How AI For Women’s Health Is Being Built Differently

A new mother navigating postpartum depression accesses support through Coddle.gettyArtificial intelligence for women’s health was supposed to make healthcare more efficient, more personalized, and more equitable. For many women using it for research, it has done the opposite—reinforcing misdiagnosis, dismissal, and fragmented care. The problem is not that AI is new. It is that many healthcare AI systems are trained on decades of male-centric data and designed around clinical workflows that do not reflect how women actually experience their bodies or navigate care.What is changing now is not just better technology, but a shift in who is building it and what they believe AI should do. These women-led companies—working in postpartum recovery, sexual health, workplace benefits, mental health—aren't using AI the way everyone else is. Instead of predicting disease, they're helping women navigate systems that were designed without them.The Core Problem: AI Mirrors Healthcare BiasA lactation coach provides breastfeeding guidance to a new mother through an employer-sponsored benefits program, accessed via her company’s customized Work& platform.gettyAt the heart of AI failure in women’s health is a structural issue: bias baked into data, design, and assumptions.“The most common bias is the ‘male default,’” sighed Karishma Patel, cofounder and chief brand officer at Ema, an AI platform designed to help women navigate healthcare with clarity and empathy. “The training data and design patterns assume a standard human that is, in fact, male.”The consequences are real. Women's symptoms look different from men's—especially with heart disease, autoimmune conditions, chronic pain, and hormonal disorders. So when AI tools train on male-pattern data, women end up misdiagnosed or dismissed.MORE FOR YOUAbbey Donnell, founder and CEO of workplace wellness platform Work&, sees the same risk in applying legacy AI to women’s care. “If we’re going down the traditional AI route, you’re just perpetuating that,” Donnell remarked. “You’re not addressing the gaps or the biases, and you’re reinforcing the dismissive experience women already have.”Patel emphasized that bias compounds across racial and ethnic groups. AI diagnostic tools exhibit markedly different performance across Asian, Hispanic, Black, and white women. That layering of bias manifests in real-world outcomes as delayed care and missed diagnoses.Ema Pilot Revealed Care Gaps That AI Could HelpThose systemic failures were echoed in the hundreds of applications submitted to Ema’s AI for Women’s Health pilot. Most applicants were early-stage companies with live products already serving women. The common thread wasn't about diagnostics. What tied the products together were instances when women feel abandoned by the healthcare system and don't know what to do next.Across applications, the same words kept showing up. “‘Overwhelmed.’ ‘Care gaps.’ ‘Too many tools.’ and 'Not enough guidance,’” Patel emphasized.Postpartum trauma, sexual pain, menopause, caregiver burnout, and benefits confusion dominated submissions. Many of these companies exist because their founders experienced the problem. They dealt with doctors who didn't talk to each other, systems that offered zero emotional support, and they had to piece together their own care.What these companies have in common is not novel technology, but clarity about where systems break down.Why Navigation Matters More Than PredictionOne of the most consistent insights across interviews was that prediction—AI’s traditional strength—often fails women, while navigation succeeds.“Prediction asks, ‘What might be wrong?’” Patel noted. “Navigation asks, ‘What’s next for you?’”When you're managing multiple doctors, different life stages, and everything else on your plate, sometimes you just need to know what to do next. That's more useful than predictions about some illness you might get. And since women's health data is spotty at best—years of underresearch and delayed care will do that—these navigation tools are built to work with what we actually have.Donnell described navigation as collaborative rather than authoritative: “We make no claim to be experts in your health. What we do is help you understand what’s happening and show you what options you have.”At Work& that means connecting benefits, physical spaces, and timing. “Help happens between 9 and 5,” Donnell observed. “No one wants to take a telehealth call at their docking station in the middle of an open office.” Her platform connects users in moments of need. It also connects the issues mothers face to employee benefits they may not be aware of.Navigation, in this framing, becomes connective tissue—linking emotional signals, logistics, and care pathways in real time.AI For Postpartum And Sexual Health Postpartum Care And The Cost Of SilencePostpartum recovery is one of the clearest examples of healthcare fragmentation. Women are discharged from hospitals after childbirth with little guidance on what recovery should look like or when to seek help.“I read everything,” explained Ruth Gordon-Martin, founder of postpartum care company Coddle. “And nowhere did I read anything about what to expect after a C-section or what postpartum recovery actually looks like.”After severe blood loss and postpartum anemia following childbirth, Gordon-Martin realized how unprepared she had been. “Every other surgery has a manual,” she mentioned. “With childbirth, you’re sent home, and that’s it.”Coddle now combines physical recovery products with education and AI-supported guidance. For Gordon-Martin, the value is not just information but validation. “A lot of it is validation,” she said. “Is this normal? You’re no longer isolated with it.”Sexual Pain And Normalized DismissalA woman experiencing pain after sex.gettySexual pain and pelvic health reveal another dimension of bias: minimization.“In pelvic health, bias shows up as minimization,” stressed Emily Sauer, founder and CEO of The Pelvic People. Its mission is to end painful sex. “Sexual pain is labeled ‘psychological.’ Postpartum discomfort is labeled ‘expected.’ Menopause symptoms are labeled ‘inevitable.’”Sauer recalled that many customers arrive after years of being told to tolerate pain. “When your pain is dismissed, you stop asking for help,” she noted. That erosion of trust affects intimacy, confidence, and well-being.AI becomes meaningful, Sauer added, when it meets women before clinical language exists. “Ema lets us meet people at the exact intersection of ‘Something feels off’ and ‘I don’t know who to ask,’” she described.What Makes Women-Centered AI DifferentThese founders aren't just talking about what their AI can accomplish—they're equally focused on its limits. Humility came up constantly. So did ethical guardrails. So did knowing when to hand things off to a real person.“Emotion is data,” Patel argued. “Hesitation can signal uncertainty. Urgency can signal risk.” The system is not meant to diagnose or replace a clinician.Design principles include transparency, representation in training data, and respect for ambiguity. “Training AI for women does not mean scaling down male data,” Patel asserted. “It means building from women’s lived experiences, language, social context, and emotional texture.”Donnell framed trust as the ultimate metric. “If the room makes you feel vulnerable, if the timing doesn’t work, if the benefit is buried somewhere you can’t find it, none of it helps,” she said. Why AI For Women’s Health Is Finally EvolvingWhat's developing isn't one game-changing product—it's a rethink of what AI should do. These women-led companies prioritize guidance over prediction, helping women find coherence in systems that appear chaotic.“Motherhood is not innate. It’s learned,” Gordon-Martin reflected. Healthcare technology may be the same. After decades of building AI around a male default, women-led founders are teaching systems to listen, adapt, and guide rather than dictate.If AI for women’s health is going to transform healthcare, it may do so not by telling women what is wrong, but by helping them find their way forward.