Cyberport this year led 8 community companies to participate in the BOC Life 11th Golden Age Expo & Summit, showcasing a diverse range of cutting-edge I&T applications in AI, GeronTech, and HealthTech at its pavilion.

🚨 Deadline Extended to 26 June 2026! 🚨 Missed the original deadline? You still have time. Backed by global private markets firm EQT, the EQT Impact Challenge Hong Kong 2026 has extended applications for early-stage deeptech startups building in Climate & Nature and Health & Well-being. Successful applicants can access: 🏆 HK$4M direct investment from the EQT Foundation 🌍 Access to EQT's global network of 600+ industrial advisors and 300+ leading companies 📣 Media exposure through South China Morning Post New deadline: 26 June 2026 Apply now and take your startup to the next level: #EQTImpactChallenge# #DeepTech# #ClimateTech# #HealthTech# #Startups# #Innovation# #HongKong#

🔍 You notice "red dots on your legs" — but could you search for "palpable purpura"? Google studied how far AI can help people actually understand skin conditions, with 2,345 participants. Title: Research into how AI can help users understand skin conditions URL: 💡 Overview More than half of adults search online for health info and one in three turn to AI. Google Research looked not at AI's diagnostic accuracy itself, but at how much AI actually helps laypeople make better decisions, across two user studies. 🩺 Challenges Solved People can spot "red dots" but rarely know to search "palpable purpura." Being able to access information is not the same as understanding it and choosing the right next step. This work tackles exactly that gap. 🧪 Methodology Using real de-identified cases with images and medical histories, 2,345 participants were randomized into three arms: a control using normal web search, an AI arm showing 3-7 image-backed predicted conditions, and a Wizard of Oz arm where the candidates were dermatologist-verified ground truth. A community study with 110 local participants tested a 4-language app in the real world. 📊 Experimental Results Condition-naming accuracy nearly tripled, 23% for AI versus 8% for control. In the community study, naming ability rose 260% and clinicians rated the app helpful 92% of the time. But accuracy on "see a doctor or self-care?" barely improved, and AI users tended to underestimate urgency. Identifying a condition is not the same as acting on it correctly. #AI# #HealthTech#

"Because it's so simple to use, we think that actually broadens its appeal." Ricky Bloomfield, Oura's chief medical officer, tells Tom Mackenzie how the company aims to make the wearable health tech even more accessible

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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: 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: