Breast cancer screening fails most often where access is constrained: limited appointments, geographic gaps, dense breast tissue, and reliance on self-exams that depend entirely on human touch. Awareness alone doesn’t close those gaps.
In this episode, Dr. Karny Ilan, co-founder and CEO of Feminai, shares how physician-led product design, multidisciplinary collaboration, and rigorous clinical trials shaped a new model for breast screening access. The conversation explores a shift in how breast health is managed—from episodic screening to continuous, individualized monitoring. Rather than relying on infrequent appointments alone, it examines tools designed to track changes over time, at home, while remaining connected to clinical decision-making.
Timestamps
(00:11) Breast cancer risk shaped by genetics and lived exposure
(08:37) Limits of traditional self-breast exams
(09:09) Personal experience shaping breast health urgency
(10:15) How at-home breast scanning detects change over time
(12:42) Designing screening tools for dense breast tissue
(17:03) Addressing breast size, shape, and post-surgical variation
(20:13) Why ease of use affects screening reliability
(29:29) Access gaps amplified by pandemic-era screening delays
(38:09) Broad inclusion across age, risk, and body types
Guest Bio
Dr. Karny Ilan — Co-Founder and CEO, Feminai Dr. Karny Ilan is a general surgery resident at Sheba Medical Center and the co-founder and CEO of Feminai, a breast health company developing an AI-enabled disposable wearable patch and app for at-home breast exams. With a strong family history of breast cancer, she brings clinical experience and patient-centered design to building scalable screening tools that expand access and personalization. LinkedIn:https://www.linkedin.com/in/karny-ilan/
Key Points
Access constraints drive missed detection: Feminai targets screening gaps caused by geography, capacity, and avoidance.
Physician-led design builds trust: Clinical credibility accelerated adoption with providers and investors.
Dense breast tissue is a priority use case: The technology is designed to perform well where mammography often struggles.
Personalized baselines change detection logic: Each scan is compared against the user’s own prior data.
Usability directly affects accuracy: Instructions, fit, and behavior shape downstream AI performance.
Deep Dives
1. At-home breast exams as infrastructure
Designed for frequent, low-friction use
Complements rather than replaces imaging
2. Patch and app workflow
Risk stratification via medical questionnaire
Bluetooth-enabled scan uploads to secure cloud
AI analysis with physician review
3. Designing for every body
Stretch materials accommodate size variation
Dense tissue explicitly accounted for
Additional sizes planned as rollout expands
4. Clinical trials beyond performance metrics
Usability drove multiple design iterations
Instruction format affected adherence
Shape changes required algorithm updates
5. Personalized longitudinal tracking
Each woman compared only to herself
Changes flagged based on deviation, not population averages
6. Leadership and multidisciplinary teams
Engineers exposed to clinical sites
Patient stories shared to reinforce mission
Stability in leadership communication protected execution
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