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

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

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How do you build trustworthy AI diagnostic tools in one of medicine's most historically under-researched areas?

In this episode of Just Now Possible, Teresa Torres talks with Tulsi Patel (Director of Product and Technology), Lorna Brightmore (Head of Data and AI), and Jack Pickard (Head of Engineering) at Hertility, a UK and Ireland-based women's health tech company. Hertility combines an in-depth online health assessment with at-home hormone testing and clinician-reviewed reports to help diagnose conditions spanning menstruation to menopause.

Built on seven years of data linking symptoms, blood results, and pelvic ultrasound scans for over a million women, the team walks through two AI products in development: Gyn.AI, a Bayesian network that gives clinicians probability-based diagnoses instead of binary calls, and a scan automation pipeline that classifies ultrasound images, measures follicle counts and ovarian volume, and drafts clinical letters using an agentic loop that checks its own output against patient data before a human ever reviews it.

You'll hear how the team guards against automation bias, builds clinician trust through transparency, minimizes PII before it ever reaches a model, and treats healthcare regulation as a design constraint from day one rather than a last-minute scramble. It's a detailed look at what it takes to bring AI into one of the most sensitive, tightly regulated corners of healthcare.

Show Notes

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

Full Transcript

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