Tools that predict mortality risk using artificial intelligence algorithms are becoming increasingly available online without charge. These applications typically employ statistical models trained on large datasets of demographic and health information to estimate an individual’s life expectancy. Users often input data such as age, gender, lifestyle habits, and medical history to receive a personalized risk assessment. One hypothetical example could involve a user entering information about their smoking status, exercise frequency, and family history of heart disease to receive an estimated probability of surviving to a certain age.
The potential benefits of accessible mortality prediction tools are substantial. These applications can empower individuals to make more informed decisions about their health and lifestyle. By providing personalized insights into potential risks, these tools can motivate positive behavioral changes, such as increased physical activity or adherence to preventative medical screenings. Historically, actuarial science and traditional statistical methods have been used to assess mortality risk, primarily within the insurance industry. The emergence of readily available online tools driven by complex algorithms represents a democratization of these types of assessments, potentially impacting public health outcomes.
