Please use this identifier to cite or link to this item: https://www.um.edu.mt/library/oar/handle/123456789/143630
Title: How ready are we to use artificial intelligence in our fight against antimicrobial resistance? An ESGAID and EAAS perspective
Authors: Giacobbe, Daniele Roberto
Ahmad, Rafi
Akilli, Fatih Mehmet
Ascandari, Abdulaziz
Eyre, David W.
Gallardo-Pizarro, Antonio
Garcia-Vidal, Carolina
Lopes, Bruno Silvester
Lyutsova, Ekaterina
Rakhimov, Ruslan
Rizzo, Alberto
Rohde, Holger
Sadeghi, Zahra
Schweitzer, Valentijn
Tartari Bonnici, Ermira
Torres-Sangiao, Eva
Guerrero-López, Alejandro
Keywords: Drug resistance in microorganism
Antimicrobial stewardship
Artificial intelligence -- Medical applications
Antibiotics -- Research
Medical informatics
Medical ethics
Machine learning
Natural language generation (Computer science)
Issue Date: 2026
Publisher: Taylor & Francis
Citation: Giacobbe, D. R., Ahmad, R., Akilli, F. M., Ascandari, A., Eyre, D. W., Gallardo-Pizarro, A., ....Guerrero-López, A. (2026). How ready are we to use artificial intelligence in our fight against Antimicrobial resistance? An ESGAID and EAAS perspective. Expert Review of Anti-infective Therapy, 1-23.
Abstract: Introduction: Antimicrobial resistance (AMR) remains one of the greatest threats to global health, requiring innovative approaches to antibiotic discovery, surveillance, diagnosis, and prescribing. In recent years, artificial intelligence (AI) has increasingly been applied across these domains, with the dual aim of accelerating research and strengthening antimicrobial stewardship. Areas covered: This perspective summarizes current advances and challenges in applying AI for tackling AMR. We examine the role of AI in antibiotic discovery, laboratory surveillance, diagnosis of resistant infections, and clinical decision support systems. Finally, we address the ethical and regulatory landscape, data transparency, and liability concerns. Expert opinion: AI offers unprecedented opportunities across the continuum of our efforts to counteract AMR, yet its adoption faces substantial hurdles. Some central challenges include the balance between model accuracy and explainability, the lack of widespread digital access, quality and transparency of training datasets, and usability for clinicians. Progress will depend on multidisciplinary collaboration, robust regulatory oversight, and the development of training programs equipping future healthcare professionals with AI-aware reasoning skills. Ultimately, AI should not replace but rather augment human reasoning in the fight against AMR, aligning innovation with ethical principles to ensure safer, more equitable AI-enhanced antibiotic prescribing and antimicrobial stewardship.
URI: https://www.um.edu.mt/library/oar/handle/123456789/143630
Appears in Collections:Scholarly Works - FacHScMid



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