Cancer is the second leading and the most expensive cause of death in the United States. With more than 500 million visits a year made to primary care physicians, prevention and early detection of cancer by identification of at-risk individuals in the primary care setting can significantly reduce the cancer care and cost burden to individuals and society at large. The Perfect Medical Assistant ™M (PMA), a patented, conversational Al-powered voice and image-based software application that collects and assesses patient data can assist primary care physicians to improve identification of at-risk patients and improve their productivity. In Phase I, we will demonstrate that the PM effectively overcomes the two major barriers to widespread adoption by PCPs, 1. patient acceptance and usability 2. easy integration into and no-disruption of physician workflows. AIM 1. Demonstrate the feasibility and acceptability of the PMA in a population of potential patient users by testing on up to 400 incentivized, but diverse patients. AIM 2. Demonstrate that the PMA and the generated SOAP (Subjective, Objective, Assessment, Plan) formatted clinical note can be integrated into PCPs' electronic health record systems and workflows as the primary patient intake and cancer risk assessment tool by integrating into the HRs of two PCP practices of at least 25 PCPs each.