Estimating the physiological age of a patient can be used as an overall health assessment as well as to illuminate initial risk factors before the onset of chronic disease. In 2017, we have developed and published a deep learning model that can be used to predict a patient’s age from their recent vital signs and lab tests using the Mount Sinai Medical Center (MSMC) Electronic Medical Records (EMR). The deep learning model was developed from analysis of 385,918 EMR from patients with ages ranging from 18-85 years old. Physical and Laboratory Measurements to Age (PALM2A) is a web-based and mobile application that predicts physiological age from 21 commonly measured parameters (vitals and labs) using the original deep learning model but with fewer variables. The reduced parameter model has a mean absolute standard deviation error from true age of 8.89 years. Besides predicting the patient age, PALM2A displays graphs of each parameter to illustrate to the user how their values compare to the population distributions and averages for each parameter. In summary, PALM2A can be used to assist adult users to monitor their overall health status and motivate patients to practice preventative medicine.