Fertility
Are period tracking apps falling behind? Why GenAI is the missing piece

By Morgan Rose, Chief Science Officer at Ema
Period tracking apps have come a long way—from simple calendar-based logs to advanced cycle prediction tools.
But are these apps truly meeting women’s needs, or are they missing a critical opportunity?
The reality is that most period apps stop at data collection—they track symptoms but don’t translate that data into real-time, personalised support.
This is where Generative AI (Gen AI) can revolutionise the experience.
Why Period Apps Need Gen AI to Support Women
- Personalised, Real-Time Answers:
- Most period tracking apps offer static, one-size-fits-all predictions, but women’s cycles are dynamic. What happens when an app flags an irregular cycle? What if a user logs new, unexpected symptoms? Currently, many apps leave women to figure it out independently. Gen AI can change that by offering contextual explanations, possible causes, and next steps—without forcing users to Google symptoms or wait for a doctor’s appointment. A Journal of Medical Internet Research study found that women desire more personalised insights and guidance from period tracking apps, mainly for managing irregular cycles and understanding symptom patterns.
- A True Health Companion, Not Just a Calendar:
- Today, apps function as logbooks, not interactive guides. With Gen AI-powered assistants, users could have real-time conversations about their symptoms, emotional well-being, and health concerns. Instead of just logging mood swings, imagine an app recognising patterns and suggesting evidence-based strategies for symptom relief.
- Smarter Insights, Not Just Static Predictions:
- Period trackers use cycle history to make predictions, but what if a user skips logging for a few months? What if their symptoms shift due to lifestyle changes? Gen AI can fill the gaps by analysing past data, current trends, and external health factors (like stress, sleep, and exercise) to provide adaptive, real-time guidance. A survey analysis of menstrual cycle tracking technologies highlights the need for more sophisticated data analysis and personalised insights to improve the accuracy and effectiveness of period tracking.
- Bridging the Gap Between Tracking and Action:
- Most apps are passive—they remind users when their period is coming but don’t offer proactive health recommendations. Gen AI closes this gap by:
- Suggesting relevant health screenings based on symptoms
- Providing cycle-specific nutrition & lifestyle tips
- Helping users prepare for PMS symptoms before they hit
- Research suggests that women are increasingly using period tracking apps to manage their health proactively, and Gen AI can facilitate this by providing actionable recommendations and personalised guidance.
- Most apps are passive—they remind users when their period is coming but don’t offer proactive health recommendations. Gen AI closes this gap by:
Future-Proofing Femtech: AI as a Competitive Advantage
Apps that fail to integrate Gen AI risk becoming outdated and less engaging.
As women seek more intuitive, intelligent tools, those that leverage AI will become the go-to platforms for personalised reproductive health support.
AI-powered platforms like Ema are bridging this gap by ensuring personalised and medically relevant insights—helping women access smarter, more adaptive health support, no matter their background or needs.
The Bottom Line
Tracking is just the beginning—women deserve more than a digital calendar. Period apps that don’t evolve with Gen AI risk missing a major opportunity to support users in real-time.
It’s time to move beyond symptom tracking and into the AI-driven, personalised health guidance era.
Will your period app keep up?
Morgan Rose is a Certified Nurse Midwife, Women’s Health Nurse Practitioner, and International Board-Certified Lactation Consultant with over a decade of experience supporting women’s health.
As the Chief Science Officer at Ema, Morgan combines her expertise with her passion for empowering women. She lives in New York City with her spunky daughter and their beloved dog.
Learn more about her work with Ema here.
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Fertility
AI patch could detect hidden hormone disruptions behind unexplained infertility

Even when standard clinical tests show normal hormone levels, men and women may have hidden problems in how their reproductive hormones are timed and coordinated, potentially affecting fertility, new research suggests.
The findings suggest reproductive health may depend not only on hormone levels in the bloodstream but also on the rhythm, timing and synchronisation of hormone changes across hours, days and the menstrual cycle.
Researchers said a wearable skin sensor patch, combined with artificial intelligence, could help detect endocrine dysfunction earlier and support more personalised fertility care.
Unexplained infertility affects about 15 to 30 per cent of couples and is diagnosed when standard investigations reveal no clear cause.
In men, current tests for infertility or hypogonadism, defined clinically as low testosterone, often include a single morning serum testosterone measurement.
In women, fertility assessment typically examines menstrual cycle characteristics and reproductive hormones such as luteinising hormone, follicle-stimulating hormone, oestradiol and progesterone.
However, reproductive hormones are not static markers. They are dynamic biological signals that rise and fall in regulated patterns throughout the day and across the menstrual cycle.
Testosterone, for example, follows a diurnal rhythm, meaning it changes across the day, while female reproductive hormones act through coordinated feedback loops involving the hypothalamic, pituitary and ovarian systems.
A single blood test may therefore miss clinically important disruption in hormonal timing.
In one study, Dr Tinatin Kutchukhidze, from the University of Oxford, examined 102 men in Georgia and the UK.
The participants were aged 22 to 38 and had normal morning total testosterone levels, measured at 12 to 35 nanomoles per litre, with or without infertility or symptoms of hypogonadism.
Hypogonadism is a condition in which the body produces too little testosterone or other sex hormones.
Kutchukhidze and colleagues used wearable AI-enabled skin sensor patches to measure testosterone levels every 15 minutes across four days.
The team found that men with symptoms had significantly disrupted testosterone rhythms, despite standard laboratory tests showing normal testosterone levels.
These previously undetected rhythm abnormalities were also associated with reduced sperm concentration and symptoms of androgen deficiency.
Androgens are hormones, including testosterone, that play an important role in reproductive health.
Kutchukhidze said: “For the first time, we have been able to track androgen patterns in real time across several days with a novel, non-invasive, continuous, AI-driven testosterone monitoring patch, compatible with Android and iPhone mobile devices.
“Previous research suggests that a normal morning testosterone level is sufficient to exclude clinically significant androgen deficiency. However, our findings challenge that assumption by demonstrating that men with normal serum testosterone may still exhibit marked disturbances in hormonal rhythmicity associated with reproductive dysfunction.”
According to the abstract, the study compared 54 men with infertility or hypogonadal symptoms with 48 age-matched healthy controls.
Mean morning serum testosterone did not differ significantly between symptomatic men and controls, at 22.4 ± 3.1 compared with 23.1 ± 3.5 nanomoles per litre.
Continuous AI-assisted monitoring, however, revealed significant differences in androgen dynamics.
Men with symptoms had lower diurnal amplitude than controls, at 5.2 ± 1.1 compared with 8.7 ± 1.4 nanomoles per litre.
The AI-derived rhythm indices predicted subclinical dysfunction with an area under the curve of 0.87, compared with 0.61 for static serum testosterone testing.
In diagnostic research, the area under the curve is used to assess how well a test distinguishes between groups, with higher values indicating stronger discrimination.
A second study by Kutchukhidze’s team examined female reproductive hormone rhythms.
The researchers developed an AI-driven metric called Endocrine Rhythm Integrity to assess whether reproductive hormones were changing in the correct pattern, at the correct time and in the correct relationship to one another across the menstrual cycle.
Endocrine refers to the hormone system, while endocrine dysfunction means hormones are not being produced or regulated in a typical way.
The team analysed data from 312 women aged 18 to 22 who had self-reported regular menstrual cycles.
Participants included fertile controls and women with unexplained infertility.
The researchers assessed key reproductive hormones during the luteal phase, including luteinising hormone, follicle-stimulating hormone, oestradiol and progesterone.
The luteal phase is the part of the menstrual cycle after ovulation. Ovulation is the release of an egg from the ovary.
They also incorporated physiological data such as basal body temperature, heart rate and sleep patterns.
Basal body temperature is the body’s resting temperature and can shift slightly around ovulation.
The study found that women with unexplained infertility had lower Endocrine Rhythm Integrity scores even when conventional hormone levels appeared normal.
Lower scores predicted infertility and were also associated with a higher incidence of implantation failure, when an embryo does not successfully attach to the womb lining.
Kutchukhidze said: “Our study reveals that a woman may have a seemingly healthy menstrual cycle and normal hormone levels but still experience hidden endocrine dysfunction that affects her ability to conceive.
“Rather than analysing hormone levels as isolated values, Endocrine Rhythm Integrity evaluates whether reproductive hormones are changing in the correct pattern, at the correct time and in the correct relationship to one another across the menstrual cycle.”
In the female study, mean cycle length did not differ significantly between fertile and infertile groups, at 28.9 ± 2.3 compared with 28.9 ± 2.5 days.
Endocrine Rhythm Integrity scores, however, were lower in the infertility group, at 0.61 ± 0.12 compared with 0.78 ± 0.10.
Disrupted endocrine rhythm integrity was observed in 64 per cent of infertile participants despite hormonally normal mid-luteal progesterone levels.
The metric independently predicted infertility status after adjustment for age, body mass index and anti-Müllerian hormone.
Anti-Müllerian hormone is made by reproductive tissues and is best known as a marker of ovarian reserve, meaning an estimate of the number of eggs remaining in the ovaries.
Receiver operating characteristic analysis indicated that Endocrine Rhythm Integrity identified infertility more effectively than cycle length or single-time-point progesterone assessment.
Lower Endocrine Rhythm Integrity scores were also associated with a higher incidence of implantation failure.
Kutchukhidze said: “Our AI-driven rhythm analyses were significantly better at identifying subclinical reproductive dysfunction than conventional testing, suggesting that both female and male endocrine disorders may not simply be disorders of hormone quantity, but rather disorders of hormonal timing, synchronisation and biological rhythm.”
The team will next assess whether the tool can reliably predict fertility outcomes across different reproductive conditions in larger and more diverse populations.
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