SCREENDX and FIBRESOLVE:
AI for Lung Fibrosis
ScreenDx, AI to Identify ILD Automatically:
ScreenDx uses AI to automatically analyze medical data for findings suggestive of interstitial lung disease (ILD). ScreenDx is designed to be supplementary for current standard-of‐care workflows, providing adjunctive information as part of a referral pathway to an appropriate, qualified clinician.
Fibresolve, AI to Better Distinguish IPF from other ILDs:
Fibresolve is FDA Breakthrough Designated and Authorized to serve as an adjunct in the diagnosis of idiopathic pulmonary fibrosis (IPF) prior to invasive testing. Fibresolve helps pulmonologists and other qualified clinicians as they work through new diagnosis of patients with suspected ILD including IPF. Fibresolve works like a lab test, but without a procedure, analyzing non-invasively collected data.
Supporting Skilled Clinicians:
Fibresolve and ScreenDx are intended to be used only by clinicians qualified in the care of lung disease, in conjunction with the patient’s clinical history, symptoms, and other diagnostic tests, as well as the clinician’s professional judgment.
the platform
The AI Lab
IMVARIA's AI Lab is a centralized, cloud-based digital lab that operates in a broadly similar fashion to that of a specialty CLIA lab–except that we handle data rather than blood or tissue samples. Clinicians, health systems, and clinical trialists transmit cases to the Lab and IMVARIA analyzes the cases and returns reports with results in minutes. The system capacity crosses imaging modalities (eg. CT, MRI), organ systems, and data types (lab results), and follows international standards for quality compliance (eg. ISO 13485). Projects in new disease areas take weeks to months instead of years to complete, and modern architecture supports deep learning, fusion, and multi-dimensional / multi-modal models.
clinicians and health systems
Clinical Assessment
IMVARIA is on the front lines of utilizing digital data to improve patient lives. Leveraging diverse clinical testing and imaging datasets to identify distinct disease signatures addresses many of the challenges with standard diagnostic methods.
Additionally, quantitative-only techniques, including many prognostic calculators, rarely capture the complexities and variance of disease phenotypes. With de-identified data from numerous sources, our systems drive new insights in predicting outcomes and helping better understand risks.
life sciences
AI Biomarkers in Clinical Trials
Through analysis of wide-ranging data types and data sources, including patient registries, clinical trials, and public datasets, the company’s next-generation data science optimizes for consistent and accurate disease assessments and predictions. Whether it is optimizing study enrollment characteristics, better risk-stratifying patients at trial enrollment, or developing predictions for treatment response, IMVARIA can help.
A New Category of Clinical AI
Led by dual physician-engineers from Google and Stanford Health Care, IMVARIA’s pioneering work with AI biomarkers is redefining how to approach high impact diseases more effectively.
Redefining
Clinical AI
IMVARIA's platform analyzes clinical testing and imaging data to generate AI-only clinical results — driving better understanding of physiology and disease noninvasively.
MAKING Better
decisions
IMVARIA empowers physicians to make the best-informed clinical decisions through the precise application of our AI platform, taking on the greatest challenges in high impact disease states.
Mastering SaMD
IMVARIA's Software-as-Medical-Device (SaMD) tools are transforming the use of clinical AI for clinical decision-making and therapeutic development-focused clinical trials.
About IMVARIA
Based in Berkeley, CA, IMVARIA invented a new category of clinical AI that generates physiologic signatures from clinical testing and imaging data. Founded by physician-engineers from Stanford and Google, the company is deploying it across serious diseases for clinicians, health systems, and biopharma partners.
CO-FOUNDER and CEO
DR. JOSHUA REICHER
Dr. Reicher is CEO and Chairman at IMVARIA. Prior to co-founding IMVARIA in 2019, Dr. Reicher served as a clinical lead at Google's medical AI group, as an analyst in healthcare investing, and as a faculty member at Stanford Health Care and Palo Alto VA in the Dept of Radiology.
CO-FOUNDER and CTO
DR. MICHAEL MUELLY
Dr. Muelly is CTO of IMVARIA. Prior to co-founding IMVARIA in 2019, Dr. Muelly served as a Product Manager at Google Cloud's Healthcare group, an AI research fellow at Google, founder of ClariPACS, and as a faculty member at Stanford Health Care in the Dept of Radiology.
COMMERCIAL ADVISOR
MR. JOHN HANNA
Mr. John Hanna is the lead commercial advisor at IMVARIA. Mr. Hanna is CEO at CareDx, a publicly traded diagnostics company. Mr. Hanna was previously CEO at Apton Biosciences and prior to that CCO at Veracyte, a publicly traded diagnostics company.
CLINICIANS + ENGINEERS BUILD BETTER CARE
Our team blends rare clinical, technical, and operational expertise, spanning medicine, machine learning, and real-world healthcare operations — united by a belief that AI can meaningfully transform patient care.
PRESS
October 6, 2021
Transforming Diagnostics with Machine Learning and Digital Biomarkers
As we’ve seen with the COVID-19 pandemic, diagnostics deliver vital information that enable health providers to properly triage patients and provide the best treatment according to their illness...
June 10, 2021
The UCSF Rosenman Institute Announces the 2021 Rosenman Innovators
The UCSF Rosenman Institute is proud to announce the 2021 cohort of the Rosenman Innovators, a group of early-stage health technology companies chosen for their innovative technologies...
June 15, 2021
MedTech Innovator selects 50 startups for showcase and accelerator program
MedTech Innovator today revealed the 50 startups it selected to partake in its flagship showcase and accelerator program. The four-month program offers the startups visibility and access...
NEWS
January 7, 2026
IMVARIA Announces Strategic Investments from Leading Healthcare CompaniesBERKELEY, Calif. – January 7, 2026 − IMVARIA Inc., a health tech company pioneering AI-driven digital biomarker solutions, today announced strategic investments from new and existing investors to accelerate IMVARIA’s growth and fuel innovation in AI-driven digital healthcare. InHealth Ventures (IHV), an early-stage fund linked to leading screening and diagnostics provider InHealth Group , led the funding, with participation from the Labcorp Venture Fund...
November 18, 2025
Viz.ai and IMVARIA Partner to Expand AI Capabilities for Interstitial Lung DiseaseSAN FRANCISCO — November 18, 2025 — Viz.ai, the leader in AI-powered disease detection and care coordination, today announced a strategic partnership with IMVARIA Inc., a health tech company pioneering AI-driven digital biomarker solutions. This collaboration expands Viz.ai’s pulmonary portfolio and introduces new AI capabilities that can assess for interstitial lung findings compatible with interstitial lung disease (ILD)...
October 20, 2025
IMVARIA’s AI-Powered, Non-Invasive Bronchosolve Solution Meets Primary Endpoints for Lung Cancer Diagnosis in Pivotal VIRTUAL-BRONCH StudyBERKELEY, Calif. – October 20, 2025 − IMVARIA Inc., a health tech company pioneering AI-driven digital biomarker solutions, today announced that the company’s new platform Bronchosolve, an AI-powered diagnostic solution for assessment of suspicious pulmonary nodules met the primary endpoints in the pivotal VIRTUAL-BRONCH Study...
September 3, 2025
IMVARIA Awarded Phase I NIH SBIR Grant to Extend the Company’s AI-powered Adjunctive Diagnostic Service into OncologyBERKELEY, Calif.--(BUSINESS WIRE)--IMVARIA Inc., a health tech company pioneering AI-driven digital biomarker solutions, today announced that it has been awarded a Phase I Small Business Innovation Research (SBIR) grant from the National Institutes of Health (NIH). The funding will support IMVARIA’s development of a new test for more accurate malignancy assessment...
June 10, 2025
IMVARIA Announces Strategic Distribution Partnership with Medical Horizons to Expand Presence in MENABERKELEY, Calif.--(BUSINESS WIRE)--IMVARIA Inc., a health tech company pioneering AI-driven digital biomarker solutions, today announced a strategic distribution partnership with Medical Horizons, a premier AI-powered medical device distributor based in Florence, Italy. This partnership makes it easier for pulmonologists based in the Middle East and North Africa (MENA) region...
May 18, 2025
IMVARIA Reports Multi-Site Clinical Experience with FDA-Authorized AI Diagnostic Service for Idiopathic Pulmonary Fibrosis at ATS 2025BERKELEY, Calif.--(BUSINESS WIRE)--IMVARIA Inc., a health tech company pioneering AI-driven digital biomarker solutions, today reported results from multi-site clinical experiences with IMVARIA’s diagnostic referral service, where pulmonologists send cases for AI-supported diagnostic evaluation of suspected Interstitial LungDisease (ILD) and Idiopathic Pulmonary Fibrosis (IPF)...
January 13, 2025
IMVARIA Receives 510(k) Clearance for ScreenDx, a First-of-its-Kind Screening Algorithm to Assess for Interstitial Lung DiseaseBERKELEY, Calif.--(BUSINESS WIRE)--IMVARIA Inc., a health tech company pioneering AI-driven digital biomarker solutions, a health tech company pioneering AI-driven digital biomarker solutions, today announced its second FDA authorization, securing 510(k) clearance from the U.S. Food and Drug Administration (FDA)...
January 16, 2024
IMVARIA Announces FDA De Novo Marketing Authorization of Fibresolve, an AI Biomarker in Lung Fibrosis, and the Adoption of Novel CPT Billing Codes by the American Medical AssociationBERKELEY, Calif.--(BUSINESS WIRE)--IMVARIA Inc., a health tech company pioneering AI-driven digital biomarker solutions, today announced that the U.S. Food and Drug Administration (FDA) has granted marketing authorization for the use of Fibresolve, a digital biomarker solution that uses artificial intelligence (AI)...
November 1, 2023
IMVARIA Enters into Know-how Agreement with Mayo Clinic to Improve the Understanding of Cancer through AI Digital BiomarkersBERKELEY, Calif.--(BUSINESS WIRE)--IMVARIA Inc., a health tech company pioneering AI-driven digital biomarker solutions, today announced a collaboration through a know-how agreement with Mayo Clinic to develop AI designed to significantly improve the analysis and understanding of cancer...
May 18, 2023
IMVARIA Announces Data Demonstrating Company’s Non-Invasive Digital Biomarker for Predicting Mortality in Interstitial Lung DiseasesBERKELEY, Calif.--(BUSINESS WIRE)--IMVARIA Inc., a health tech company pioneering AI-driven digital biomarker solutions, today announced data from a study evaluating the company’s lead non-invasive digital biomarker Fibresolve showing the tool’s supplemental ability to predict mortality...
April 18, 2023
IMVARIA Announces Presentations at American Thoracic Society International Conference...IMVARIA Inc., a software-as-medical-device (SaMD) company pioneering AI-driven digital biomarker solutions, today announced that four presentations utilizing the company’s technology will be featured at the upcoming American Thoracic Society 2023 International Conference (ATS 2023)...
August 16, 2022
Aiming Precision Medicine at Chronic DiseasesChronic diseases cover a wide range of medical conditions from asthma and diabetes to gastrointestinal and heart problems. As the U.S. Center for Disease Control and Prevention (CDC) notes: “Chronic diseases are defined broadly as conditions that last 1 year or more...
October 6, 2021
Transforming Diagnostics with Machine Learning and Digital BiomarkersAs we’ve seen with the COVID-19 pandemic, diagnostics deliver vital information that enable health providers to properly triage patients and provide the best treatment according to their illness...
June 15, 2021
MedTech Innovator selects 50 startups for showcase and accelerator programMedTech Innovator today revealed the 50 startups it selected to partake in its flagship showcase and accelerator program.The four-month program offers the startups visibility and access to leading manufacturers, providers, investors and other industry stakeholders...
June 10, 2021
The UCSF Rosenman Institute Announces the 2021 Rosenman InnovatorsThe UCSF Rosenman Institute is proud to announce the 2021 cohort of the Rosenman Innovators, a group of early-stage health technology companies chosen for their innovative technologies and patient impact.2021 is the sixth year for the...
IMVARIA Publications and Abstracts
- Taha A, Kheir F. Integration of closed-loop fully-automated AI model with clinician assessment for lung nodule stratification: a multi-reader study. Respir Investig. 2026;64(5):101507. [link]
- Tambe AP, Boente RD, George G, Kheir F, Tahamtani Omran O, Selvan KC. Clinical utility of an FDA-authorized artificial intelligence imaging platform in interstitial lung disease diagnosis. Diagnostics. 2026;16(15):2445. [link]
- Tahamtani-Omran O, Kalra A, Muelly M, Reicher J, Ahmed R. Paired validation of a multimodal vision-transformer system for the non-invasive diagnosis of idiopathic pulmonary fibrosis. Diagnostics. 2026;16(15):2398. [link]
- Taha A, Kalra A, Muelly M, Reicher J, Kheir F. Sequential integration of Bronchosolve for indeterminate pulmonary nodule stratification: diagnostic gains in moderate-risk nodules [abstract]. Am J Respir Crit Care Med. 2026;212(suppl 1):aamag162.3792. [link]
- Taha A, Kalra A, Muelly M, Reicher J, Kheir F. Clinician performance in indeterminate pulmonary nodule risk stratification using Bronchosolve: a multi-reader multi-case study [abstract]. Am J Respir Crit Care Med. 2026;212(suppl 1):aamag162.3791. [link]
- Liu H, Grewal H, Reicher J, Omran O, Batra H. Risk stratification of incidental lung nodules: a fully automated approach to CT imaging analysis [abstract]. Am J Respir Crit Care Med. 2026;212(suppl 1):aamag162.3794. [link]
- Tambe A, Selvan KC. Clinical utility of Fibresolve, an imaging artificial intelligence tool, in the diagnosis of idiopathic pulmonary fibrosis [abstract]. Am J Respir Crit Care Med. 2026;212(suppl 1):aamag162.2459. [link]
- Taha A, Muneer MS, Kalra A, Muelly M, Reicher J. Performance validation of a closed-loop fully automated AI model for lung nodule stratification in screening cases. Respir Investig. 2026;64(2):101373. [link]
- Batchu S, Callahan S, Kalra A. Automated AI detection of interstitial lung disease on CT in the COPDGene trial: sub-analysis of COPD vs non-COPD patients. Eur Respir J. 2025;66(suppl 69):PA4033. [link]
- Batchu S, Kalra A, Muelly M, Reicher J, Taha A. Closed loop, fully automated lung nodule risk assessment AI software: subanalysis of former vs current smokers. Chest. 2025;168(4)(suppl):A4776-A4777. [link]
- Chen SJ, Kalra A, Muelly M, Reicher J, Callahan S, Scholand MB, Kulkarni T. Automated artificial intelligence detection of early or under-diagnosed interstitial lung disease by computed tomography in the COPDGene trial. Respir Med. 2025;250:108545. [link]
- Callahan SJ, Scholand MB, Kalra A, Muelly M, Reicher JJ. Multi-modal machine learning classifier for idiopathic pulmonary fibrosis predicts mortality in interstitial lung diseases. Respir Investig. 2025 Aug 6;63(5):1012-1017. doi: 10.1016/j.resinv.2025.07.021. [link]
- Uribe J, Kalra J, Muelly M, Reicher J, Kheir F. Clinical experience with the first FDA-authorized artificial intelligence tool in interstitial lung disease and idiopathic pulmonary fibrosis [abstract]. Am J Respir Crit Care Med. 2025;211:A1709. [link]
- Batchu S, Callahan SJ, Scholand MB, Kalra A, Muelly M, Reicher J, Kulkarni T. Automated AI detection of interstitial lung disease by computed tomography (CT) in the COPDGene trial; subanalysis and characteristics of accurately detected cases [abstract]. Am J Respir Crit Care Med. 2025;211:A2087. [link]
- Taha A, Reisenauer J, Kalra A, Muelly M, Reicher J. Closed loop, full automation of suspicious lung nodule risk assessment with AI in screening cases [abstract]. Am J Respir Crit Care Med. 2025;211:A5226. [link]
- Batchu S, Kalra A, Muelly M, Reicher J, Taha A. Age-stratified subanalysis of a closed loop, fully automated lung nodule risk assessment AI software [abstract]. Am J Respir Crit Care Med. 2025;211:A4823. [link]
- Toulomes N, Gagianas G, Bradley J, et al. ScreenDx, an artificial intelligence-based algorithm for the incidental detection of pulmonary fibrosis. AJMS. 2025; pre-proof published online February 27, 2025. [link]
- Callahan SJ, Scholand MB, Kalra A, Muelly M, et al. Expert Center External Validation of a CT-Based Deep Learning Algorithm to Predict Diagnosis of Idiopathic Pulmonary Fibrosis. Chest. 2024;166(4):A3312-A3313. [link]
- Selvan KC, Reicher J, Muelly M, Kalra A, Adegunsoye A. Machine learning classifier is associated with mortality in interstitial lung disease: a retrospective validation study leveraging registry data. BMC Pulm Med. 2024 May 23;24(1):254. [link]
- Selvan K, Reicher J, Muelly M, Kalra A, Adegunsoye A. Machine learning classifier predicts mortality in interstitial lung disease: a validation study. Poster presented at: 2024 American Thoracic Society Conference; May, 2024; San Diego, CA. [link]
- Bradley J, Kalra A, Muelly M, Reicher J. The impact of CT manufacturer and slice thickness on the ability of ScreenDx, an AI-based algorithm, to detect incidental pulmonary fibrosis. Poster presented at: 2024 American Thoracic Society Conference; May, 2024; San Diego, CA. [link]
- Callahan S, Scholand MB, Kalra A, Muelly M, Reicher J. Multi-modal machine learning classifier for idiopathic pulmonary fibrosis predicts mortality in interstitial lung diseases. Poster presented at: 2024 American Thoracic Society Conference; May, 2024; San Diego, CA. [link]
- Kulkarni T, Kalra A, Muelly M, Reicher J. Automated AI detection of clinical interstitial lung disease by CT in the COPDGene trial. Poster presented at: 2024 American Thoracic Society Conference; May, 2024; San Diego, CA. [link]
- Moran-Mendoza O, Singla A, Kalra A, Muelly M, Reicher JJ. Computed tomography machine learning classifier correlates with mortality in interstitial lung disease. Respir Investig. 2024 May 20;62(4):670-676. [link]
- Ahmad Y, Mooney J, Allen IE, et al. A machine learning system to indicate diagnosis of idiopathic pulmonary fibrosis non-invasively in challenging cases. Diagnostics (2024). [link]
- Chang M, Reicher JJ, Kalra A, et al. Chang M, Reicher JJ, Kalra A, Muelly M, Ahmad Y. Analysis of validation performance of a machine learning classifier in interstitial lung disease cases without definite or probable usual interstitial pneumonia pattern on CT using clinical and pathology-supported diagnostic labels. J Imaging Inform Med. 2024 Feb;37(1):297-307. [link]
- Bradley J, Huang J, Kalra A, Reicher J. External validation of Fibresolve, a machine-learning algorithm, to non-invasively diagnose idiopathic pulmonary fibrosis. Am J Med Sci. 2023 Dec 24:S0002-9629(23)01475-1. doi: 10.1016/j.amjms.2023.12.009. [link]
- Bradley J, Huang J, Kalra A, Reicher J. External validation of Fibresolve, a machine-learning algorithm, to non-invasively diagnose idiopathic pulmonary fibrosis. Poster presented at: 2023 Pulmonary Fibrosis Foundation Summit; November, 2023; Orlando, FL.
- Maddali MV, Kalra A, Muelly M, Reicher JJ. Development and validation of a CT-based deep learning algorithm to augment non-invasive diagnosis of idiopathic pulmonary fibrosis. Respir Med. 2023 Oct 13;219:107428. [link]
- Toulomes N, Kalra A, Bradley JA, Gagianas G, Muelly M, Reicher J. Artificial intelligence in incidental detection of lung fibrosis by computed tomography. Oral presentation at: 2023 CHEST Conference; October, 2023; Honolulu, HI. [link]
- Selvan KC, Kalra A, Reicher J, Muelly M, Adegunsoye A. Computer-Aided Pulmonary Fibrosis Detection Leveraging an Advanced Artificial Intelligence Triage and Notification Software. J Clin Med Res. 2023 Sep;15(8-9):423-429. [link]
- Maddali M, Kalra A, Muelly M, Reicher J. Development and validation of a CT-based deep learning algorithm to augment non-invasive diagnosis of idiopathic pulmonary fibrosis. Poster presented at: 2023 American Thoracic Society Conference; May, 2023; Washington DC. [link]
- Ahmad Y, Li J, Mooney J, Allen I, Seaman J, Kalra A, Muelly M, Reicher J. Predicting interstitial pulmonary fibrosis using a machine learning classifier in cases without definite or probable usual interstitial pneumonia pattern on computed tomography. Poster presented at: 2023 American Thoracic Society Conference; May, 2023; Washington DC. [link]
- Ahmad Y, Mooney J, Allen I, Seaman J, Kalra A, Muelly M, Reicher J. A machine learning system to predict diagnosis of idiopathic pulmonary fibrosis non-invasively in challenging cases. Poster presented at: 2023 American Thoracic Society Conference; May, 2023; Washington DC. [link]
- Moran Mendoza O, Reicher J, Singla A. Chest computed tomography machine learning classifier for idiopathic pulmonary fibrosis predicts mortality in interstitial lung diseases. Oral presentation at: 2023 American Thoracic Society Conference; May, 2023; Washington DC. [link]
- Jonas A, Muelly M, Gupta N, Reicher JJ. Machine learning to distinguish lymphangioleiomyomatosis from other diffuse cystic lung diseases. Respir Investig. 2022 May;60(3):430-433. [link]
Careers
As a health tech company with a strong clinical and technical founding team with backgrounds at Stanford and Google, we are growing core engineering and commercial teams to build and deploy a medical-grade software platform. You'll get to help shape the trajectory of this mission-driven company, while receiving a hands-on education in building and commercializing medical AI.
IMVARIA is a remote-first company: we welcome flexible schedules, work from home, and digital collaboration. Join us!
Jobs posted on Wellfound
