Large Language Models in Ambulatory Devices for Home Health Diagnostics:
  A case study of Sickle Cell Anemia Management
Oluwatosin Ogundare Subuola Sofolahan
This study investigates the potential of an ambulatory device that
incorporates Large Language Models (LLMs) in cadence with other specialized ML
models to assess anemia severity in sickle cell patients in real time. The
device would rely on sensor data that measures angiogenic material levels to
assess anemia severity, providing real-time information to patients and
clinicians to reduce the frequency of vaso-occlusive crises because of the
early detection of anemia severity, allowing for timely interventions and
potentially reducing the likelihood of serious complications. The main
challenges in developing such a device are the creation of a reliable
non-invasive tool for angiogenic level assessment, a biophysics model and the
practical consideration of an LLM communicating with emergency personnel on
behalf of an incapacitated patient. A possible system is proposed, and the
limitations of this approach are discussed.