Healthcare • AI • Fairness
Transforming AI in healthcare by delivering unbiased data insights for better outcomes for all
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Sign up to get updates on EvalRx and be the first to know when we launch.
EvalRx audits datasets for bias before deployment to prevent inequities and enable equitable decision‑making from the ground up.
We’re Mohani and Nicole, female minority founders with backgrounds in biomedical science and data analytics. With experience in healthcare AI, data tools, and AI policy, we’re uniquely equipped to tackle bias at the data level.
AI tools in healthcare must be accurate and equitable, but current evaluation methods often lack clinical relevance. Generic approaches miss early signals of bias, leading to inequities and mistrust among clinicians—sometimes before models are even deployed.
EvalRx is a web platform that audits healthcare datasets for bias before they’re used to train AI models. It detects underrepresentation and systemic imbalance, then generates clear, actionable reports to guide fair, transparent development.
Readable summaries with ethical risk highlights and recommendations that technical and non‑technical stakeholders can use to make informed decisions.
Tailored equity metrics for clinical risk, population diversity, and regulatory needs.
Flags bias early—before model training and well before deployment.
Clear, digestible reports with recommendations for remediation and governance.
Expanding EvalRx to audit both data and models, ensuring fairness across the ML lifecycle with continuous checks during retraining.
Upcoming API integrations and team growth across privacy, data engineering, and go‑to‑market to support enterprise adoption and security.
Biomedical Science background with experience in healthcare AI applications and AI policy.
BBA in Computer Information Systems (Data Analytics); skilled in data tools, machine learning, and database systems.
Healthcare organizations, AI developers, researchers, and digital health startups. Ultimately, patients benefit from fair and accurate AI‑driven care.
Bias in structured healthcare data including demographic, geographic, and class imbalance—flagging underrepresentation and systemic gaps.
If models are trained on incomplete or skewed data, decisions about care can be inaccurate or unfair. EvalRx helps prevent that—starting at the data layer.
Be first to hear about launch, research updates, and pilot opportunities.