Sumesh Sasidharan of the Faculty of Medicine at Aix-Marseille University explores how transformations in medtech may not impact all patients equally.
AI-powered digital twin technology could transform how doctors understand and treat heart disease. But if the medical data used to build these virtual models overlook biological differences between women and men, the promise of truly personalised medicine may remain incomplete.
Artificial intelligence is beginning to reshape how doctors study and treat heart disease. One of the most ambitious ideas is the ‘digital twin’, a computer model built from a patient’s medical data that allows researchers to simulate how a disease might develop and how treatments might work.
In cardiology, these models combine medical imaging, clinical records and biological data to create a virtual version of the heart. In the future, doctors could potentially test treatment strategies on this digital model before applying them to the patient.
But an important scientific question is emerging: What if the medical data used to build these models are missing important biological differences between women and men?
As digital health technologies move closer to clinical practice, ensuring these tools reflect the full diversity of human biology is becoming increasingly important.
In our research at Aix Marseille University on patient-specific computational models of inflammatory heart disease (MYOCAR3 funded by Civis Alliance), we are beginning to see how differences in immune responses between women and men could influence how these diseases develop and how they might appear in future digital models.
Digital twins are attracting growing attention across Europe as a way to advance precision medicine.
Instead of treating patients based on average responses observed in large populations, researchers hope to build personalised models that capture the unique biological characteristics of each individual. Several European initiatives are exploring this approach.
The European Virtual Human Twin Initiative, supported by the European Commission, aims to accelerate the development of digital twin technologies for healthcare. Other projects, such as SimCardioTest, focus on building patient-specific cardiovascular models to improve diagnosis and treatment planning.
These efforts bring together engineers, clinicians and data scientists to better understand complex heart diseases. But the success of these models depends heavily on one crucial factor: the quality and representativeness of the data used to build them.
Over the past decade, researchers have increasingly recognised that biomedical research has sometimes treated male biology as the default.
A widely cited analysis published in Nature reported that male animals historically outnumbered females by roughly five to one in many preclinical studies.
In cardiovascular medicine, this imbalance matters.
Heart disease remains the leading cause of death worldwide, responsible for nearly 18 million deaths each year, according to the World Health Organization.
Yet heart disease does not affect women and men in exactly the same way. Symptoms, disease mechanisms and responses to treatment can differ.
Inflammatory heart disease provides a striking example. Myocarditis, an inflammation of the heart muscle, can occur after viral infections and, in rare cases, after vaccination.
Global estimates suggest that myocarditis affects around 1.8 million people each year and occurs two to four times more frequently in men than in women, particularly among young adults.
Research published in journals such as Circulation suggests that these differences may be linked to variations in immune responses, hormonal influences and cardiac tissue biology.
For scientists developing digital heart models, this raises an important question: if datasets do not fully capture these biological differences, can digital twins accurately reproduce how the disease behaves in different patients?
These concerns are part of a broader shift in biomedical research towards what is known as sex and gender-sensitive medicine.
This emerging field recognises that both biological sex and sociocultural gender factors influence health, disease progression and responses to treatment.
Researchers are increasingly working to integrate these dimensions into medical research, clinical practice and healthcare education.
For example, the University Hospital Zurich Heart Center has developed consultations dedicated to gender-sensitive cardiology. Researchers analyse international datasets, identify patterns across large patient cohorts and generate new clinical data to better understand how sex and gender influence cardiovascular disease.
At the same time, European scientific collaborations are working to strengthen how sex differences are considered in research.
The European Initiative COST Action EU-SABV is the first Europe-wide effort that focuses on improving how “sex as a biological variable” is integrated into biomedical research, helping ensure studies produce findings that are both rigorous and relevant for diverse patient populations.
Together, these efforts aim to generate better data sets, the essential foundation for reliable digital health technologies.
Digital twins represent one of the most exciting frontiers in cardiovascular medicine. In the future, these models could allow doctors to simulate disease progression, test therapies virtually and tailor treatments to individual patients.
But the promise of digital medicine ultimately depends on the data that shape these models.
If those data fail to reflect biological differences between women and men, even the most advanced algorithms may miss part of the picture.
Ensuring that digital twins reflect the full diversity of human biology will, therefore, be essential. Only then can these technologies fulfil their promise of truly personalised medicine, not for an ‘average’ patient, but for every patient.
Sumesh Sasidharan is a biomedical engineer and senior researcher at Aix-Marseille University‘s Faculty of Medicine and a CIVIS3i Laureate, funded by the European Union. He was ranked among the top selected candidates globally and is the first Indian researcher to receive this prestigious fellowship. His research focuses on developing patient-specific computational models and digital twin frameworks for inflammatory heart diseases, with particular emphasis on acute myocarditis and immune-mediated cardiotoxicity.
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