AI-powered digital twin developed to anticipate individual health trajectories
Key Takeaways:
- Researchers at the Weizmann Institute of Science have developed a personalised “digital twin” using artificial intelligence to predict disease risk, guide prevention strategies, and simulate treatment responses.
- This innovation builds on data from the Human Phenotype Project—a 25-year longitudinal study collecting in-depth biological, genetic, and lifestyle data from tens of thousands of participants worldwide.
- The AI model can estimate a person’s biological age across 17 body systems and has already shown success in detecting pre-diabetes and menopause onset earlier than conventional tests.
Rethinking personal health decisions
Many individuals naturally simulate potential outcomes before making major life decisions. However, doing so for one’s health—such as selecting an appropriate treatment or diet—is often much more complex. Personal biological differences make it difficult to predict how a given choice will affect a specific individual.
In response to this challenge, researchers from Professor Eran Segal’s laboratory at the Weizmann Institute of Science have developed an artificial intelligence-powered “digital twin.” This digital twin is capable of identifying an individual’s risk for certain diseases, simulating how they might respond to different interventions, and guiding preventative care decisions. Their findings, recently published in Nature Medicine, are rooted in data collected through the Human Phenotype Project—a large-scale initiative that has gathered detailed medical information from over 13,000 individuals.
Beyond the genome: Capturing the full human picture
Although the Human Genome Project, launched in 1990, identified tens of thousands of genes linked to human traits and diseases, it quickly became clear that genetics alone could not fully explain health outcomes. Environmental factors, the microbiome, ageing processes, and lifestyle play crucial roles.
To capture this broader picture, Professor Segal initiated the Human Phenotype Project in 2018. This 25-year longitudinal study involves comprehensive testing of participants every two years across 17 different body systems. The assessments include:
- Anthropometric measurements
- Nutritional intake tracking
- Ultrasound imaging
- Bone mineral density scanning
- Voice recordings
- Home-based sleep studies
- Two-week continuous glucose monitoring
- Gene sequencing
- Protein profiling
- Microbiome analyses of gut, vaginal, and oral samples
“When we launched the project in Israel in 2018, our initial goal was 10,000 participants,” Segal stated. “Since then, more than 30,000 people have signed up, and we hope to reach 100,000 in the future.”
The project is now expanding internationally, with new branches in Japan and the United Arab Emirates, the latter in collaboration with Professor Eric Xing from the Mohamed bin Zayed University of Artificial Intelligence. The age range of participants is also expanding to include both younger and older individuals, increasing the diversity of the data collected.
“We recognised the importance of sharing this resource with the scientific community and have now made it accessible digitally to research groups worldwide, while maintaining the privacy of the participants,” Segal added. “We believe that the data we have compiled will profoundly affect the field of medicine.”
Measuring biological age: A more nuanced health indicator
Traditional medicine tends to benchmark health indicators against average values for one’s age and sex. However, these reference ranges can miss important individual variations in health and disease risk.
To address this, a team led by Drs Lee Reicher and Smadar Shilo within Segal’s lab developed an AI model to calculate a person’s biological age by analysing 17 physiological systems across the body. The technology is based on a platform developed by Pheno.AI, a health-focused artificial intelligence company.
“The model assigns scores to each body system and compares these values to the expected values for the participant’s chronological age, sex and body mass index,” explained Segal. “Based on the deviation from these predicted values, the model determines the participant’s biological age. The older the apparent age of a body system, the greater the risk of associated diseases.”
One of the early successes of this model has been in the detection of pre-diabetes. By analysing blood glucose patterns across the lifespan, the team identified pre-diabetes in 40% of participants who had been classified as healthy by conventional testing.
Gender-specific patterns have also emerged. “While men’s biological age generally increases relatively linearly, we observe an acceleration in women’s biological ageing during their fifth decade of life,” Segal noted.
“Menopause is a pivotal event in many medical respects, and it appears to reset the biological age clock. For example, we found that a decrease in bone density is more strongly correlated with the time that passed since the onset of menopause than with chronological age. Furthermore, our measurements make it possible to detect the start of menopause early, so that hormonal treatment can be planned accordingly.”
Microbiome signatures for early disease detection
The Human Phenotype Project has also revealed new paths for early diagnosis of conditions such as breast cancer, endometriosis, and inflammatory bowel disease. These illnesses are often preceded by detectable changes in the composition of the individual’s microbiome. Such microbiome alterations form unique “signatures” that can be identified well before symptoms emerge.
The rise of the digital twin: Personalised medicine in action
The most transformative potential of the project lies in its application of AI to create a digital twin for each participant—a sophisticated, personalised model that simulates future health trajectories.
This ongoing work, led by doctoral researcher Guy Lutsker, involves training an AI system on the full suite of medical data from each participant. The model learns by repeatedly being asked to predict missing pieces of information based on the rest of the record. Over time, this training allows the AI to anticipate likely future events, such as disease onset or treatment response.
Already, the system has successfully forecast which individuals with pre-diabetes are most likely to progress to type 2 diabetes within two years. It has also been used to simulate the effects of different dietary interventions and medications, helping to determine which strategies are most likely to benefit each person.
Eventually, the digital twin model is expected to incorporate all aspects of the dataset, enabling the prediction of a wide range of health events. This may significantly reduce the trial-and-error process that currently characterises treatment decisions in many areas of medicine.
Bringing AI-driven health insights directly to participants
Professor Segal emphasised the critical role of the participants in enabling this progress:
“This achievement is primarily made possible by the community of participants in the Human Phenotype Project. It is a dedicated group of individuals committed to advancing medicine and to the continuous monitoring of their health.”
To empower participants with direct access to their health data and insights, the team is now developing a mobile application. This app will provide each person with a dynamic view of their individualised “health trajectory”—a predictive map of their future health status.
“We are living in an era of incredibly rapid change. The realms of health and medicine will undergo dramatic transformations in the coming years, becoming increasingly AI-driven.
“Our project is poised to be a leading global source of information and innovation, and this is all thanks to our participants. I want to take this opportunity to express my sincere gratitude to each and every one of you—your exceptional collaboration is the true driving force behind this revolution in medicine,” Segal concluded.
