Integrated AI Medical Emergency Diagnostics Advising System

The application of AI (Artificial Intelligence) in emergency medicine helps significantly improve the quality of diagnostics under limitations of resources and time constraints in emergency cases. We have designed a comprehensive AI-based diagnostic and treatment plan decision-support system for emergency medicine by integrating the available LLMs (Large Language Models), like ChatGPT, Gemini, Claude, and others, and tuning them up with additional training on actual emergency cases. There is a special focus on early detection of life-threatening and time-sensitive diseases like sepsis, stroke, and heart attack, which are the major causes of death in emergency medicine. Additional training was conducted on a total of 600 cases (300 sepsis; 300 non-sepsis). The collective capability of the integrated LLMs is much stronger than each individual engine. Emergency cases can be predicted based on information from multiple sensors and streaming sources combining traditional IT (Information Technology) infrastructure with Internet of Things (IoT) schemes. Medical personnel compare and validate the AI models used in this work.

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Publication Details

Journal
Electronics
Published
2024-11-08
DOI
https://doi.org/10.3390/electronics13224389
Citations
6
Primary Topic
Artificial Intelligence in Healthcare
Type
article
Field-Weighted Citation Impact
4.25
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article

Integrated AI Medical Emergency Diagnostics Advising System

Mario Giosuè Balzanelli, Luigì Santacroce, Abdolreza Mosaddegh, Rolando Herrero et al.
6 citations
Electronics
Artificial Intelligence in Healthcare
4.25
article

Integrated AI Medical Emergency Diagnostics Advising System

Mario Giosuè Balzanelli, Luigì Santacroce, Abdolreza Mosaddegh, Rolando Herrero, Angelo Cefalo, Jacopo M. Legramante, Kieu C. D. Nguyen, Rita Lazzaro, Marilena Minieri, Francesco Inchingolo, Sergey K. Aityan, Ciro Gargiulo Isacco, Lucia Carriero, Manuel Mersini, Nicola Iacovazzo
article en
6 citations

Abstract

The application of AI (Artificial Intelligence) in emergency medicine helps significantly improve the quality of diagnostics under limitations of resources and time constraints in emergency cases. We have designed a comprehensive AI-based diagnostic and treatment plan decision-support system for emergency medicine by integrating the available LLMs (Large Language Models), like ChatGPT, Gemini, Claude, and others, and tuning them up with additional training on actual emergency cases. There is a special focus on early detection of life-threatening and time-sensitive diseases like sepsis, stroke, and heart attack, which are the major causes of death in emergency medicine. Additional training was conducted on a total of 600 cases (300 sepsis; 300 non-sepsis). The collective capability of the integrated LLMs is much stronger than each individual engine. Emergency cases can be predicted based on information from multiple sensors and streaming sources combining traditional IT (Information Technology) infrastructure with Internet of Things (IoT) schemes. Medical personnel compare and validate the AI models used in this work.

ElectronicsVol. 13(22)
University of Rome Tor Vergata (IT), Northeastern University (US), Oakland University (US), CTO Andrea Alesini (IT), North Atlantic Treaty Organization (BE), University of Bari Aldo Moro (IT), University of Naples Federico II (IT)
Industry, innovation and infrastructure
Openalex Percentile: Top 5%
Artificial Intelligence in Healthcare
4.25
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