Look who's on the cover of @Spotify's Women of K-Pop playlist! 💛🍋 S2U, let's keep streaming "Lemon Tang" by Hearts2Hearts all day, every day, and show the world the power of our Lemon Tang energy! 🍋✨ 💛 🎧 #Hearts2Hearts# #하츠투하츠# #H2H# #LemonTang# #Hearts2Hearts_LemonTang# @SpotifyKR @SpotifyKpop

Wooow FOUR songs that hit the billion mark on Spotify!! Women lie, men lie.. numbers don’t. I’m so excited to put out new music and reach even more milestones. Thank you everyone for listening, it really means alot.. can’t wait for you to see and hear what’s next😏 #BillionsClub#

Catch the latest episode of WWE AHORA with Andrea Bazarte and Eduardo Bates, featuring new Women’s World Champion Stephanie Vaquer ☝️👩‍🦰🏅🇨🇱 📺 Watch the full episode here: 🎧 Listen on Spotify: 🔈 And on Apple:

The dominant and ready Navy of today is a product of the brave men and women who have dedicated their lives to serving something greater than themselves. Learn more about what it's like to be a sonar technician in the latest episode of the "Sailors First" podcast featuring Chief of Naval Operations Adm. Daryl Caudle, Master Chief Petty Officer of the Navy John Perryman and two of our undersea warfighters on YouTube, Spotify, Apple Podcasts, Amazon Music and iHeartRadio.

Endometriosis can take up to ten years to diagnose—and even then, women often have to keep fighting for the ongoing care they need. Hertility Health built GynAI to close that gap. Every data point—an online health assessment, blood test results, scan images, even a clinician conversation—stacks into a clearer likelihood score, so doctors can refer women onward with confidence, much earlier in the process. 👉 Find a link to the full episode here: Spotify: Apple Podcast: YouTube:

NHS clinicians get ten minutes with a generalist, if they can get an appointment at all—leaving little room for the specific, personal questions women's health actually requires. Hertility Health made a longer intake mandatory before purchase, and something unexpected happened: conversion went up. Women finally felt heard before being handed a test kit—and clinicians got better information to work with, easing the pressure that drives burnout. 👉 Find a link to the full episode here: Spotify: Apple Podcast: YouTube:

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: 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. Guests: - Tulsi Patel – Director of Product and Technology, Hertility - Lorna Brightmore – Head of Data and AI, Hertility - Jack Pickard – Head of Engineering, Hertility What we cover: - What makes Hertility's data set unique: seven years of linked symptoms, blood tests, and pelvic scans from over a million women - How 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 Listen on Spotify, Apple Podcasts, or watch on YouTube. Spotify: Apple Podcast: YouTube: