Computers in Biology and Medicine, Год журнала: 2025, Номер 191, С. 110178 - 110178
Опубликована: Апрель 14, 2025
Язык: Английский
Computers in Biology and Medicine, Год журнала: 2025, Номер 191, С. 110178 - 110178
Опубликована: Апрель 14, 2025
Язык: Английский
Computers in Biology and Medicine, Год журнала: 2025, Номер 189, С. 109984 - 109984
Опубликована: Март 14, 2025
Язык: Английский
Процитировано
0Expert Review of Anti-infective Therapy, Год журнала: 2025, Номер unknown
Опубликована: Март 25, 2025
Traditional microbiological diagnostics face challenges in pathogen identification speed and antimicrobial resistance (AMR) evaluation. Artificial intelligence (AI) offers transformative solutions, necessitating a comprehensive review of its applications, advancements, integration clinical microbiology. This examines AI-driven methodologies, including machine learning (ML), deep (DL), convolutional neural networks (CNNs), for enhancing detection, AMR prediction, diagnostic imaging. Applications virology (e.g. COVID-19 RT-PCR optimization), parasitology malaria detection), bacteriology automated colony counting) are analyzed. A literature search was conducted using PubMed, Scopus, Web Science (2018-2024), prioritizing peer-reviewed studies on AI's accuracy, workflow efficiency, validation. AI significantly improves precision operational efficiency but requires robust validation to address data heterogeneity, model interpretability, ethical concerns. Future success hinges interdisciplinary collaboration develop standardized, equitable tools tailored global healthcare settings. Advancing explainable federated frameworks will be critical bridging current implementation gaps maximizing potential combating infectious diseases.
Язык: Английский
Процитировано
0Computers in Biology and Medicine, Год журнала: 2025, Номер 191, С. 110178 - 110178
Опубликована: Апрель 14, 2025
Язык: Английский
Процитировано
0