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Fertility
Governments ‘fail to to address’ declining global fertility rates, warn doctors
While there have been advances in fertility care, doctors have said that equity remains a challenge across countries, cultures and economies

The global fertility rate is declining and most governments are failing to recognise and address the impact on economies and societies, a group of doctors has warned.
Not including the effects of migration, many countries are predicted to have a population decline of more than 50 per cent from 2017 to 2100, the doctors wrote in a new paper, published in Human Reproduction Update.
“By 2050, 77 per cent of predominantly high-income countries, and by 2100, 93 per cent of all countries will have a total fertility rate below the replacement level of 2.1 children per woman,” the paper’s authors, who included fertility specialists from Australia, Chile, Denmark, Egypt, Greece, The Netherlands, South Africa, Switzerland, the UK and the US, added.
With underpopulation becoming a great concern in an increasing number of countries, the paper aimed to provide recommendations for what governments, policymakers, companies, healthcare professionals and patients could do to address infertility risk factors and make fertility care more affordable and accessible.
“Choosing to have a family is a human right,” said Bart CJM Fauser, co-first author of the paper, scientific director at the International Federation of Fertility Societies (IFFS) and professor emeritus of reproductive medicine at Utrecht University.
“However, access to fertility care is often unaffordable, inaccessible, and inequitable and that needs to change.”
The authors pointed out that there had been “major advances in fertility care that have dramatically improved family building opportunities over the last three decades,” but equity remains a challenge across countries, cultures and economies.
Edgar Mocanu, IFFS president, said: “The good news is that infertility is often preventable. A simple step is offering balanced fertility and contraceptive education so that everybody can decide when to prevent pregnancy and when it is ideal for them to start a family, if they choose.”
One in six people of reproductive age grapple with infertility and the problem affects women and men equally. Some sexually transmitted infections, smoking, excessive alcohol intake, obesity and poor nutrition could negatively affect both male and female fertility, the physicians wrote.
Alarmingly, infertility risk factors, such as air pollution and the proliferation of harmful and poorly regulated chemicals, are increasing, making it critical for policymakers to develop and implement policies that reduce environmental infertility risk factors.
Solutions to improving birth rates, the doctors said include “policy measures supporting families and working women” such as compensation for extended pregnancy leave and childcare, parental leave and increased access to assisted reproductive technology services (ART).
Dr Luca Gianaroli, IFFS director of education and former chairman of the European Society of Human Reproduction and Embryology, said: “While more than 10 per cent of all children are born with fertility assistance in some wealthy countries, there is great variation in access to care and the high cost remains a barrier across the board.
“A limited number of countries have started public funding of fertility treatment to mitigate falling birth rates and the IFFS is asking that more countries consider providing financial assistance for individuals needing fertility care.
“The economic benefits to society of providing fertility care clearly exceed the cost of treatment and these benefits will only increase as populations become more aged,” he added.
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Fertility
AI could transform ovarian care through personalisation, study finds

AI could transform ovarian care by personalising cancer and fertility treatment, but more clinical validation is needed before routine use.
A systematic review and meta-analysis found AI models showed high diagnostic accuracy for ovarian cancer when combining data such as ultrasound scans and blood test results.
Across 81 studies, AI models correctly identified ovarian cancer in around nine out of 10 cases, with pooled rates of 89 to 94 per cent.
They were also highly accurate at ruling out ovarian cancer when it was not present, with specificity of 85 to 91 per cent.
The analysis also found that explainable AI tools could predict complete surgical cytoreduction in advanced ovarian cancer.
Complete surgical cytoreduction means removing all visible cancer during surgery, which can be an important goal in treatment planning.
The tools achieved a pooled AUC of 0.87. AUC is a measure of how well a model distinguishes between different outcomes, with higher scores showing stronger performance.
In reproductive medicine, AI algorithms helped physicians optimise ovarian stimulation protocols and predict follicular growth during IVF.
Ovarian stimulation is the use of hormones to encourage the ovaries to produce eggs, while follicles are the small sacs in the ovaries where eggs develop.
The review found AI could reliably model ovarian response in IVF with a pooled AUC of 0.81.
However, researchers said challenges remain in translating promising research findings into routine clinical practice.
They identified substantial variation across studies, driven by retrospective study designs, variable AI systems and a lack of standardised validation.
Only 22 per cent of analysed studies reported prospective, multicentre external validation, where models are tested forward in time across multiple healthcare settings.
The authors called for rigorous validation to help close the gap between research and routine clinical practice, alongside standardised methodological and reporting frameworks, smooth integration with clinical workflow and robust governance to support responsible and ethical AI use.
They concluded: “Artificial intelligence is a transformative force in the management of ovarian conditions.
“In gynaecologic oncology, AI enhances every phase of care, from early detection and accurate diagnosis to prognostic stratification and surgical planning.”
In reproductive medicine, AI personalises ovarian stimulation and refines the diagnosis of heterogenous endocrine disorders such as PCOS.
PCOS, or polycystic ovary syndrome, is a hormonal condition that can affect periods, skin, weight and fertility.
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