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  Deep learning in Emergency Medicine: Recent contributions and methodological challenges

  Francesco Faita

  17-03-2020

  https://doi.org/10.4081/ecj.2020.8573

  1663

  Downloads: 717

• 14 | Application of an artificial intelligence algorithm to patients presenting to the emergency department of the Pisa University Hospital for chest pain: retrospective assessment of diagnostic accuracy

  Greta Barbieri, Alessandra Bionda, Valeria Natalini, Bianca Alderotti, Luca Pellungrini, Ada Colella, Lorenzo Ghiadoni

  15-07-2026

  https://doi.org/10.4081/ecj.2026.15954

  30

• The importance of discrete event simulation as a methodology for performance evaluation in the emergency department

  Yannik Angler, Astrid Lossin, Olav Goetz

  09-08-2024

  https://doi.org/10.4081/ecj.2024.12562

  4407

  Downloads: 858

• The productivity trap: will AI restore presence or accelerate throughput?

  Manuel Monti

  10-07-2026

  https://doi.org/10.4081/ecj.2026.15615

  138

  Downloads: 37

• Response to: Potential and limitations of large language models in acute chest pain triage

  Arian Zaboli, Gianni Turcato

  10-07-2026

  https://doi.org/10.4081/ecj.2026.15676

  57

  Downloads: 21

• Potential and limitations of large language models in acute chest pain triage. Response to Evaluating the predictive accuracy of ChatGPT in risk stratification for chest pain in the emergency department

  Varsha Shinde, Pratik Kanani, Keyur Bhimani

  15-05-2026

  https://doi.org/10.4081/ecj.2026.15130

  244

  Downloads: 88

• Evaluating the predictive accuracy of ChatGPT in risk stratification for chest pain in the emergency department

  Fabio Malalan, Arian Zaboli, Armando Fiore, Nicola Fanton, Laura Sponga, Barbara Zaia, Giovanni Mantelli, Caterina Barberi, Paola Pisani, Laura Giorio, Gianni Turcato

  06-06-2025

  https://doi.org/10.4081/ecj.2025.13829

  898

  Downloads: 914