Building AI for Women's Health: How Hertility Combined Bayesian Diagnosis and Scan Automation

Guests

• Tulsi Patel, Director of Product and Technology, Hertility
• Lorna Brightmore, Head of Data and AI, Hertility
• Jack Pickard, Head of Engineering, Hertility

In this episode

• What makes Hertility's data set unique: seven years of linked symptoms, blood tests, and pelvic scans from over a million women
• How Gyn.AI uses a Bayesian network to give clinicians probability-based diagnoses instead of binary yes/no calls
• Why showing clinicians the reasoning behind a diagnosis—not just the label—builds trust and speeds up triage
• Guarding against automation bias with holdout sets and independent, fresh-eyes review
• Inside the scan automation pipeline: classifying ultrasound images, detecting follicles, and measuring ovarian volume more precisely than manual methods
• Using an agentic loop to check AI-drafted clinical letters against patient data and catch hallucinations before a human sees them
• The infrastructure challenge of securely piping DICOM ultrasound images from third-party scan providers into Hertility's systems
• How Hertility handles PII and PHI: pseudonymization, data minimization, and running models in-house on AWS Bedrock
• Why treating healthcare regulation as a product requirement from day one makes AI products more scalable, not slower

Key Takeaways

• Probabilistic, transparent AI outputs build more clinician trust than binary classifications.
• Guardrails against automation bias are as important as the model itself.
• Data minimization and in-house infrastructure make it possible to build AI responsibly with sensitive health data.
• Treating regulation as a design constraint from day one makes AI products more defensible and scalable, not slower.

Resources & Links

• Hertility — At-home hormone testing and reproductive health diagnostics for women in the UK and Ireland
• AWS Bedrock — The platform Hertility uses to run LLMs in-house under its own governance and regulatory controls
• PyTorch — The foundation for Hertility's in-house image classification and contouring models

Chapters

00:00 Meet the Team
00:13 What Hertility Does
01:51 How Customers Access It
04:06 A Unique Women’s Health Dataset
07:03 Mission and Efficiency with AI
10:03 Why Long Assessments Convert
13:52 Before AI Workflows
16:52 Research Publications and Impact
18:48 GynAI Reducing Time to Diagnosis
21:21 Triage and Clinician Support
24:37 Keeping Patient UX the Same
26:12 Bayesian Network and Explainability
30:19 Multiple Diagnoses and Probabilities
32:37 Probabilistic Diagnosis Shift
33:50 Clinician Adoption and Workflow Fit
34:58 Communicating Medical Uncertainty
36:43 Scan Automation Overview
40:30 In House Image Analysis
44:25 DICOM Pipeline Engineering
47:30 Evals and Automation Bias
50:31 LLM Letter Guardrails
56:47 PHI Handling and Regulations
01:00:43 Infrastructure Choices and Wrap Up