Editors: Dr. R. Balachandar

Dr. K. Ashok Kumar (2025)

ISBN: 978-93-94174-20-7

AI-Enabled Accident and Emergency Care: Smart Triage, Monitoring, and Critical Decision-Making

Prema Rathinam^1*, Senthilkumar Chelladurai¹, Sebastin Varghese², Mageshwari Rajendran³, Chandrasekaran Padmanaban⁴ and Sabitha Rajamanickam⁵

¹Department of Pharmaceutics, Sir Issac Newton College of Pharmacy, Nagapattinam, Tamilnadu, India

²Department of Pharmaceutical Chemistry, Malik Deenar College of Pharmacy, Kasaragod, Kerala, India

³Department of Pharmacology, Sree Bhavani College of Pharmacy, Veppur, Cuddalore, Tamilnadu, India

⁴Department of Pharmaceutics, Shree Krishna College of Pharmacy, Chengam, Tamilnadu, India

⁵Department of Pharmaceutics, School of Pharmacy, Dhanalakshmi Srinivasan University, Trichy, Tamilnadu, India

Accident and emergency (A&E) departments serve as a critical gateway to healthcare systems, where rapid assessment, accurate decision-making, and timely intervention are essential for saving lives. Modern emergency units face increasing challenges, including overcrowding, rising case complexity, workforce shortages, and growing expectations for high-quality, rapid care. Traditional emergency care models rely heavily on manual triage, intermittent monitoring, and clinician-driven workflows, which often struggle to meet contemporary demands. Artificial intelligence (AI) offers a transformative opportunity to redesign emergency services through intelligent triage, continuous monitoring, predictive analytics, and automated operational support. This chapter explores how AI-based technologies are reshaping accident and emergency care into a smart, data-driven, and patient-centered ecosystem. It begins by tracing the evolution of emergency care frameworks from reactive systems to adaptive, AI-enhanced models supported by digital health infrastructure. AI-enabled triage systems are highlighted as a major advancement, using machine learning algorithms to analyze large volumes of clinical data and prioritize patients based on predicted risk rather than static rules. Such systems facilitate early identification of critically ill patients, reduce waiting times, and optimize resource allocation. The chapter further examines AI-driven clinical decision support and predictive modeling, demonstrating their role in rapid interpretation of electrocardiograms, imaging studies, and laboratory data to accelerate diagnosis of time-sensitive conditions such as sepsis, stroke, trauma, and myocardial infarction. Continuous AI-based monitoring systems detect early physiological deterioration while minimizing alarm fatigue. Integration of AI into point-of-care diagnostics, wearable sensors, and smart ambulances extends emergency care beyond hospital settings. Finally, challenges related to data quality, bias, ethics, cybersecurity, and regulation are discussed, emphasizing human–AI collaboration as the foundation for safe, equitable, and effective smart emergency care systems.

*Corresponding author; e-mail: premajeny@gmail.com

Cite this Chapter: Prema Rathinam, Senthilkumar Chelladurai, Sebastin Varghese, Mageshwari Rajendran, Chandrasekaran Padmanaban and Sabitha Rajamanickam. 2025. AI-Enabled Accident and Emergency Care: Smart Triage, Monitoring, and Critical Decision-Making. In: R. Balachandar and K. Ashok Kumar (Eds.), Development of Biogenic Nanomaterials and their Benign Applications. Excellent Publishers, India. pp. 219-228. doi: https://doi.org/10.20546/978-93-94174-20-7_19