Sreerag Raghunathan

Medical device teams are under increasing pressure to add intelligent monitoring capabilities while maintaining or improving existing performance, mitigating patient data privacy risks, and enabling clinicians to make better informed diagnoses. Through examples like EMG and adaptive prosthetics, we will show how systems can flag deviations from baseline in real time - supporting preventative analytics, improving responsiveness, and preserving privacy. Real-time healthcare doesn’t have to rely on the cloud. On-device AI brings intelligence directly to the patient, reducing latency and improving reliability. This session explores how embedded, on-device AI enables time-series analysis for patient-specific monitoring, allowing systems to detect physiological anomalies while maintaining clinician oversight.