This I-Corps project focuses on the development of a digital tool to better diagnose chronic pain, which affects over 100 million adults in the United States and contributes to an estimated $560–$635 billion annually in lost productivity and healthcare costs. Misdiagnosis or delayed treatment can lead to serious problems, including unnecessary reliance on opioid medications. Currently, doctors mostly depend on patient self-reports and their own judgment, but that doesn’t always give the full picture of the need for pain medications. Some patients struggle to communicate their pain, and important biological signals—like heart rate and hormone levels—often go unnoticed. This solution is an artificial intelligence system that can analyze multiple sources of information, including language, behavior, and biological data, to provide more accurate insights. By improving diagnosis, the technology aims to reduce ineffective treatments and contribute to a more scientific approach to managing chronic pain. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of a digital phenotyping platform that combines natural language processing, machine learning algorithms, and metabolic signal analysis to identify patterns in chronic pain expression. The system analyzes patient-generated language, behavioral patterns such as daily ac