At Leeaf, we seek to disrupt existing patterns in female health care and contribute our part to
a future where women benefit from personalized health care tailored to their individual needs.
We strive to develop an efficiency enabling digital solution that empowers fertility treatments and supports patients on an artificial reproduction journey to successfully conceive, ideally needing two cycles or less.
Our mission is to positively impact the conception journey by delivering tailored fertility treatment recommendations to doctor and patient based on big data analysis and AI.
Olga Chabr Grillova
Founder & Co-CEO
Head of Product
Head of UX/Design
Head of Legal and Compliance
Head of Investor Relationships
Head of Business Development
Prof. Ivana Oborna, PhD.
Dr. Nabil Aziz
Spire Liverpool Hospital (UK)
Prof. Gabor Kovacs
Prof. emer. MD., PhD, DSc.
Szentagothai Research Center University of Pecs (HU)
Dr. Franccisco Anaya Blanes
UR La Vega Alicante (SPAIN)
Associate Prof. Glenn Schattman
Weill Cornell Medicine (US)
At Leeaf, we strive to innovate through science utilizing modern technology transparently and ethically.
Our research contributes to the field of artificial reproduction through dedicated research projects as well as independent, standardized, and anonymized data collection.
To contribute to the improvement of fertility treatment outcomes on a larger scale, the Leeaf research team is currently conducting a study to verify the effectiveness of data and AI-driven treatment personalization in increasing pregnancy and take-home baby rates for infertile couples.
Simultaneously, our continuous data collection seeks to reveal how lifestyle changes initiated and tracked through women‘s lifestyle wearables impact IVF results.
Principles of Ethical AI
Leeaf’s Ethical AI Principles are based on the latest thought leadership of global technology and consulting companies. We acknowledge that algorithms augment the capabilities of doctors to produce better results, together. Final decision making stays with the patient and should always be based on doctors' treatment guidance and approval. The data provided always belongs to the patient, with algorithms and technology remaining transparent, explainable, fair, and auditable at any time.