Most IVF add-ons lack reliable evidence, with benefits either absent or inconclusive, the largest review of its kind has found.

More than 70 per cent of IVF patients in the UK, Australia and New Zealand reportedly pay for one or more additional treatments.

However, researchers found that most of the procedures, medicines and techniques had no effect on fertility or were backed by limited or low-quality evidence.

Unproven add-ons can also lead to false hope, greater financial strain and unnecessary medical procedures at an already difficult time for patients.

Dr Sarah Lensen, of the University of Melbourne, said: “In many countries, infertility care is largely provided by private clinics where IVF is highly commercialised, and some add-ons are extremely expensive.

“Our review finds a lack of evidence that most of the IVF add-ons we assessed provide any benefit to patients. Unproven add-ons can lead to false hope, greater financial strain and unnecessary medical procedures at what already can be a very difficult time for patients.”

Researchers said concerns have grown in recent years about potentially untrustworthy randomised controlled trials in reproductive medicine, including studies of IVF add-ons.

The team set out to review the effectiveness and safety of 10 commonly offered add-ons using trustworthy studies.

Researchers initially identified 157 potentially eligible randomised controlled trials but excluded 72 because of concerns about their reliability.

Randomised controlled trials compare treatments by assigning participants to different groups, helping researchers assess whether an intervention causes a particular outcome.

The team combined data from the remaining 85 trials in a meta-analysis, which brings together findings from several studies.

The review found no effect on fertility or inconclusive evidence for seven of the 10 add-ons examined.

These included acupuncture, which involves inserting thin needles into points on the body, and corticosteroids, medicines that reduce inflammation and suppress immune activity.

Endometrial receptivity testing was also not backed by reliable evidence. The procedure involves taking a sample from the lining of the womb to examine patterns of gene activity.

Another add-on was intralipid infusion, which delivers a fat-containing liquid into the bloodstream.

Researchers separately examined injections of platelet-rich plasma into the ovaries and infusions of platelet-rich plasma into the womb.

Platelet-rich plasma is made from a patient’s blood and contains a high concentration of platelets, which play a role in healing.

The seventh treatment was pre-implantation genetic testing for aneuploidy, which examines embryos to check whether they have the expected number of chromosomes.

The review found only weak evidence of a possible benefit from three other add-ons.

EmbryoGlue, an embryo transfer medium containing hyaluronic acid, may increase the probability of pregnancy and live birth. However, the evidence on live birth rates was not considered robust.

Endometrial scratching, a minor procedure that deliberately disturbs the lining of the womb, may also increase the probability of pregnancy and live birth.

Physiological intracytoplasmic sperm injection, known as PICSI, selects sperm based on their ability to bind to hyaluronic acid. Weak evidence suggested it may reduce the risk of miscarriage.

Lensen said: “There is widespread misinformation about IVF add-ons with private clinic websites and patient forums on social media – major information sources for patients – often overstating the benefits and omitting the costs and risks of add-ons.

“IVF clinics and clinicians should carefully consider whether it is appropriate to offer unproven add-ons, as their availability is often perceived by patients as implicit endorsement of benefit.”

Changes in AI mammogram scores may help predict breast cancer years before diagnosis, research involving more than 54,000 women suggests.

Scores rose steadily among women who later developed the disease but remained broadly stable among those who did not.

The increase could be detected up to six years before diagnosis and became much steeper during the final two years.

Researchers led by Professor Constance Lehman, of Harvard Medical School and healthcare technology company Clairity, analysed screening mammograms taken between 2009 and 2019.

They used a validated, open-source deep learning model to calculate five-year breast cancer risk scores from the images alone.

Deep learning is a form of artificial intelligence trained to recognise complex patterns in large amounts of data.

The model examined the whole mammogram rather than relying on a limited, predetermined feature such as breast density.

Models of this kind have performed better than traditional risk models and breast density alone when estimating a woman’s five-year breast cancer risk.

The study initially included 239,703 consecutive two-dimensional screening mammograms from 89,882 patients across six imaging sites spanning urban tertiary, community-based and rural settings.

All were standard bilateral full-field digital mammography examinations, taken with or without digital breast tomosynthesis.

Digital breast tomosynthesis uses multiple low-dose X-ray images to create a three-dimensional view of the breast.

After exclusions, the final analysis involved 54,014 women with a median age of 61 and a total of 158,807 mammograms.

Each woman contributed one index examination and up to six previous annual mammograms. Women had a median of three scans each.

For women who developed cancer, the index examination was their final screening mammogram within the year before diagnosis. For the cancer-free group, it was their final mammogram during the five-year study period.

The model did not use demographic information, clinical records or historical imaging data when calculating each score.

Of the women included, 817, or one per cent, were diagnosed with breast cancer within 365 days of their index examination.

This included 451 women, or 55 per cent, with invasive breast cancer and 118, or 14 per cent, with ductal carcinoma in situ, known as DCIS.

DCIS occurs when abnormal cells are found inside a milk duct but have not spread into the surrounding breast tissue.

The cancer type was unknown for the remaining 248 patients, representing 30 per cent of the cancer group.

A total of 682 cancers, or 83 per cent, were detected through screening, while 135, or 17 per cent, were interval cancers diagnosed between routine mammograms.

The other 53,197 women were not diagnosed with breast cancer during follow-up and formed the cancer-free comparison group.

Professor Lehman said: “We observed clinically relevant differences in risk trajectories between women who did and did not develop cancer. The increase in scores among cancer patients was detectable as early as six years prior to diagnosis and became more pronounced over time.”

Among women later diagnosed with the disease, the median score rose from 2.1 five to six years before diagnosis to 6.6 at the index examination.

Scores among cancer-free women remained stable, with median values ranging from 1.8 to 2.2 throughout the study.

The rise among women who developed cancer was steepest during the two years before their index examination.

Professor Lehman said: “These findings demonstrate signals, invisible to the human eye, in the image alone can predict future risk. This is exciting, because 85 per cent of women diagnosed with breast cancer do not have a significant family history of breast cancer or known genetic mutations.”

Most breast cancers are considered sporadic, meaning they are not driven by inherited genetic changes or a family history of the disease.

Traditional risk models have a limited ability to distinguish between women who will and will not develop breast cancer when used across large screening populations.

Researchers said tracking how scores change over time could provide more information than calculating risk at a single appointment.

Professor Lehman said: “AI-derived risk scores can identify patients who are otherwise predisposed to the disease, and our findings demonstrate that image-based AI risk scores evolve over time and that changes in those scores may provide additional information about future breast cancer risk.”

The patterns remained consistent when women were grouped by age and breast density.

Breast density describes the amount of fibrous and glandular tissue visible on a mammogram. Dense tissue can make cancers harder to detect and is also associated with an increased risk of the disease.

Researchers said image-based scores could support personalised screening and risk-reduction strategies without relying on self-reported or inconsistent clinical information.

Professor Lehman said: “These trends remained robust across subgroups defined by age and breast density, further supporting the generalisability of our findings. This is particularly relevant given persistent disparities in screening performance across patient populations. A dynamic biomarker approach grounded in the imaging data could mitigate some of these disparities by enabling risk-based personalisation that does not rely on self-reported or inconsistent clinical data.”

A biomarker is a measurable sign that can indicate a person’s health, disease risk or response to treatment.

Changing scores could eventually help clinicians identify women who may benefit from additional imaging or measures intended to reduce their risk.

Professor Lehman said: “With the power of AI, computer vision, and the ability to extract predictive data, we are able to apply the power of imaging to risk assessment and preventing disease from developing. Having a dynamic risk score opens up a whole new domain of more effective preventive therapies for breast cancer, similar to how we screen for and treat patients with high cholesterol and hypertension.”

AI image-based risk scores are included in the 2026 National Comprehensive Cancer Network guidelines.

The guidelines recommend that, from the age of 35, women with an elevated five-year risk score of more than 1.7 per cent consider breast MRI alongside annual mammography.

An AI image-based model approved by the US Food and Drug Administration is already being used to calculate five-year breast cancer risk at selected US healthcare institutions.

Women’s health startup Ovum has raised US$4m in seed funding to develop its AI health journal and expand research using women’s health data.

The round valued the Melbourne startup at US$18m.

Ovum plans to use the funding to develop its artificial intelligence technology and longitudinal datasets, which track health information over time to reveal changes and patterns.

The AI captures symptoms, lifestyle factors, biometric measurements, reproductive health stages, medication, appointments and medical reports.

It uses this information to identify health patterns and create summaries and questions for medical appointments.

Ovum previously raised US$1.7m in pre-seed funding in February 2025 before launching its health journal app in August that year.

Since then, the company says the app has grown by 30 per cent month on month and recorded more than 20,000 downloads.

It has captured 57,000 health data insights and hosted more than 107,000 AI health conversations involving women aged between 15 and 84.

Founder Dr Ariella Heffernan-Marks developed the idea while she was a third-year medical student experiencing chronic migraines and was told that her pain was caused by anxiety.

The company describes the resulting women’s health journal as combining technology and clinical research to make health information more actionable and equitable for women.

Heffernan-Marks said: “I’ve sat on both sides of the desk, as a patient and as a doctor, and that’s why this mission matters so much to me.

“For too long, women have had to navigate healthcare systems that were not designed around their lived experiences or backed by sufficient female health data. Ovum exists to help women better understand their bodies, advocate for themselves with confidence, and contribute to research that improves care for future generations.”

Private health insurer Medibank is an Ovum partner, alongside Fernwood Fitness, Sweat and Menopause Friendly Australia.

Australian Red Cross Lifeblood is also involved in a pilot examining productivity losses caused by women reducing their working hours or leaving employment for health reasons.

Earlier in 2026, Ovum launched clinical trials with St George Hospital and the Royal Hospital for Women to assess AI as a preventative health tool for women.

The research is examining how women currently manage their health, which digital tools they use and whether AI could support health confidence, self-advocacy and continuity of care.

Continuity of care means receiving connected and consistent support across different appointments, healthcare professionals and services.

The funding round was led by Admiralty Capital Group, with participation from Antler, Giant Leap, Aviron Investments, Foggy Valley Aotearoa, Brisbane Angels and Think & Grow.

Existing investor LaunchVic, which is due to merge with Breakthrough Victoria, also participated through its Alice Anderson Fund, which focuses on female founders.

Amanda Andriano, founding partner at Admiralty Capital Group, said the gender health gap was a problem that should not be tolerated.

She said: “Ovum combines mission, market timing and technical capability with an exceptional founder uniquely positioned to lead this movement, and we believe that creates the foundation for a company of global significance.”