Scholarlink

<p>One of the major innovations introduced in Health Systems in the last years has been the revolution of Artificial Intelligence (AI). There is no field in Medicine which has not been reshaped by its integration. Notably, respiratory diseases have been the forefront for AI application. World Health Organization (WHO) highlights the continuous spreading of Antimicrobial resistance (AMR), combatable only by improving diagnostics and personalized treatments.</p><p>High costs and the need of infrastructures often not disposable in peripheral centres are hampering such fight. Especially in the field of pneumonia caused by MRSA, prompt therapeutic action is hindered by the need to await bacterial cultures. The systematic review we aim to write will synthesize the existing study pool that evaluate the potential use of AI tools for risk prediction and therapy optimization of MRSA pneumonia. Explored AI tools are supervised ML models for pneumonia discrimination and optimal therapeutic dosing determination. Additionally, ML-CDSS with potential for integration into clinical practice will be explored. As this is a systematic review protocol, no results are reported: consequently, outcomes represent planned analyses rather than findings. This protocol has been uploaded to the International Prospective Register of Systematic Reviews (PROSPERO) and is written according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols (PRISMA-P) guidelines. Inclusion and exclusion criteria were established following the PICOS method. With this systematic review we aim to differentiate MRSA pneumonia from a different etiology by leveraging AI tools, minimizing empirical antibiotic use and help combat AMR.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Artificial Intelligence Application for MRSA Pneumonia: a Protocol of a Systematic Review

  • 期刊论文

  • 发表日期：

    2026年3月18日

查看全文
  • Marco Petronio,
  • Roberta Di Matteo,
  • Serena Penpa,
  • Annalisa Roveta,
  • Andrea Santomauro,
  • Menada Gardalini,
  • Marianna Farotto,
  • Iacopo Megna,
  • Cesare Bolla,
  • Antonio Maconi

摘要

One of the major innovations introduced in Health Systems in the last years has been the revolution of Artificial Intelligence (AI). There is no field in Medicine which has not been reshaped by its integration. Notably, respiratory diseases have been the forefront for AI application. World Health Organization (WHO) highlights the continuous spreading of Antimicrobial resistance (AMR), combatable only by improving diagnostics and personalized treatments.

High costs and the need of infrastructures often not disposable in peripheral centres are hampering such fight. Especially in the field of pneumonia caused by MRSA, prompt therapeutic action is hindered by the need to await bacterial cultures. The systematic review we aim to write will synthesize the existing study pool that evaluate the potential use of AI tools for risk prediction and therapy optimization of MRSA pneumonia. Explored AI tools are supervised ML models for pneumonia discrimination and optimal therapeutic dosing determination. Additionally, ML-CDSS with potential for integration into clinical practice will be explored. As this is a systematic review protocol, no results are reported: consequently, outcomes represent planned analyses rather than findings. This protocol has been uploaded to the International Prospective Register of Systematic Reviews (PROSPERO) and is written according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols (PRISMA-P) guidelines. Inclusion and exclusion criteria were established following the PICOS method. With this systematic review we aim to differentiate MRSA pneumonia from a different etiology by leveraging AI tools, minimizing empirical antibiotic use and help combat AMR.