AI Vendor Selection, Implementation, and Ongoing Quality Monitoring for Radiology Practices

Radiology is moving rapidly toward AI-assisted and AI-generated workflows, including FDA-cleared point solutions, automated triage tools, and emerging vision language model (VLM)–driven preliminary reports. This creates a new challenge for practice leaders: deciding which path to take, how to implement it safely, and how to ensure quality does not degrade over time.

Ora Informatics helps radiology practices evaluate AI options, implement them pragmatically, and continuously monitor performance for quality, drift, and bias.

Think: a Datadog-style monitoring and governance layer for clinical AI. Not vendor marketing metrics, but ongoing operational visibility.

Contact Us or email ty@orainformatics.com

The Decisions Radiology Leaders Now Face

  • Off-the-shelf, FDA-cleared algorithms for detection, triage, or quantification
  • One-off or niche algorithms targeting specific modalities or bottlenecks
  • Internal or custom models, including VLM-based approaches used for workflow support or prelim drafting

What I Help You Do

  • Choose the right AI strategy, not just a vendor
  • Select and pressure-test vendors and models
  • Implement AI without disrupting throughput
  • Establish defensible quality baselines
  • Monitor performance, drift, and bias over time
  • Build governance leadership can stand behind

What This Looks Like in Practice

Engagement 1: AI Strategy and Vendor Selection

Strategy framework, vendor shortlisting, structured scorecards, and a decision memo with explicit tradeoffs.

Engagement 2: Implementation and Rollout Support

Workflow mapping, staged rollout, adoption tracking, and early quality measurement.

Engagement 3: Ongoing Quality and Drift Monitoring

Monthly monitoring, audits, accountability reviews, and leadership-ready reporting.

The Metrics That Matter

  • Clinically meaningful discrepancy rates
  • Radiologist edit distance and overrides
  • Prelim versus final deltas
  • Turnaround time by site and shift
  • Failure modes by cohort and protocol
  • Stability and degradation trends

Contact

If you want a clear-eyed assessment of your AI options or need real quality instrumentation:

Contact Us
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