In this exclusive interview, Avez Rizvi, MD, CEO of RADPAIR, shares insights into the future of AI in radiology and the role RADPAIR aims to play in creating an AI-driven, patient-focused healthcare environment.
AXIS Imaging News: How does RADPAIR’s generative AI LLM model specifically address the clerical burden faced by radiologists, and what kind of time savings can users expect?
Avez Rizvi: RADPAIR’s generative AI streamlines documentation by auto-filling accurate, context-sensitive reports, reducing the need for manual data entry. Radiologists can speak naturally, without modifying grammar or adding punctuation, enabling more intuitive interactions, and yielding approximately 30%-40% time savings—sometimes more, depending on additional AI inputs like diagnostic classifiers.
Furthermore, RADPAIR saves radiologists time by eliminating non-value-added tasks, such as repeatedly switching to a search engine to look up classifications or guidelines. With our PAIR Insights feature, integrated with Radiopaedia, users can simply instruct RADPAIR to “insert the guideline” or use any natural phrase, and the system will pull the relevant guideline directly from Radiopaedia’s knowledge base into the report. This enhances reporting efficiency while maintaining high standards of patient care and clinical accuracy, ensuring quality without sacrificing speed.
AXIS: With the recent launch of RADPAIR 2.0 powered by Groq’s LPU AI inference technology, how do you envision this product transforming radiology workflows?
Rizvi: RADPAIR 2.0 introduces a new speech engine, designed to be the fastest and most accurate cloud-based radiology speech engine available. With a word error rate significantly lower than legacy solutions, our engine—enhanced by Groq’s LPU technology—delivers unmatched speed and precision, reducing report generation time by up to 50%. For plain film cases, results return in less than two seconds, while CT and MRI cases are processed in under 5 seconds. This efficiency enables us to handle complex cases seamlessly, providing radiologists with faster turnaround times and enhanced reporting accuracy
AXIS: Can you explain the key advantages of “instant inference” and how it enhances report pre-processing and intelligent AI editing for radiologists?
Rizvi: Instant inference enables radiologists to receive reports faster by integrating insights from diagnostic classifiers, system metadata, and multimodal imaging data. Groq’s LPU-AI inference technology further enhances report preprocessing, allowing radiologists to make quick, spoken edits to reports that are processed in near real-time—making reports smarter, cohesive, and easier to refine in real time.
AXIS: How do you ensure that RADPAIR’s AI models align with the unique needs and workflows of radiologists, particularly in terms of accuracy and user experience?
Rizvi: RADPAIR is developed by radiologists, with each workflow refined in close collaboration with radiology professionals to address specific edge cases across various study types. Our fine-tuned models tackle major data science challenges, such as hallucinations and the omission of complex findings, using proprietary methods like our PAIRCision feature. This innovation ensures high report accuracy, aligning RADPAIR’s AI models with radiologists’ unique needs for precision and user experience.
AXIS: What role do you see AI playing in reducing radiologist burnout, and how does RADPAIR contribute to improving both efficiency and job satisfaction for radiologists?
Rizvi: For decades, radiologists have had to juggle diagnosing findings on imaging with manually inputting them into rigid, form-based reporting systems. Legacy systems require radiologists to dictate perfectly structured sentences and click through multiple fields, interrupting the natural flow of diagnosis and contributing to burnout as this process is repeated thousands of times across hundreds of cases daily. RADPAIR changes this by acting as an AI-driven assistant that handles reporting—allowing radiologists to describe findings naturally, without concern for perfect phrasing or grammar. This seamless, intuitive process refocuses attention on diagnostic work, improving both efficiency and job satisfaction.
AXIS: As RADPAIR prepares for the RSNA conference, what key developments or advancements can radiologists expect from your team, and how do these innovations align with the future of AI in healthcare?
Rizvi: At RSNA, we’re excited to showcase RADPAIR’s vision for the future of radiology—a shift away from the outdated, rigid workflows that have burdened radiologists for decades. Instead of reading cases in the legacy style, radiologists will become conductors of an AI-powered workflow, focusing on patient diagnostics while the AI manages non-value-added tasks like report editing and formatting.
A key RSNA highlight, Wingman, exemplifies this approach. With Wingman, radiologists can speak naturally to RADPAIR, which responds and makes real-time edits to their findings, confirming each action—like having a responsive assistant at their side. This keeps radiologists immersed in the images, confident their observations are accurately reflected in the report. By the time the case is ready for final review, everything is set for a quick check and signature. This transformative shift brings radiology closer to an AI-driven, patient-focused future.
