Despite huge advances in public health and attempts to address health inequalities, a persistent and under-recognised problem continues to undermine outcomes for a significant portion of the population: men are still far less likely than women to engage with preventive healthcare, to attend routine screenings, or to seek help from their GP. This is leading to many serious and treatable conditions being diagnosed late, when the options are fewer, the interventions more invasive, and the prognosis poorer.

The reasons behind this lack of engagement are quite well-documented.

Men, particularly those of working age, often cite lack of time, inconvenience, uncertainty around what tests are relevant, and a reluctance to seek help when feeling “well”, or unwell for that matter. Preventive healthcare has not traditionally been framed in a way that addresses men’s reluctance to engage and changes to address this have been slow in coming. Asking men to proactively book time off work, navigate a clinic-based system, and undergo testing for conditions they may not perceive as an immediate threat simply does not reflect the realities of their day-to-day lives and priorities.

This challenge is compounded by the limitations of many existing screening programmes, which, though better than no screening at all, don’t seem to have made the leaps and bounds the rest of the medical world has over the last 10 years in terms of personalisation and improving access. The most common approach remains the blunt tool of age-based screening, in which all men above a certain age are treated the same, regardless of the wide variation in personal risk.

While administratively simple, this is a crude and increasingly inefficient method of population health management. Two men of the same age may have radically different health trajectories based on a combination of genetic and epigenetic factors, biomarker trends, ethnicity, lifestyle, comorbidities, and socioeconomic status. To apply the same screening threshold to both is not only wasteful, in a cash-strapped NHS, but potentially harmful, leading to over-investigation in low-risk individuals, while missing opportunities for early detection in those at higher risk but below the screening age.

This is where newer, more intelligent screening and diagnostic models offer an opportunity to reframe how we approach men’s health. Rather than wait for symptoms or age triggers, we now have the tools to offer screening that is more personalised, more accurate, and significantly more accessible. The expansion of decentralised testing infrastructure, including at-home blood testing, workplace screening, and remote monitoring, means that the initial step in the healthcare journey can now occur without disruption to a patient’s schedule.

The technological opportunity doesn’t end with access.

Artificial intelligence is now playing an essential role in enhancing the value and precision of diagnostics; indeed it is making it increasingly difficult for us to define what is a biomarker or what is a diagnostic. By integrating large volumes of clinical data, ranging from biomarkers to family history and data from wearables, AI risk models can stratify patients with a level of accuracy well beyond traditional population health tools. In cardiovascular disease, for example, machine learning models can now predict risk based on patterns of inflammation, lipid subfractions, glucose variability and more, rather than relying solely on crude cholesterol cut-offs or arbitrary risk scores. In cancer screening, AI models are already improving specificity in prostate cancer detection by modelling PSA kinetics and integrating longitudinal health data, helping to reduce unnecessary anxiety and invasive follow-up in low-risk men, whilst enabling earlier detection in men who would otherwise have been missed due to their age.

Perhaps the greatest strength of these technologies lies in their ability to make risk dynamic rather than static. Health risk is not something that can be adequately captured in a single appointment or a single biomarker reading. Instead, risk evolves over time, and so too should the way we screen for disease. By allowing patients to test at regular intervals in the community and by analysing change over time, we can identify disease earlier and intervene with more confidence. This is particularly valuable in male patients, who may otherwise go many years between appointments.

It is worth emphasising that more targeted, intelligent diagnostics are not simply about improving outcomes. They also improve efficiency. By screening according to real risk rather than age-based assumptions, we can reduce unnecessary testing, concentrate resources on higher-risk individuals, and reduce healthcare costs. At a system level, this is essential as we seek to scale preventive care while managing constrained budgets. At a patient level, it means more relevant care, better outcomes, and higher trust in the healthcare system.

The inequality in men’s health is not a failure of medicine; it is a failure of delivery.

Smarter, personalised, and more accessible diagnostics, can help address inequalities in healthcare outcomes for men, and the population as a whole.