Artificial intelligence for early diagnosis in emergency department.
Authors
Affiliations (11)
Affiliations (11)
- Department of Life Sciences, Health and Health Professions, Link Campus University, Rome, 00165, Italy.
- Department of Systems Medicine, Geriatric Medicine Residency Program, University of Rome 'Tor Vergata', Rome, Italy.
- Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Emergency Medicine, Humanitas University-Research Hospital, Rozzano, Milano, Italy.
- Department of Biomedical Sciences, Emergency Medicine Residency Program, Humanitas University, Pieve Emanuele, Milan, Italy.
- Division of Anesthesia and Critical Care Medicine, Department of Emergency Medicine, San Giovanni Bosco, ASL, City of Turin, Italy. [email protected].
- Division of Anesthesia and Critical Care Medicine, Department of Emergency Medicine, San Giovanni Bosco, ASL, City of Turin, Italy.
- Department of Anesthesiology and Critical Care, Città Della Salute E Della Scienza of Turin, Turin, Italy.
- Department of Anatomical, Histological, Forensic and Orthopaedical Sciences, Sapienza University of Rome, Rome, 00185, Italy.
- SIC Medicina Legale Basilicata, Via Potito Petrone, Potenza, 85100, Italy.
- Department of Clinical Medicine, Public Health, Life and Environment Science, University of L'Aquila, L'Aquila, 67100, Italy.
Abstract
In recent years, artificial intelligence (AI) has become an increasingly prominent player in emergency medicine, offering innovative tools to enhance the early diagnosis of acute conditions. This systematic review explores how AI, particularly through machine learning (ML) and deep learning (DL), is transforming the way physicians and healthcare professionals respond to high-stakes clinical scenarios. The evidence gathered shows that smart algorithms are capable of detecting complex patterns in clinical, diagnostic, and laboratory data, patterns that may even elude expert clinicians, especially under the high-pressure environment of the emergency room. From acute coronary syndrome to stroke, from sepsis to respiratory failure, AI has demonstrated impressive predictive power and provides real, practical support in risk stratification, triage optimization, and faster diagnosis. Equally important is its role in automated medical image analysis, which enables quicker and more accurate diagnostic decisions, offering real-time support for clinicians. However, the widespread adoption of these technologies also brings significant challenges: the need for algorithmic transparency, the necessity of earning the trust of healthcare providers, and the sensitive ethical issues related to patient data privacy. To overcome these barriers, it is essential to involve healthcare professionals in the development and implementation of AI technologies-ensuring their clinical expertise complements the analytical power of these new tools. Targeted training programs and large-scale validation studies are critical steps for ensuring the safe and effective use of AI. Ultimately, this review confirms that AI holds great promise as a catalyst for a more efficient, timely, and patient-centered approach to emergency medicine.