NEW YORK – March 17, 2026 – Diadia Health has officially launched its AI causal reasoning platform nationwide following successful beta validation across 12+ clinical sites and thousands of patient cases. The platform analyzes genetic variants and lab results to help clinicians identify potential root causes in complex chronic disease cases and generate personalized treatment recommendations with 60% less trial-and-error than standard protocols. The platform focuses particularly on complex endocrine, metabolic, and hormonal conditions, where under-researched pathways create significant diagnostic gaps. Diadia brings precision medicine insights to cases where standard lab tests return normal results despite persistent symptoms.

Diadia’s platform analyzes patterns across nearly one million genetic variants, over 100 metabolic pathways, and hundreds of biomarkers simultaneously to generate prioritized treatment protocols. Functioning as a vendor-agnostic intelligence layer on top of existing lab data sources, Diadia augments rather than replaces clinician judgment, with 98% of AI-generated reports requiring zero revision before clinical use.

“Precision medicine will be AI-first because of large data volumes. There's no way a single physician could streamline all that information,” said Dr. Anil Bajnath, MD., Founder of the American Board of Precision Medicine. “I already know, after going through iterations of it, that Diadia is going to be one of the best technologies I've seen to help bring precision medicine to the forefront of healthcare.”

Diadia draws on a growing database of 310,000+ peer-reviewed research papers and was developed by AI veterans and precision medicine specialists from Stanford, Berkeley, Penn Medicine, Uber, and IBM. Unlike consumer wellness apps or LLM-based tools that can produce unreliable outputs, Diadia utilizes genomic data and predictive biomarker patterns to determine not only genetic predisposition but also whether specific genes are actively expressed. The platform maps biological relationships among genetics, lab results, and patient history to generate transparent reports that show clinicians the exact medical logic and nearly 100 peer-reviewed research citations behind every rationale and recommendation.

“A recent study supported by Harvard Medical School, Johns Hopkins, and other expert institutions noted that 91.8% of clinicians have ‘encountered medical hallucinations.’ If AI hallucinates upstream, you get garbage downstream," said Dr. Bajnath. “What's exciting about Diadia is they've accounted for that hallucination potential and eliminated those choke points. This is a non-hallucinating, evidence-driven AI copilot.”

“Standard labs are insufficient to understand the root-cause issues in complex and chronic health cases,” says Diadia CEO and Founder Elena Ikonomovska, PhD. “At the end of the day, each person’s body is so unique and individual, that without genetics, without a multi-omic analysis, we will continue to build cookie-cutter protocols that lack the nuance necessary for effective solutions.”

After leading AI at Reddit and prototyping technology for Google, Dr. Ikonomovska founded Diadia to address blanket treatment protocols and bring interpretability to the complex interconnections between genetic expression, biomarkers, and clinical presentation in chronic disease. Born from her own experience navigating unexplained symptoms with normal lab results, Diadia's research-backed platform focuses specifically on cases where standard diagnostic approaches fail to identify root causes.

Backed by Salesforce Ventures, Sound Ventures, Tribe Capital, and others, Diadia validated the platform across dozens of clinical sites during beta testing, and is now scaling to meet demand for AI diagnostic support tools that provide transparent, evidence-based clinical reasoning.

​​Diadia is now available to healthcare providers nationwide. Clinicians interested in integrating the platform can learn more at diadiahealth.com.