MedicalXpress · Jun 2025
Press coverage of the PNAS study highlighting that Human Dx data helped show hybrid collectives improve diagnostic accuracy.
Public company, workplace, funding, and market signals
Human Dx (The Human Diagnosis Project) is a San Francisco-based medical collaboration and diagnostic decision-support company that combines collective intelligence from clinicians with machine learning to help map possible diagnoses and improve accuracy, affordability, and access to care.
Primary product
Human Dx collaborative diagnostic platform (web/mobile medical case collaboration and open medical intelligence system)
Founded
2013
Headquarters
San Francisco, California, United States
Team size
11-50 employees
Work style
Flexible
Industry
Civic and Social Organizations
Sub-industry
medical diagnosis collaboration / clinical decision support
Offices
11 jobs at Human Dx
Business model
Stage
early-stage VC
Total raised
$2.9M
Latest round
Venture Round · Oct 2013
Latest amount
$2.8M
Leadership
Jayanth Komarneni
Founder & CEO / Chair
Founder / Team Member
Jonas Goldstein
Design Director
Mission-driven and service-oriented, with a strong emphasis on helping underserved patients, clinician collaboration, and global participation.
Work style
Flexible
Visa sponsorship
Limited
Compensation
Public compensation signals are limited. Historical H1B filings for software roles averaged about $100k, and LinkedIn-derived estimates show software engineer pay around $138k and engineering around $119k, suggesting mid-market startup compensation rather than large-scale tech pay.
Pricing
Not publicly disclosed; terms indicate paid services can be purchased with major credit cards, while third-party listings describe free or $0/mo access for some users.
Differentiators
Technology
Customers
Competitors
Estimated revenue
$1M-$2M
Estimated monthly visits
95.3K
Traffic estimate as of Jul 2026
MedicalXpress · Jun 2025
Press coverage of the PNAS study highlighting that Human Dx data helped show hybrid collectives improve diagnostic accuracy.
EurekAlert! · Jun 2025
University/research press release describing the Human Dx-backed study and its findings on hybrid diagnostic collectives.
PNAS · Jun 2025
Peer-reviewed study using Human Dx data found hybrid human-AI collectives outperformed humans-only and AI-only groups in open-ended medical diagnostics.
NEJM AI · Oct 2024
Study using Human Dx vignettes showing that aggregating multiple LLM outputs improves diagnostic accuracy versus single models.
arXiv · Jun 2024
Preprint describing the Human Dx dataset and the human-AI collective diagnostics method later published in PNAS.