01 / FAST

Chest X-rays are currently the best available method for diagnosing different lung associated diseases like hernia, pneumonia, fibrosis, edema, emphysema, cardiomegaly, pleural thickening, consolidation, pneumothorax, mass, nodule, atelectasis, effusion and infiltrations. Our application can detect diagnosis of each of these conditions faster than an average processing time from a radiological laboratory.


The users private radiological data is analyzed on a secure cloud platform.

03 / EASY

Our application is easy to use and can be used easily with no prior experience.

Images Tested


Prioritized Patients


Turnaround Time

1 Minute Per Image

Reporting Efficiency 

More than 95%


MEET ChestAi


ChestAi was started with computer scientists and geneticists at Yale university with a common motivation of providing a free and open source platform for rapid and robust diagnosis of pathologies identified by radiological examinations. 


Chest X-ray exam is one of the most frequent and cost-effective medical imaging examinations. However clinical diagnosis of chest X-ray can be challenging, and sometimes believed to be harder than diagnosis via chest CT imaging. Even some promising work have been reported in the past to achieve a clinically relevant computer-aided detection and diagnosis (CAD) in real world medical sites on all data settings of chest X-rays is still very difficult. Our vision is to decrease the diagnosis burden with improving diagnosis accuracies.


With approximately 2 billion procedures per year, chest X-rays are the most common imaging examination tool used in practice, critical for screening, diagnosis, and management of lung diseases. However, an estimated two thirds of the global population lack access to radiology diagnostics. With automation at the level of experts, we hope that this technology can improve healthcare delivery and increase access to medical imaging expertise in parts of the world where access to skilled radiologists is limited.


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Tel: 404-431-0213

333 Cedar Street

New Haven, CT 06511



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Latest R&D Publications
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1. Common cancer biomarkers of breast and ovarian types identified through artificial intelligence, Shrikant Pawar, Tuck Onn Liew, Aditya Stanam, Chandrajit Lahiri, Wileys: Chemical Biology and Drug Design, 96(3):995-1004. doi: 10.1111/cbdd.13672. Epub 2020 May 15. PMID: 32410355.
2. Developing a DEVS-JAVA Model to Simulate and Pre-test Changes to Emergency Care Delivery in a Safe and Efficient Manner, Shrikant Pawar and Aditya Stanam, Springer: Lecture Notes in Computer Science,  vol 11466. Springer, Cham. https://doi.org/10.1007/978-3-030-17935-9_1.

3. Scalable, reliable and robust data mining infrastructures, Shrikant Pawar and Aditya Stanam, IEEE Fourth World Conference on Smart Trends in Systems, Security and Sustainability, 2020, pp. 123-125, doi: 10.1109/WorldS450073.2020.9210388.
4. A Six-Gene-Based Prognostic Model Predicts Survival in Head and Neck Squamous Cell Carcinoma Patients, Shrikant Pawar and Aditya Stanam, Springer: Journal of Maxillofacial and Oral Surgery, 2019 Jun;18(2):320-327. doi: 10.1007/s12663-019-01187-z. Epub 2019 Jan 24. PMID: 30996559; PMCID: PMC6441444.
5. Clustering Reveals Common Check-Point and Growth Factor Receptor Genes Expressed in Six Different Cancer Types, Shrikant Pawar and Aditya Stanam, Springer: Lecture Notes in Computer Science, vol 12108. Springer, Cham. https://doi.org/10.1007/978-3-030-45385-5_52.

6. Linear regression model for prediction of multi-dimensional time-point forecasting data, Shrikant Pawar and Aditya Stanam, ITISE, I.S.B.N: 978-84-17970-78-9, Legal Deposit: Gr 1209-2019
7. Stochastic dimension reduction techniques for time-point forecasting data, Shrikant Pawar and Aditya Stanam, ITISE, I.S.B.N: 978-84-17970-78-9, Legal Deposit: Gr 1209-2019
8. Evaluating the computing efficiencies (specificity and sensitivity) of graphics processing unit (GPU)-accelerated DNA sequence alignment tools against central processing unit (CPU) alignment tool, Shrikant Pawar, Aditya Stanam and Ying Zhu, Journal of Bioinformatics and Sequence Analysis, 9(2), 10-14.
9. Predicting the prognosis for cancer patients with interleukins gene expression level, Aditya Stanam, and Shrikant Pawar, AACR: Cancer Research, Print ISSN 0008-5472, Online ISSN 1538-7445, DOI: https://doi.org/10.1158/1538-7445.AM2019-4247.

10. Software effort prediction with algorithm-based frameworks, Shrikant Pawar and Aditya Stanam, International Journal of Engineering and Computer Science, 7(09), 24206–24213. Retrieved from http://www.ijecs.in/index.php/ijecs/article/view/4174.

11. Machine learning for identification and characterization of molecular gene signatures in progression of benign tumors, Shrikant Pawar, Aditya Stanam, and Rushikesh Chopade, ACM Proceedings, 1-3, https://doi.org/10.1145/3469213.3469214

12. Techniques of time series modeling in complex systems, Shrikant Pawar and Aditya Stanam, Springer Lecture Notes in Networks and Systems, 978-981-16-2377-6, DOI: 10.1007/978-981-16-2377-6

13. Single shot detector application for image disease localization, Shrikant Pawar, Aditya Stanam, Rushikesh Chopade, bioRxiv 2021.09.21.461307; DOI: https://doi.org/10.1101/2021.09.21.461307 

14. Cyclical Learning Rates (CLR’s) for Improving Training Accuracies and Lowering Computational Cost. Rushikesh Chopade, Aditya Stanam, Anand Narayanan, Shrikant Pawar. Research Square. DOI:10.21203/rs.3.rs-1129014/v1

1. 4th International Conference on Applied Mathematics and Simulation 2021. Machine learning application in genomics.
2. 2nd International Conference on Artificial Intelligence and Information Systems (ICAIIS 2021). Artificial intelligence and cancer biomarker discovery. 

3. 8th International Work-Conference on Bioinformatics and Biomedical Engineering, Granada, Spain, 2020. Clustering reveals common check-point and growth factor receptor genes expressed in six different cancer types. 

4. 4th Smart Trends in Systems, Security and Sustainability Conference, London, UK, 2020. Scalable, reliable and robust data mining infrastructures.
5. American Association for Cancer Research, Atlanta, GA, March 2019. Predicting the prognosis for cancer patients with interleukins gene expression level. 

6. 6th International Congress on Information and Communication Technology, 
London, UK, 2021. Techniques of time series modeling in complex systems.

7. Radiological Society of North America (RSNA) Annual Conference, Chicago, USA, 2021

1. Kickstarting health care innovation with artificial intelligence. Tsai Center for Innovative Thinking at Yale (Tsai CITY) Editors: ReCore team, Andrew Nguyen, David Hou.

2. How NGS and big data can be instrumental in tackling genetic disorders. SelectScience, Editor: Charlotte Carter

3. ChestAi with Yale MD/MBA candidate Anna Zhao selected as semi-finalists for New Haven chapter of Nucleate activator program

4. We thank our past and current interns Dr. Rayudu, Mr. S Kumar(Git), Mr. R Chopade(Git), Mr. Samarth, Mr. Rahul(Git), Mr. Rohan Sharma (Git), Ms. V Bhaskara from Indian Institute of Technology (IIT), Kharagpur and Guwahati, India for continued efforts. We hope our internship program will benefit their future aspirations. Students interested in this program can contact our office for more information.

Latest Conference Presence 
Latest Media Outlets
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