Developing Responsible AI Monitoring Technologies for Chronic Care

Abstract: Data from everyday devices are increasingly being repurposed to monitor symptoms of heterogeneous chronic conditions: conditions where symptoms present diversely across individuals, and the devices used for symptom monitoring vary across a population. While these variations may not greatly affect personal tracking applications, they pose challenges towards use in clinical settings. Specifically, how can we develop technologies that accurately identify patient-specific symptoms, and ensure reliable symptom monitoring? How can these tools support patients and their healthcare providers? In this talk, I will discuss my work designing, developing, and evaluating AI-driven symptom monitoring technologies to address these challenges. I will close by presenting my vision for a more responsible approach to develop these technologies – one that is deeply integrated with the needs of patients, healthcare providers, and other key stakeholders within our health system.

Bio: Dan Adler is a PhD Candidate in the College of Computing and Information Science at Cornell University. His research designs, develops, and evaluates novel data-driven technologies and AI models that support chronic care. Dan’s work has been published at top-tier venues in human-computer interaction (CHI, CSCW), ubiquitous computing (IMWUT), and digital health (npj Mental Health Research, BJPsych, JMIR). His research has been highlighted in the national media, cited in government reports, translated into interventions that support patients, and led to patentable systems. He is the recipient of an NSF Graduate Research Fellowship, and was a finalist for the Gaetano Borriello Outstanding Student Award at ACM UbiComp. Dan holds a Bachelor’s in Biomedical Engineering and Applied Mathematics and Statistics from The Johns Hopkins University. More information at: https://dadler.co