gesund gesund

A CRO platform for clinical-grade AI

Train. Validate. Secure clearance.
The problem we are facing
Medical AI adoption is lagging due to a lack of
compliant, scalable, and ML-friendly data access

About orchestrates the AI as-a-Medical Device lifecycle, providing privacy-centered access to diverse
yet standardized medical data sources, and a unique analytical toolbox that fosters clinical validation, regulatory clearance and effective marketing

How it works

Standardized, unified and diversified data customized for your ML needs and regulatory requirements
Gesund assesses model validation needs and provides a suitable mix of high-quality data
from its multiple and diverse clinical partner sites
Model owner shares clinical study with Gesund for curation of appropriate dataset(s), and uploads their model onto Gesund's federated validation platform, which resides on hospital premise or private cloud.
Model runs against a previously unseen validation data set that has been curated on the hospital side.
Model accuracy metrics are produced and displayed on the Gesund platform for further examination with respect to patient characteristics, scenario analyses and stress testing.
The model insights are exported into a report for the model owner to supplement their regulatory submission.

Request early access

Are you an academic researcher in medical AI?
Bring your own data or model and join the Gesund community. For free

No-hassle model exploration
and validation running models against
real-world data in a secure environment

No more dependency
on software engineers to containerize or deploy models

Share models
and insights out of the
box with collaborators

Gesund’s CRO platform provides
end-to-end services for machine-learning
algorithm development and validation

Dataset matching
according to case-specific
regulatory demands
Assessment of demographic characteristics for explanatory purposes
Post-market validation
and evaluation
Update model via
re-training with prospective
studies against standard of care
Identify gaps
in algorithms
To tap proprietary data sources and reader expertise through our platform

Latest news

March 3, 2022

‘I need evidence yesterday’: Gesund raises $2 million to provide algorithm-validating data

March 3, 2022 Exits Stealth with $2M in Funding to Build the Highway of Clinical-Grade AI for Safe and Effective Medical Applications

March 9, 2022

A startup provides medical data for testing AI health solutions

March 11, 2022

Enes Hoşgör, Ph.D., CEO at Gesund: AI Is on the Road to Improving Healthcare: It’s Time to Build a Superhighway

Advisory board

The Honorable
David J. Shulkin, M.D.

The Honorable Dr. David J. Shulkin was the ninth Secretary of the US Department of Veterans Affairs in the Trump Administration and VA’s Under Secretary of Health in the Obama Administration. As such, Secretary Shulkin was the only member of the Cabinet to have served both Presidents and to have been confirmed by the US Senate by a vote of 100-0.

As Secretary, Dr. Shulkin represented the 21 million American veterans and was responsible for the nation’s largest integrated health care system with over 1,200 sites of care, serving over 9 million Veterans. VA is also the nation’s largest provider of graduate medical education and major contributor of medical research and provides veterans with disability payments, education through the GI bill, home loans, and runs a national cemetery system.

Secretary Shulkin is a widely respected healthcare executive having served as chief executive of leading hospitals and health systems including Beth Israel in New York City, Morristown Medical Center in Northern NJ, and currently advises leading health systems. Secretary Shulkin has held numerous physician leadership roles including the Chief Medical Officer of the University of Pennsylvania Health System, the Hospital of the University of Pennsylvania, Temple University Hospital, and the Medical College of Pennsylvania Hospital. Secretary Shulkin has held academic positions including the Chairman of Medicine and Vice Dean at Drexel University School of Medicine. As an entrepreneur, he founded and served as the Chairman and CEO of DoctorQuality one of the first consumer orientated sources of information for quality and safety in healthcare. Secretary Shulkin has served on boards of managed care companies, technology companies, and health care organizations. He now works with healthcare organizations that are leading the transformation of medicine around the world.

Secretary Shulkin is a board-certified internist. He received his medical degree from the Medical College of Pennsylvania, his internship at Yale University School of Medicine, and a residency and Fellowship in General Medicine at the University of Pittsburgh Presbyterian Medical Center. He received advanced training in outcomes research and economics as a Robert Wood Johnson Foundation Clinical Scholar at the University of Pennsylvania.

Over his career Secretary Shulkin has been named “One Hundred Most Influential People in American Healthcare” by Modern Healthcare. He continues to advocate on behalf of the country’s veterans by serving on the board of numerous nonprofits that serve veterans, is the host of the popular Policy Vets Podscast, and is the author of the recent book, “It Shouldn’t Be This Hard to Serve Your Country: Our Broken Government and the Plight of Veterans”.

Follow @DavidShulkin

Bryan Sivak

As a visionary leader and entrepreneur, Bryan Sivak uses intellectual agility, abstract reasoning, and a passion for growing leaders to boost organizations into disruptive powerhouses. In addition to leadership roles in the public sector (Chief Technology Officer of the U.S. Department of Health and Human Services, Chief Innovation Officer for the State of Maryland, and Chief Information Officer for the District of Columbia), Bryan has served in executive leadership positions at major corporations including Centene and Kaiser Permanente. Additionally, he founded a global enterprise software company, InQuira, which was acquired by the Oracle Corporation. Bryan currently is an active investor and board member for a number of high-growth startups primarily focused on improving individual health and healthcare across the world.

Prof. Paul Chang, M.D.

Dr. Chang is Professor and Vice-Chairman of Radiology Informatics at the University of Chicago School of Medicine. Dr. Chang received his B.A. from Harvard University and his M.D. degree from Stanford University. Concurrent with his medical school training, he also received his M.S. degree in Engineering-Economic Systems from Stanford. Dr. Chang completed his residency and fellowship training in Diagnostic Radiology at Stanford University Hospital.

Dr. Chang is an internationally recognized expert in the field of imaging informatics and has been involved in numerous research and development projects related to imaging informatics as well as enterprise-wide informatics challenges. His early work in workstation design has resulted in presentation and navigation models that are widely used by the majority of PACS systems. While at the University of Iowa, he established and evaluated one of the first US rural teleradiology networks to provide primary interpretation. A novel lossless wavelet-based image distribution mechanism, dynamic transfer syntax (DTS), was co-invented by Dr. Chang, and enabled the development of one of the world’s first viable enterprise image distribution solutions. This technology was subsequently commercialized by the creation of Stentor PACS, which was acquired by Philips Medical Systems.

Dr. Chang has been an early advocate for deep and granular IT system interoperability to support data driven informatics workflow orchestration in radiology. He has led the development of one the world’s first Service Oriented Architecture (SOA) implementations within a healthcare enterprise at the University of Chicago. Dr. Chang has been able to demonstrate that this SOA approach can be leveraged to improve efficiency and quality in image acquisition, interpretation, and results communication.

Dr. Chang has been active in the application of informatics approaches to radiology education. His research group was one of the first to describe, implement, and evaluate the use of simulation in radiology resident education. Under his leadership, along with important contributions by RSNA informatics staff, Diagnosis Live, a novel cloud based interactive educational platform featuring gamification and deep analytics was developed. Diagnosis Live was an extremely popular part of the annual RSNA meeting and was used by hundreds of residency programs worldwide.

Dr. Chang has been member of the RSNA Radiology Informatics Committee (RIC), ACR Informatics Committee, and the ACR Commission on Clinical Research and Information Technology. He has served as a member of the ACR Council Steering Committee and Editorial Board of the JACR. He was an informatics consultant to the RSNA for the RadSCOPE electronic education initiative and the myRSNA portal. He has published over 100 peer reviewed articles and book chapters, and has been awarded 14 patents. He has given over 600 invited lectures worldwide and has served as course director and/or faculty for over 200 courses for the RSNA and for the Society for Imaging Informatics in Medicine (SIIM) in PACS and radiology informatics. In 2002, he was named as one of the “Top 20 Most Influential People in Radiology” by Diagnostic Imaging. In 2005, he was inducted as a Fellow to the College of the Society for Computer Applications in Radiology (SCAR/SIIM). In 2010, he was named as one of the “25 Most Influential People in Imaging” by RT Image. In 2016, he was awarded the Gold Medal by the RSNA “for having revolutionized the practice of radiology through his expertise in the field of imaging informatics.”


Enes Hosgor, Ph.D.

CEO & Founder

Dr. Enes Hosgor is an engineer by training and an AI entrepreneur by trade driven to unlock scientific and technological breakthroughs having built AI products and companies in the last 10+ years in high compliance environments. After selling his first ML company based on his Ph.D. work at Carnegie Mellon University, he joined a digital surgery company named caresyntax to found and leads its ML division. His penchant for healthcare comes from his family of physicians including his late father, sister and wife. Formerly a Fulbright Scholar at the University of Texas at Austin, some of his published scientific work can be found in Medical Image Analysis; International Journal of Computer Assisted Radiology and Surgery; Nature Scientific Reports and British Journal of Surgery, among other peer-reviewed outlets.

Veysel Kocaman

VP of Engineering

Veysel is a seasoned medical data scientist with a strong background in every aspect of machine learning, artificial intelligence, and big data with over ten years of experience. He’s also pursuing his Ph.D. in Computer Vision at Leiden University, Netherlands, and delivers graduate-level lectures.
Veysel has worked with large pharmaceutical and healthcare companies in the past and run multimillion dollar AI projects to integrate smart solutions into their workflows. He has a broad consulting experience in Statistics, Data Science, Software Architecture, DevOps, Machine Learning, and AI to several start-ups, boot camps, and companies around the globe. He is officially recognized by Google as an ML Developer Expert and also speaks at Data Science & AI events, conferences and workshops, and has delivered more than a hundred talks at international as well as national conferences and meet ups.

Burak Sayici

ML Engineer

Ray Funahashi, M.D.

Clinical Affairs

Ray leads Gesund’s Clinical Affairs team in establishing partnerships and implementation at clinical institutions. Prior to joining Gesund, he co-founded the AT Center for Medical Innovation at the University of Pittsburgh, in collaboration with UPMC Neurosurgery. In addition, as Program Manager for Drug Discovery at Scivelo, he worked on commercializing an NIH-funded Organs-on-a-Chip data platform. His prior research includes bioengineering at Columbia University and the National Institutes of Health (NIH), where he co-patented a method to genomically edit human neural stem cells. He holds an MD from the University of Pittsburgh and a BS in Molecular Biology from West Chester University.

Brian Ayers, M.D., MBA

Clinical Affairs

Brian Ayers is a current general surgery resident at the Massachusetts General Hospital. He attended Middlebury College where he studied biochemistry and computer science. He subsequently went on to earn an MD from the University of Rochester School of Medicine and an MBA from the Simon Business School. He has published extensively on applications of machine learning within medicine, including developing prognostic models for heart failure patients.

Akson Sam

Data Engineer

Resul Turan

Full-Stack Developer

Isa Sumer

Front-End Developer

Murat Kilic

UX/UI Designer


DevOps Engineer (Remote)

We are building a privacy-first MLOps platform for data-driven organizations in healthcare and life sciences. The platform is designed to support the entire lifecycle of machine learning (ML) efforts to accelerate breakthrough medical research and bring clinical-grade ML solutions to market. Our fast-expanding strategic network includes early clinical and technology partners and organizations in the US, Israel and Europe.

Job Description

We are looking for a DevOps Engineer who is proficient with cloud technologies and setting up CI/CD pipelines for containerized applications in secure environments.


  • proficient in Python or Node.js
  • hands-on experience with AWS or Azure (IAM user and role management,, EKS, S3, RDS, ELB, ECS, Elastic Cache) and a solid understanding of security applications,
  • in-depth knowledge of designing, building and maintaining CI and CD data lines & deployment pipelines using Github Actions or Jenkins
  • a good knowledge of Docker and Kubernetes technologies,
  • experience with NoSQL databases, mainly MongoDB
  • hands-on experience with ELK (elastic, log stash, Kibana) stack
  • comfortable with configuring and supporting Linux-based servers and applications,
  • a solid knowledge of networking, remote access management, VPCs, security groups, and can troubleshoot any type of network traffic,
  • experience with on-prem server setup and monitoring,
  • experience in the distribution of a wide variety of software products for testing, staging and manufacturing systems,

you will be working on the following areas:

  • Help conceptualize and build an MLOps platform around data management and federated learning in healthcare industry; from initial design to full implementation and deployment
  • Work with the team to design and implement tools and APIs for a centralized system with distributed agents/workers
  • Build supplementary software components that enables data scientists to interact with the platform
  • Support integration with existing ML/DL/FL libraries

Machine Learning Engineer (Remote)

We are building a privacy-first MLOps platform for data-driven organizations in healthcare and life sciences. The platform is designed to support the entire lifecycle of machine learning (ML) efforts to accelerate breakthrough medical research and bring clinical-grade ML solutions to market. Our fast-expanding strategic network includes early clinical and technology partners and organizations in the US, Israel and Europe.


  • Help conceptualize and build an MLOps platform around data management and federated learning; from initial design to full implementation and deployment
  • Work with the team to design and implement tools and APIs for a centralized system with distributed agents/workers
  • Build supplementary software components that enables data scientists to interact with the platform
  • Support integration with existing ML/DL/FL libraries
  • Develop highly scalable machine learning (computer vision) models to solve problems such as medical image classification and segmentation
  • Develop in-house machine learning tools and pipelines to support fast experimentation of machine learning models
  • Work with other engineers to identify and solve machine learning problem


  • Experience in one or more of the following areas: deep learning, computer vision,
  • Experience with machine learning frameworks such as TensorFlow, PyTorch or YOLO
  • Curiosity and minimal experience in Federated Learning & Self- Supervised Learning algorithms & applications
  • Expert knowledge in Python (object oriented design)
  • Expertise in API design with FastAPI
  • Through understanding of deploying ML models via Docker and Kubernetes at scale on-prem and cloud.

Let's Connect

United States: 80 Fawcett St, Cambridge, MA 02138
Germany: Martin Opitz Str. 23, Berlin 13357

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    Job Description