Gender differences complicate rise of "digital twin" technology in medicine

Artificial intelligence is poised to transform how doctors diagnose and treat heart disease through the use of so-called "digital twins"—virtual replicas of patients built from their medical data. But scientists warn that the promise of truly personalised medicine could fall short if these AI-powered models fail to capture fundamental biological differences between women and men.

One of the most ambitious concepts emerging in modern medicine is the digital twin: a computer-generated model created using a patient's medical imaging, clinical records and biological information that allows researchers to simulate how a disease may progress and how different treatments might perform before they are administered in real life.

In cardiology, digital twins could eventually enable physicians to test multiple treatment strategies on a virtual version of a patient's heart, tailoring care to the individual while reducing uncertainty and improving outcomes.

Researchers are already working to make that vision a reality. A project led by Aix-Marseille University, known as MYOCAR3 and funded through the Civis Alliance, is investigating patient-specific computational models of inflammatory heart disease. With their findings highlighted in an article by The Conversation, these scientists are examining how differences in immune responses between women and men influence disease progression—and how those distinctions should be incorporated into future digital heart models.

The work forms part of a broader European effort to develop digital twin technology for healthcare.

The European Commission-backed Virtual Human Twin (VHT) Initiative aims to accelerate the adoption of digital twins across medicine, while projects such as SimCardioTest are developing patient-specific cardiovascular models designed to improve diagnosis, treatment planning and clinical decision-making.

These initiatives bring together engineers, physicians and data scientists to build increasingly sophisticated simulations of the human heart. Yet their success ultimately depends on one critical factor: the quality, diversity and representativeness of the medical data used to train the models.

Over the past decade, researchers have become increasingly aware that much of biomedical research has historically relied disproportionately on male subjects. A widely cited analysis published in Nature found that male animals outnumbered females by roughly five to one in many preclinical studies, raising concerns that important biological differences have been overlooked.

The implications are particularly significant in cardiovascular medicine. Heart disease remains the world's leading cause of death, claiming nearly 18 million lives annually, according to the World Health Organization.

Gender-specific medicine

Yet cardiovascular disease does not affect women and men in identical ways. Symptoms often differ, disease mechanisms vary, and patients may respond differently to the same treatments.

Inflammatory heart disease illustrates this challenge. Myocarditis—an inflammation of the heart muscle that can develop after viral infections and, in rare cases, following vaccination—is estimated to affect around 1.8 million people globally each year. Studies indicate it occurs two to four times more frequently in men than in women, particularly among younger adults.

Research published in journals including Circulation suggests these differences stem from variations in immune system function, hormonal influences and the biological characteristics of heart tissue.

For developers of AI-powered digital twins, this presents a fundamental challenge. If training datasets fail to adequately represent these biological differences, the resulting models may not accurately predict how heart disease develops or responds to treatment across different patient groups.

The issue reflects a wider shift toward what researchers describe as sex- and gender-sensitive medicine—an emerging field that recognizes both biological sex and sociocultural gender as important factors influencing disease risk, progression and therapeutic response.

Medical researchers are increasingly working to integrate these dimensions into scientific studies, clinical practice and healthcare education. Among the institutions leading this effort is the University Hospital Zurich Heart Center, which has established dedicated gender-sensitive cardiology consultations. Its researchers analyze international datasets, identify patterns across large patient populations and generate new clinical evidence to better understand how sex and gender shape cardiovascular disease.

As AI becomes more deeply embedded in healthcare, many scientists argue that the effectiveness of digital twins will depend not only on increasingly powerful algorithms, but also on ensuring the data behind them accurately reflects the full diversity of the patients they are intended to serve.

By Nazrin Sadigova