 Essential Procurement Questions

When was the last time you read an RFP response that accurately described what an AI system would actually do in your radiology department? Most vendors promise accuracy figures without explaining the study design, patient demographics, or imaging protocol. Meanwhile, procurement teams lack a framework to ask the right follow-up questions.

What Is an AI Radiology Vendor RFP?

An AI radiology vendor Request for Proposal (RFP) is a formal procurement document that articulates your hospital's clinical and technical requirements, then invites vendors to describe how their AI systems meet those requirements. Unlike traditional radiology system RFPs, an AI RFP must evaluate not just deployment and integration, but also the underlying model validation, bias testing, performance degradation scenarios, and clinical governance processes. The RFP serves as a binding specification: vendors commit to the accuracy rates, integration timelines, and compliance controls they describe in their response. A well-structured RFP is your primary tool for vendor accountability and risk mitigation.

Why Traditional Radiology RFPs Fail for AI Systems

Hospital procurement teams often recycle RFP templates from traditional PACS, teleradiology, or EHR implementations. These templates ask the right questions about vendor stability, support response times, and HIPAA compliance—but they miss the unique complexities of AI. Consider these gaps:

Accuracy claims are unverified. A vendor claims 97.5% sensitivity on fracture detection. On what dataset? Which fracture types? What was the patient population—children, elderly, trauma patients, or a mix? When Fractify validated the 97.7% bone fracture detection accuracy in our clinical trials, we tested across age groups, fracture severity levels, and anatomical regions. A traditional RFP won't ask for this granularity.

Integration timelines are underestimated. Vendors claim "4-week implementation." But this usually means software installation, not clinical validation, clinician training, or the incremental rollout that most radiology departments require. In my experience deploying Fractify across hospital networks, integration timelines depend heavily on dicom standardization, PACS vendor cooperation, and your team's data governance maturity—factors a basic RFP won't surface.

Bias and demographic performance are ignored. An AI system validated on a predominantly European dataset may perform differently on South Asian or African patient populations due to anatomical variation, imaging equipment differences, or disease prevalence patterns. Traditional RFPs don't ask vendors to report performance stratified by demographic groups or to disclose the ethnic/geographic composition of their training datasets.

This RFP template addresses these gaps with 40 vendor-agnostic questions organized into five categories: clinical accuracy and validation, regulatory compliance and security, technical integration, workflow integration, and support and scalability.

Category 1: Clinical Accuracy & Validation (8 Questions)

Accuracy claims mean nothing without context. These questions force vendors to articulate the evidence behind their numbers.

1. On what dataset was the AI system trained and validated? Demand specific dataset composition: sample size, imaging modalities (CT, MRI, X-ray), anatomical regions, disease prevalence, patient demographics (age range, gender distribution, comorbidities), and imaging equipment models. Vendors should disclose whether datasets are public (ImageNet, CheXpert) or proprietary.

2. What is the clinical validation methodology? Was the validation prospective (new patients imaged after model training) or retrospective (historical cases)? Were images reviewed by a single radiologist or a consensus panel? Was there a reference standard—biopsy, surgery, or clinical outcome follow-up? Fractify's chest X-ray model detects 18+ pathologies and was validated prospectively against radiologist consensus, not on a held-out dataset, to ensure real-world applicability.

3. What are the sensitivity, specificity, and AUC (area under the curve) reported separately for each condition? Not an overall "97.9% accuracy" figure. Demand per-condition metrics. Brain MRI tumor detection accuracy varies dramatically by tumor type, size, and location.

4. How does accuracy degrade by imaging protocol, scanner model, and image quality? A chest X-ray acquired on a portable bedside unit differs from a departmental radiography room. An MRI from a 1.5T scanner differs from a 3T. Vendors should report accuracy for the specific equipment your hospital uses.

5. What is the false positive rate and how does it affect clinician workflow? An AI system with 95% sensitivity but 50% false positive rate will trigger excessive manual review, negating efficiency gains. Request data on positive predictive value (PPV) and false positive rates specific to your expected patient population.

6. How were edge cases and rare conditions handled during training? Ask vendors how they addressed severe cases (intracranial hemorrhage with mass effect, aortic dissection), atypical presentations, and imaging artifacts. What was the representation of rare conditions in the training dataset?

7. Does the model degrade gracefully or fail catastrophically? When an image is truly uninterpretable—severe motion artifact, quantum noise, extreme under/overexposure—what does the AI output? A good system reports low confidence or flags the case for manual review. A poor system may produce confidently wrong predictions.

8. Can you provide de-identified case examples where the AI correctly identified conditions that radiologists initially missed? This reveals whether the system has genuine diagnostic value or merely mimics radiologist decisions. Cases demonstrating detection of incidental findings or subtle abnormalities are particularly valuable.

Category 2: Regulatory Compliance & Security (8 Questions)

Compliance isn't negotiable. These questions verify the vendor's infrastructure for data handling, privacy, and audit.

9. What are your HIPAA compliance certifications and audit frequency? Request evidence of annual BAA (Business Associate Agreement) audits, current HIPAA compliance audit reports, and any remediation from prior findings.

10. Where is patient data stored and processed? On-premise only? Cloud? Which cloud provider? If cloud-based, is data encrypted in transit and at rest? Can you choose data residency (e.g., data stored exclusively in Malaysia if your hospital is subject to local data sovereignty requirements)?

11. What is your data retention and deletion policy? How long are diagnostic images and inference outputs retained? Can they be deleted on request or is there a mandatory retention period? Are deleted images cryptographically verified as unrecoverable?

12. What role-based access controls (RBAC) and audit logging are implemented? Who can access patient imaging data? Are all data access events logged and queryable? Can you generate audit trails for regulatory review?

13. How does the vendor handle adversarial attacks or prompt injection attempts? If the AI interprets text reports or clinical notes as part of its analysis, can attackers manipulate text inputs to bias AI predictions? Request documentation of adversarial robustness testing.

14. What is your incident response plan for data breaches? Who is notified, on what timeline? What are your financial obligations to the hospital if there's a breach? Request the vendor's cyber liability insurance certificates.

15. Are model weights and training data held in escrow in case of vendor bankruptcy or service discontinuation? This ensures your hospital retains access to the trained model even if the vendor fails.

16. What regulatory certifications do you hold outside HIPAA? CE mark (EU In Vitro Diagnostic Regulation)? FDA 510(k) clearance? Regional approvals in your jurisdiction? If you operate internationally, list all relevant regulatory statuses.

Category 3: Technical Integration & Deployment (8 Questions)

Integration is where AI projects fail most often. Demand specificity on DICOM, HL7/FHIR, and PACS workflows.

17. What DICOM version and tag support do you implement? Can the system ingest DICOM from your specific PACS vendor (GE, Philips, Siemens, etc.)? Does it preserve all relevant DICOM metadata? Can it output annotated DICOM objects (images with AI findings baked in)?

18. What is the image ingestion latency? How long between a technician acquiring an image and the AI generating a report? Real-time (under 2 minutes) is standard for urgent pathology flagging; batch processing (end-of-shift) is acceptable for quality assurance workflows.

19. How does the system integrate with your PACS? Does it pull images via DICOM Query/Retrieve? Does it push results back as DICOM Secondary Capture or structured reports? Is HL7/FHIR messaging supported for EHR integration?

20. What is the system's computational infrastructure? Does it run on-premise (your hardware) or cloud-hosted (vendor's infrastructure)? If on-premise, what are the GPU, CPU, RAM, and storage requirements? What is the per-study processing cost if cloud-based?

21. How does the system handle network outages or PACS downtime? If the vendor's cloud service is unavailable, does image analysis queue locally and reprocess when connectivity restores? Can the system operate offline?

22. What is the system's uptime SLA (service level agreement)? Most cloud services offer 99.9% uptime, meaning ~44 minutes of outage per month. Is this acceptable for your radiology workflow? What are the penalties if uptime falls below the SLA?

23. What monitoring and alerting does the vendor provide? Can you monitor system health, image processing success rates, and model performance drift in real time? Can you set alerts for accuracy degradation or unusual error patterns?

24. What is your disaster recovery and backup strategy? How frequently are backups taken? How long would restoration take? Is the backup site geographically separate from the primary infrastructure?

Category 4: Workflow Integration & Clinical Governance (8 Questions)

An AI system must align with how radiologists actually work. These questions ensure clinical integration, not just technical integration.

25. How are AI findings presented to radiologists? As a preliminary report the radiologist reviews? As highlighted regions on the image? As a grad-cam heatmap showing the model's visual attention? Each presentation method affects clinician trust and decision-making.

26. Can radiologists override, correct, or annotate AI predictions? If a radiologist disagrees with the AI, can they flag the case and provide corrected annotations? Does the system learn from corrections or are corrections logged passively?

27. How does the system handle prior-study comparison? Many diagnostic decisions depend on comparing the current study to prior studies. Does the AI incorporate prior studies in its analysis? Can radiologists review AI findings alongside priors?

28. What is the system's confidence scoring methodology? When the AI reports "97.9% confidence" in a finding, what does that mean? Bayesian probability? Model softmax output? Calibration against ground truth? A poorly calibrated confidence score of 97.9% might be overstated.

29. How does the system prioritize cases by urgency? Critical findings (tension pneumothorax, aortic dissection, intracranial hemorrhage) should be flagged immediately. Can the system implement urgency tiers and route critical cases to senior radiologists first?

30. What happens when the AI encounters an uninterpretable image? If an X-ray is severely motion-degraded or a CT is non-diagnostic, does the AI report "uninterpretable" or does it make a guess? Graceful failure is critical for liability and clinical safety.

31. How is the AI documented in the final radiology report? Is it transparent that AI was used? Does the report explain which findings are AI-detected vs. radiologist-detected? Transparency is both ethically important and legally prudent.

32. What audit logs are available for clinical review? If there's a diagnostic discrepancy or complication, can you retrieve exactly what the AI reported, when, and on what version of the model? Detailed audit trails are essential for medicolegal review.

Category 5: Support, Training & Scalability (8 Questions)

33. What is the vendor's support model? 24/7 phone support? Email ticketing with SLA? Dedicated account manager? Support hours are non-negotiable for a system your hospital depends on clinically.

34. What training do you provide for radiologists, technicians, and IT staff? Does the vendor conduct on-site training? Webinar-based? How many hours of training are included? Are there additional fees for ongoing training?

35. How frequently are model updates released and what is your update process? Monthly? Quarterly? When an update is released, how is it validated before deployment to production? Are radiologists involved in assessing whether an update improves or degrades performance?

36. What happens if a model update introduces performance degradation? Can you rollback to the prior model version? Is rollback automatic or manual? What is the rollback SLA?

37. How does the system scale to your hospital's volume? If you currently process 200 chest X-rays per day and expect to grow to 500, can the system scale? What are the scaling costs? Is there a volume-based pricing model?

38. What is the long-term pricing model? Per-study fees? Annual licensing? Shared revenue model? Request a 5-year cost projection including implementation, licensing, cloud infrastructure, training, and support.

39. What is your product roadmap for the next 2–3 years? Are you adding new imaging modalities or anatomical regions? Are you planning regulatory certifications (CE mark, FDA clearance) that would increase your market competitiveness?

40. Can you provide references from three hospitals with similar size and complexity? Speak directly with radiologists and IT staff who have implemented your system. Ask about actual timelines, real-world accuracy, integration challenges, and whether they would make the same choice again.

Evaluating and Scoring RFP Responses

Collecting 40 answers is only half the challenge; evaluating them requires a structured scoring framework. Here's how to weight vendor responses fairly:

Category	Weight	Rationale	What to Score
Clinical Accuracy & Validation	35%	The system must reliably detect pathology. Accuracy claims must be evidence-backed and applicable to your patient population.	Specificity of validation methodology, per-condition accuracy metrics, testing on your hospital's equipment
Regulatory Compliance & Security	25%	Data breaches and compliance failures are existential risks. Non-negotiable in regulated healthcare.	HIPAA certification, audit history, incident response plan, data residency options
Technical Integration & Deployment	20%	A perfect AI system is worthless if it can't integrate into your PACS and DICOM workflows.	DICOM compatibility, latency, uptime SLA, scalability
Workflow Integration & Clinical Governance	15%	The system must enhance radiologist decision-making, not add friction or liability.	Confidence score calibration, prior-study integration, radiologist override capability
Support & Scalability	5%	Good support and scalability reduce operational friction post-implementation.	Support SLA, training comprehensiveness, volume pricing structure

Within each category, assess responses on a 1–5 scale: 1 = vendor did not address the question, 2 = answer is vague or non-binding, 3 = answer meets minimum requirements, 4 = answer is detailed and evidence-backed, 5 = answer exceeds requirements with proactive commitments. Multiply each score by the category weight to calculate a weighted total. A vendor scoring 85/100 with comprehensive answers across all categories is better than one scoring 92/100 with weak responses on compliance and integration.

Honestly, I'd caution against pure numerical scoring. The numbers help, but they can obscure red flags. A vendor that scores 92/100 overall but refuses to disclose model bias testing or claims they can't provide references from existing customers should raise alarms. Reference calls often reveal critical integration challenges or support failures that RFP responses sanitize.

Red Flags: What NOT to Accept in Vendor Responses

Some vendor responses should disqualify them immediately. Watch for these patterns:

Vague accuracy claims. "Our AI achieves >95% accuracy" without specifying the condition, dataset, patient population, or validation methodology. Fractify is transparent: we claim 97.9% accuracy on brain MRI tumor detection because that figure is specific, peer-validated, and tied to a defined patient cohort.

Refusal to disclose training data composition. A vendor that claims "proprietary" and won't describe dataset demographics, disease prevalence, or equipment models is hiding potential bias or limited applicability.

No written SLA or penalty clauses. If uptime, accuracy, or integration timelines are not contractually binding with financial penalties, they're aspirational. Reputable vendors back their claims in writing.

Unavailable customer references. If a vendor can't provide three hospitals using their system in production, they're either new (higher risk) or hiding poor customer outcomes. Reference calls are non-negotiable.

Defensive responses about bias or limitations. A vendor that dismisses questions about demographic performance variation or edge case handling suggests they haven't seriously tested for these scenarios. Ask specifically about performance on minority populations and different imaging equipment.

Implementation Timeline After Vendor Selection

This 6-month timeline assumes moderate complexity—a typical hospital with mature PACS infrastructure and dedicated IT support. If your hospital lacks DICOM standardization or has multiple legacy PACS systems, add 1–2 months. If you're also implementing workflow changes (e.g., reorganizing prioritization protocols based on AI urgency tiers), add another month for organizational change management.

Why the RFP Matters More Than You Think

When we were validating Fractify's chest X-ray engine, we noticed that hospitals with disciplined procurement processes—those that asked detailed questions about bias testing, validation methodology, and integration challenges—had dramatically better outcomes than those that simply selected based on vendor reputation or lowest price.

The RFP isn't bureaucratic overhead; it's your primary tool for vendor accountability. When Databoost Sdn Bhd developed Fractify, we built the validation pipeline with the explicit goal of being able to answer all 40 of these questions credibly. Vendors that resist detailed questions or provide glossy brochures instead of specifics are signaling that their systems aren't ready for the scrutiny your hospital deserves.

Radiologists who've integrated Fractify into their PACS workflow tell me the same thing: they trust the system more when they understand exactly how it was validated and what its limitations are. They want to know that the 97.9% accuracy figure on brain MRI tumors is real, tested on their patient population, and comes with a commitment to monitor for degradation over time.

One honest caveat: I haven't seen enough long-term data (3+ years) on whether AI vendors sustain the accuracy rates they achieve during pilot implementation. Model drift is real—as clinical practice evolves, imaging protocols change, and equipment ages, AI performance can degrade. Your RFP should require vendors to commit to ongoing performance monitoring and transparent reporting of accuracy metrics over time. If a vendor resists this, that's a red flag.

Finding the Evidence: External Validation Sources

When evaluating vendor accuracy claims, require evidence from authoritative sources. The DICOM standard governs medical image file formats and metadata—vendors claiming DICOM compliance must demonstrate it technically. For clinical validation standards, consult Radiology journal, which publishes peer-reviewed AI validation studies with strict methodology requirements. Vendors should be able to cite published papers (not just internal reports) validating their systems.

Conclusion: The RFP as Your Guardrail

Procurement teams in healthcare face immense pressure to move fast—the radiology department is waiting, the C-suite is expecting ROI, and newer AI vendors promise faster implementations. The RFP is your chance to slow down, ask hard questions, and ensure the vendor you select can actually deliver the clinical accuracy, integration reliability, and regulatory compliance your hospital demands.

The 40 questions in this template represent thousands of hours of deployment experience across radiology networks. They've caught vendors overselling accuracy, underestimating integration timelines, and glossing over compliance gaps. They're designed to be vendor-agnostic—you can ask them of Fractify, of incumbent PACS vendors adding AI modules, or of any startup claiming to revolutionize radiology.

Your hospital's radiology department deserves a system that's been validated on your patient population, integrates cleanly into your PACS and EHR workflows, maintains clinical governance and transparency, and comes with vendor accountability backed by signed contracts and financial penalties. This RFP template helps you get there.

What is the difference between AI-detected findings and radiologist-detected findings in a report?

AI-detected findings are initially identified by the AI system, while radiologist-detected findings are identified by the reading radiologist. A clinically responsible report distinguishes between them: "AI-assisted detection of nodule (6mm, right upper lobe); confirmed by radiologist" vs. "Radiologist-identified pleural effusion." This transparency protects patients and supports medicolegal documentation.

What accuracy metrics should I demand from an AI radiology vendor?

Demand sensitivity, specificity, and AUC (area under the curve) reported separately for each condition, not an overall "97% accuracy" figure. Request per-condition metrics because accuracy varies dramatically—brain tumor detection may be 97.9% but rare intracranial hemorrhage subtypes may be 85%. Ensure validation was prospective (new patients post-training) and specify the patient demographics and equipment used.

How do I know if an AI vendor's clinical validation claims are credible?

Check whether the validation was peer-reviewed and published in a medical journal (Radiology, European Radiology, etc.), conducted by an independent third party, or done on your hospital's own data. Vendor-conducted validation on proprietary, undisclosed datasets is weaker evidence. Ask for the validation dataset composition, reference standard (biopsy, surgery, clinical outcome), and whether results were independently verified.

What should I specifically ask vendors about model bias and demographic fairness?

Ask vendors to report accuracy metrics stratified by patient demographics (age, gender, race/ethnicity), imaging equipment models, and disease prevalence. Ask what percentage of training data came from non-European populations and whether the system was tested on diverse populations. Request documentation of adversarial robustness testing and performance on edge cases like rare conditions or imaging artifacts.

How long does a realistic AI radiology implementation actually take?

A typical implementation takes 5–6 months: 1 month infrastructure and PACS connectivity, 1 month pilot integration testing, 1 month clinical validation and radiologist training, 1 month pilot deployment with radiologist subset, 1 month full rollout. Complex implementations with legacy PACS systems may take 8–10 months. Any vendor claiming faster than 4 months is likely cutting critical clinical validation steps.

What HIPAA and data governance language should an RFP require?

Require vendors to provide current HIPAA audit reports, annual BAA audits, and detailed incident response plans. Specify data residency (on-premise vs. cloud, which geographic region). Require audit logging of all data access, automated deletion of images after a specified retention period, and cryptographic verification of deletion. Request cyber liability insurance and clarify financial liability if there's a breach.

What happens medicolegally if an AI system makes a wrong diagnosis?

Liability depends on whether the incorrect finding was AI-identified or radiologist-detected. If AI missed a finding that the radiologist didn't catch, liability attaches to the radiologist per standard malpractice law. If the radiologist overruled a correct AI finding, liability is the radiologist's. If the AI confidently reported a false positive that the radiologist accepted, responsibility is shared. Clinical governance—transparent AI output in the report, logged radiologist overrides, honest confidence scores—is crucial for medicolegal protection.

How do I evaluate whether an RFP timeline from a vendor is realistic?

Request a detailed project plan with specific milestones, not ranges. A credible vendor provides: Week 1–2 infrastructure setup, Week 3–4 PACS connectivity and DICOM testing, Week 5–8 pilot phase with sample cases and radiologist feedback, Week 9–12 clinical validation, Week 13–16 training and phased rollout. Vague "4–12 weeks" claims suggest the vendor hasn't done large hospital implementations. Ask vendors for references and confirmed timelines from similar-sized hospitals.

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