Pulmonary experts from Harvard's Mass General Hospital are presenting clinical data from multi-site experiences with IMVARIA's Fibresolve, the first FDA-authorized AI adjunctive diagnostic service for lung fibrosis, at the ATS 2025 International Conference in San Francisco this week.
The Berkeley-based health tech company IMVARIA Inc. developed Fibresolve as a diagnostic referral service where pulmonologists can send cases for AI-supported evaluation of suspected Interstitial Lung Disease (ILD) and Idiopathic Pulmonary Fibrosis (IPF). The service received FDA authorization in early 2024 after being designated as an FDA Breakthrough device.
"We're excited that our clinical users are sharing real-world experience with Fibresolve at the ATS Conference," said Joshua Reicher, MD, Co-founder and CEO of IMVARIA. "At IMVARIA, we've taken a different approach to AI – one that makes it far easier for pulmonologists to benefit from this new technology without changing workflows or installing complex systems."
Innovative Approach to Clinical AI Implementation
Fibresolve was designed by physician-engineers to meet rigorous medical standards while remaining accessible to clinicians. The service operates through a centralized platform that helps guide safe, non-invasive diagnoses for patients with suspected pulmonary fibrosis.
A notable achievement for the technology is its status as the first FDA Breakthrough-Designated AI diagnostic tool with simultaneously adopted CPT billing codes by the American Medical Association (AMA) in any disease area. This dual recognition addresses both regulatory and reimbursement challenges that often hinder adoption of innovative diagnostic technologies.
Dr. Reicher, who co-founded IMVARIA with Dr. Michael Muelly in 2019, emphasized their practical approach to AI implementation: "As practicing medical doctors, my co-founder and I designed Fibresolve to meet the highest medical standards, deliver new insights, and make it easy for clinicians to use AI with confidence and minimal burden. We're proud to see that approach working in real clinical settings."
Expanding AI Portfolio for Pulmonary Diagnostics
Beyond Fibresolve, IMVARIA is presenting data on two additional AI solutions in its pulmonary portfolio at the ATS conference:
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ScreenDx: FDA-cleared in 2025, this is the first AI technology authorized to assess interstitial lung findings compatible with ILD. The poster presentation will share data on "Automated AI Detection of Interstitial Lung Disease by Computed Tomography (CT) in the COPDGene Trial," including a subanalysis of accurately detected cases.
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Bronchosolve: Currently under research investigation, this investigational tool is designed to support more accurate assessment of indeterminate lung nodules. Two poster presentations will showcase data on "Closed Loop, Full Automation of Suspicious Lung Nodule Risk Assessment with AI in Screening Cases" and an "Age-Stratified Subanalysis" of the technology.
Clinical Impact and Future Directions
Interstitial Lung Disease and Idiopathic Pulmonary Fibrosis represent significant diagnostic challenges for pulmonologists. IPF, in particular, is a progressive and life-threatening condition characterized by scarring of lung tissue that makes diagnosis and early intervention critical for patient outcomes.
IMVARIA's AI-driven approach aims to transform clinical decision-making into data science, potentially enabling earlier and more accurate diagnoses. The company was founded by physician-engineers from Google and Stanford University and operates its AI Lab with automated, machine-learning algorithm technology.
The multiple presentations at ATS 2025 reflect IMVARIA's mission to "empower clinicians to make the best decisions through clinically meaningful AI," according to company materials. The conference, focused on respiratory diseases, is being held May 16-21, 2025, in San Francisco.
For pulmonologists managing patients with suspected ILD or IPF, these technologies may offer new pathways to improve diagnostic accuracy while maintaining existing clinical workflows – addressing a key barrier to adoption of AI tools in specialized medical practice.
