In the News MedTech Strategist
Gesund.ai Puts Algorithms to the Test to Improve AI Equity”

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 Gesund.ai

Gesund is the world’s first compliant AI factory on a mission to help bring clinical-grade AI solutions to market. To help comply with regulatory requirements, our platform audits and validates 3rd party medical AI solutions for safety, effectiveness and equity. Backed by marquee investors including Merck, McKesson, Northpond and 500, Gesund orchestrates the entire AI/ML lifecycle for all stakeholders by bringing models, data and experts together in a no-code environment.

How it works

Standardized, unified and diversified data customized for your ML needs and regulatory requirements
Gesund.ai assesses model validation needs and provides a suitable mix of high-quality data from its multiple and diverse clinical partner sites

1.

Model owner shares clinical study with Gesund.ai for curation of appropriate dataset(s), and uploads their model onto Gesund.ai's federated validation platform, which resides on hospital premise or private cloud.

2.

Model runs against a previously unseen validation data set that has been curated on the hospital side.

3.

Model accuracy metrics are produced and displayed on the Gesund.ai platform for further examination with respect to patient characteristics, scenario analyses and stress testing.

4.

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.ai 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

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

Dataset matching
according to case-specific
regulatory demands

Annotation
as-a-service

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

Compliance

hipaadrata

Gesund.ai is Now Proudly Compliant with SOC 2 - Type II Standards!

Gesund.ai utilizes enterprise-grade best practices to protect our customers’ data, and works with independent experts to verify its security, privacy, and compliance controls, and has achieved SOC 2 Type II report against stringent standards.

SOC 2 Report

We work with an independent auditor to maintain a SOC 2 Type II report, which objectively certifies our controls to ensure the continuous security of our customers' data.

Developed by the Assurance Services Executive Committee (ASEC) of the AICPA, the Trust Services Criteria is the set of control criteria to be used when evaluating the suitability of the design and operating effectiveness of controls relevant to the security, availability, or processing integrity of information and systems, or the confidentiality or privacy of the information processed by the systems at an entity, a division, or an operating unit of an entity.

Continuous Security Control Monitoring

Gesund.ai uses Drata’s automation platform to continuously monitor 100+ security controls across the organization. Automated alerts and evidence collection allows Gesund.ai to confidently prove its security and compliance posture any day of the year, while fostering a security-first mindset and culture of compliance across the organization.

Employee Trainings

Security is a company-wide endeavor. All employees complete an annual security training program and employ best practices when handling customer data.

Penetration Tests

Gesund.ai works with industry leading security firms to perform annual network and application layer penetration tests.

Secure Software Development

Gesund.ai utilizes a variety of manual and automatic data security and vulnerability checks throughout the software development lifecycle.

Data Encryption

Data is encrypted both in-transit using TLS and at rest.

Vulnerability Disclosure Program

If you believe you’ve discovered a bug in Gesund.ai’s security, please get in touch at security@gesund.ai. Our security team promptly investigates all reported issues.

Latest News

January 21st 2024

“2024 is the Year of AI Safety” Gesund.ai CEO & Founder Dr. Enes Hosgor, Ph.D

January 2nd 2024

“Biden and AI Governance: The Case for a Hub-and-Spoke Framework in Model Validation” Dr. Enes Hosgor

December 27th 2023

"Overcoming data access challenges: an interview with Enes Hosgor"

Publications

Papers

An automated approach for machine learning model performance analysis on sub cohorts to assess for biases

Authors: Brian Ayers MD MBA, Ray Funahashi MD, Veysel Kocaman PhD, Enes Hosgor PhD

presented at
pdf_summary

1. Research Questions

Can an automated approach help identify clinically significant biases within machine learning prognostic models?

2. Findings

Of the 1,343 total patients included in the study, 179 (13%) patients died. The final model accuracy on the validation cohort overall was 80% (minority class F1 score=0.39, AUC=0.663). However, using our automated tool there were numerous clinically significant differences identified in model accuracy on different patient subsets. For example, the model was much more accurate for patients requiring the intensive care unit (86% accuracy) but was much worse for other sub cohorts such as current smokers (60% accuracy) and male patients (78% accuracy). Moreover, there were many sub cohorts with insufficient patient data to perform sufficient analysis.

3. Conclusions

These data demonstrate the high risk for model performance discrepancies on subset of patients with different characteristics. Using a standardized, automated approach for systematic model validation is instrumental in minimizing model biases before implementing a machine learning model in a clinical setting.

Backed by

merck
mckesson
northpond
500

Advisory Board

The HonorableDavid J. Shulkin, M.D.
Bryan Sivak
Prof. Paul Chang, M.D.
Vance Moore

Team

Enes Hosgor, PhD
CEO & Founder
Veysel Kocaman, PhD
VP of Engineering
Marco Smit
VP of Business Development
Ronald Schuchard, PhD
Regulatory Expert
Sumir Patel, M.D., MBA
Chief Medical Officer
Peter Bucciarelli, M.D.
Clinical Scientist
Nikhil Patil, M.D.
Clinical Data Science Intern
Yashwanth Soundararaj
Regulatory Affairs Manager
Melis Yilmaz
UI/UX Designer
Pedro Martins
Sr. MLOps Engineer
Akson Sam
MLOps Engineer
Mehmet Burak Sayici
MLOps Engineer
Deniz Dugru
MLOps Engineer
Muhammed Ali Bulut
MLOps Engineer
Hammad Khalid
MLOps Engineer
Resul Turan
Full-Stack Developer
Isa Sumer
Front-End Developer
Furkan Turkoglu
Front-End Developer
Necip Tahir Ozgan
Front-End Developer
Taha Yasin Cevik
Front-End Developer
Serkan Sokmen
Medical Software Engineer

Careers

QA Engineer (Selenium Python Test Automation)

Develop, maintain, and execute test plans, test cases, and test scripts for our frontend web application using Python Selenium automation.

Work closely with the DevOps team to integrate test automation into the continuous integration and continuous deployment (CI/CD) pipeline using tools such as Jenkins.

...

DevOps Engineer/ Backend Engineer (Remote)

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

Solid experience in Restful API design and development

...

Machine Learning (Remote)

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

...

Let's Connect

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