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Related Concept Videos

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Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
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Related Experiment Video

Updated: Jan 10, 2026

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function
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Cardia-AI: Passive Cardiac Event Monitoring Using Smartwatch Sensors and Predictive Analysis via Large Language

Elyan Ali Momin1, Hamid Mansoor1

  • 1Computer Science, University of Manitoba, Winnipeg, CAN.

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|November 24, 2025
PubMed
Summary
This summary is machine-generated.

Cardia-AI integrates wearable device data with electronic health records to create educational summaries for cardiovascular disease management. This AI tool aims to improve patient understanding and timely follow-up care under clinical supervision.

Keywords:
digital healthgenerative ailarge language models (llms)sensor datawearable devices

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Area of Science:

  • Biomedical Informatics
  • Artificial Intelligence in Healthcare
  • Cardiovascular Disease Management

Background:

  • Cardiovascular diseases necessitate continuous, context-aware monitoring beyond isolated clinical encounters.
  • Integrating real-time wearable data with longitudinal electronic health records (EHR) is crucial for comprehensive patient assessment.

Purpose of the Study:

  • To develop and validate Cardia-AI, a proof-of-concept pipeline for time-aligning smartwatch signals with EHR data.
  • To utilize a compact medical language model for generating grounded educational summaries from synchronized patient data.
  • To establish safety guardrails and escalation guidance within the AI system for patient education and navigation.

Main Methods:

  • Developed Cardia-AI, a pipeline that time-aligns smartwatch data (heart rate, blood pressure, oxygen saturation) with EHR.
  • Employed a lightweight medical large language model (BioMistral-7B) with retrieval from curated sources for summary generation.
  • Implemented guardrails for educational outputs and explicit escalation protocols for red-flag symptoms.

Main Results:

  • Cardia-AI successfully compiled synchronized smartwatch trends with EHR entries in two scenario-based validations.
  • The system referenced specific measurements and diagnoses from the prompt context and recorded transcripts for audit.
  • Feasibility and safety guardrails were demonstrated; clinical effectiveness and diagnostic accuracy were not assessed.

Conclusions:

  • Pairing wearable data streams with a domain-tuned language model shows potential for reducing cognitive load from complex data visualizations.
  • This approach may shorten the time from symptom onset to appropriate follow-up, supporting clinician oversight.
  • Prospective evaluations are planned to assess clinical effectiveness and patient outcomes.