Unlike traditional health assessments, which often focus on a single condition, Delphi-2M analyzes the risks of more than a thousand diseases simultaneously. These include chronic conditions such as diabetes and hypertension, but also neurological disorders, rare genetic diseases, and cancers. The tool relies on sophisticated machine learning algorithms that continuously learn from data and adapt over time, which means its accuracy improves as the database grows.

What makes this technology particularly compelling is its capacity for personalization. Based on individual patterns from lifestyle choices to genetic predisposition, it can provide users with a clearer picture of their future risks and suggest specific measures, such as preventive screenings, lifestyle changes, or targeted therapies. In this way, the AI tool does not replace doctors but acts as additional support for decision-making and preventive care planning.

Still, experts warn that deploying such systems comes with challenges. Data privacy remains a central concern, as analyzing sensitive health information requires strict security protocols. Algorithms can also make mistakes, which means results must always be interpreted alongside professional medical judgment.

Despite these obstacles, Delphi-2M highlights the direction in which modern medicine is moving, toward predictive and personalized care. If proven effective in practice, it could significantly reshape how people approach their health: shifting from a passive response once illness occurs to a proactive model that emphasizes prevention and long-term quality of life.