Avoidant/Restrictive Food Intake Disorder (ARFID), a new diagnosis in the DSM-5, is an eating disorder that can emerge in early childhood, threatens optimal physical growth and social-emotional development, and has been reported to persist, for some, well into adolescence or adulthood. Food selectivity more broadly has been reported to be more elevated in families of lower income, while the accessibility and affordability of treatment for mental health patients in the underrepresented group are limited. Therefore, it is crucial to develop accessible, affordable, and effective therapies. We designed a unique clinical study that can be implemented at home, which provides patients with a framework to work towards overcoming the challenges associated with ARFID. During the intervention, participants are filmed and relevant facial information is collected, automatically analyzed with machine learning and computer vision, and delivered to medical experts to enhance the knowledge they use for clinical judgment. We automatically extract affect-related features right after the participants taste or smell a food they labeled as moderately challenging. We observed that facial action units activation provides interesting patterns helpful in understanding the patient’s experience throughout the food exposure treatment. This rich information enables quantification of the effectiveness of the currently investigated treatments and differentiation of patient-specific responses to them, potentially leading to scalable personalized medicine for ARFID.