Viz.ai Introduces AI-Powered Solution for Pulmonary Care Delivery

Viz.ai has launched the Viz Pulmonary Suite, an integrated artificial intelligence (AI)-powered solution in the Viz.ai enterprise platform that is built to help health systems streamline care delivery for pulmonary conditions. The suite brings acute and chronic pulmonary workflows together in a single solution, giving clinicians a more streamlined way to steer patients toward guideline-directed care.

With 55%-65% of potential in-network referrals lost due to health system leakage,¹ improving continuity across the patient journey becomes a critical challenge. The Viz Pulmonary Suite, which integrates seamlessly with EHR systems, aims to help pulmonary teams improve retention and outcomes by combining context-aware patient summaries and guideline surfacing with specialized tools for major pulmonary conditions—including chronic obstructive pulmonary disease (COPD), lung nodules, and pulmonary embolism—to prevent missed diagnoses and treatment delays. Early findings show promising results for Viz.ai’s network of healthcare partners. The Viz Pulmonary Suite includes Viz PE, which was shown to reduce time to treatment from 1.75 days to 0.56 days and lowered in-hospital mortality among high-risk PE patients in a single-center study.²

“Pulmonary care breaks down not because we lack treatments, but because patients may be lost between moments of care,” Viz.ai Chief Medical Officer Tim Showalter, M.D., stated. “Across COPD, pulmonary embolism, and lung nodules, critical findings are often identified but not acted on in time due to fragmented workflows. The Viz Pulmonary Suite brings identification, coordination, and follow-through into a single solution that is fully integrated with the EHR, helping clinicians intervene earlier and manage these patients with greater consistency.”

Breakdowns in identification and follow-up are common for the more than 34 million adults living with some form of chronic lung disease. Nearly half of COPD patients hospitalized for acute exacerbations are readmitted within 30 days.³ Up to 71% of clinically significant lung nodules go without appropriate follow-up.⁴ And in pulmonary embolism, up to 79% of incidental cases are missed on initial imaging,⁵ even though 67% of those missed emboli are clinically significant.⁶

“As patients move through imaging, the ER, and acute care visits, their journey often becomes fragmented. Consistent coordination can be challenging, especially for conditions like chronic obstructive pulmonary disease, pulmonary embolism, and lung nodules that depend on well-managed care pathways,” Monogram Health Chief Pulmonologist Lakshman Swamy, M.D., said. “Solutions like the Viz Pulmonary Suite provide clinical teams with connected AI-enabled workflows to help identify potential patients earlier, act more efficiently, and support more consistent follow-up across the care continuum.”

Viz.ai builds and deploys AI-powered care pathways and helps doctors perform their work. The Viz Platform is deployed in 2,000 U.S. hospitals and trusted by many life sciences companies. The platform combines real-time, multimodal clinical data with deep clinician engagement to detect disease earlier, coordinate care teams, and help ensure patients receive the right treatment faster. Viz.ai was the first company to be awarded CMS reimbursement for AI and is ranked the top Healthcare AI Platform by hospitals and health systems in the Black Book Research survey.

References
¹ Roy S. 30+ Patient Referral Statistics: Why Your System Is Bleeding Money. Dialog Health. Published March 17, 2025. Updated August 26, 2025.
² Meredith J, Muck P, Reichard A, Fellner AN. PERT activation with AI improves time to intervention and survival in high-risk pulmonary embolism. Poster presented at: PERT Consortium meeting; San Diego, CA; 2025. Results are from a single-center study and may not be generalizable.
³ Shah T, Press VG, Huisingh-Scheetz M, White SR. COPD Readmissions: Addressing COPD in the Era of Value-Based Health Care. CHEST. 2016.
⁴ Enhancing the earlier detection of lung cancer: effective management of incidental pulmonary nodules. Lung Cancer Policy Network. Accessed January 13, 2026. https://www.lungcancerpolicynetwork.com/app/uploads/Enhancing-the-earlier-detection-of-lung-cancer-effective-management-of-incidental-pulmonary-nodules.pdf
⁵ Wiklund P, Medson K. Use of a deep learning algorithm for detection and triage of cancer-associated incidental pulmonary embolism. Radiology: Artificial Intelligence. 2023;5(6):e220286. doi:10.1148/ryai.220286
⁶ Wildman-Tobriner B, Ngo L, Mammarappallil JG, Konkel B, Johnson JM, Bashir MR. Missed incidental pulmonary embolism: harnessing artificial intelligence to assess prevalence and improve quality improvement opportunities. J Am Coll Radiol. 2021;18:992-999.