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This study investigates the potential of an ambulatory device thatincorporates Large Language Models (LLMs) in cadence with other specialized MLmodels to assess anemia severity in sickle cell patients in real time. Thedevice would rely on sensor data that measures angiogenic material levels toassess anemia severity, providing real-time information to patients andclinicians to reduce the frequency of vaso-occlusive crises because of theearly detection of anemia severity, allowing for timely interventions andpotentially reducing the likelihood of serious complications. The mainchallenges in developing such a device are the creation of a reliablenon-invasive tool for angiogenic level assessment, a biophysics model and thepractical consideration of an LLM communicating with emergency personnel onbehalf of an incapacitated patient. A possible system is proposed, and thelimitations of this approach are discussed.

Large Language Models in Ambulatory Devices for Home Health Diagnostics: A case study of Sickle Cell Anemia Management