article · Indian Journal of Clinical Cardiology
Sudden cardiac arrest represents a substantial global public health challenge, causing nearly one thousand deaths daily around the world. The vast majority of these events, approximately eighty percent, happen outside hospital settings. Survival rates remain low, standing at under twenty percent for out-of-hospital victims and around thirty percent for individuals who experience cardiac arrest inside a hospital. When recognition of sudden cardiac arrest is delayed, and prompt, high-quality cardiopulmonary resuscitation is not started immediately, patients face severe neurological complications, including vegetative states and post-anoxic coma. Combining human medical expertise with artificial intelligence offers a pathway to substantially improve outcomes in sudden cardiac arrest. Such integrated systems can support emergency physicians by assisting with crucial clinical decision-making during patient management and the prediction of patient prognoses.
Sudden cardiac arrest is widely fatal, with most incidents happening away from immediate hospital care. Delayed intervention often causes irreversible brain damage or death. Introducing artificial intelligence into emergency settings could help clinicians recognise cardiac emergencies faster and make better treatment choices, potentially boosting low survival rates and reducing permanent neurological injuries.
The work highlights clinical decision-support and outcome prognostication tools as primary applications for emergency physicians managing sudden cardiac arrest. However, as this is a review presenting conceptual benefits rather than a tested algorithm or product evaluation, the technology appears to be at an early conceptual stage, and the abstract gives no specific details regarding real-world readiness or commercial testing.
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Sudden cardiac arrest is a major public health problem as it accounts for nearly 1,000 deaths per day worldwide. An estimated 80% of these occur outside of hospitals, with less than 20% survival for out-of-hospital victims and around 30% for in-hospital victims. Delays in recognizing sudden cardiac arrest and initiating high-quality cardiopulmonary resuscitation result in significant neurological problems like post-anoxic coma and vegetative states. Human expertise integrated with artificial intelligence will contribute to a dramatic improvement in sudden cardiac arrest outcomes by aiding emergency physicians in making critical decisions in the management and prognostication of patient outcomes.
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DOI: 10.1177/26324636251323008
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