Artificial Intelligence in Infectious Diseases and Clinical Microbiology: Current and Future Directions İnfeksiyon Hastalıkları ve Klinik Mikrobiyolojide Yapay Zekâ: Güncel ve Gelecek Yönelimler
Klimik Dergisi, cilt.39, sa.1, 2026 (ESCI, Scopus, TRDizin)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 39 Sayı: 1
- Basım Tarihi: 2026
- Doi Numarası: 10.36519/kd.2026.5400
- Dergi Adı: Klimik Dergisi
- Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), Scopus, CAB Abstracts, CINAHL, EMBASE, TR DİZİN (ULAKBİM), Academic Search Ultimate (EBSCO), Biomedical Reference Collection: Corporate Edition (EBSCO), Health Research Premium Collection (ProQuest)
- Anahtar Kelimeler: antimicrobial stewardship, Artificial intelligence, infection control, infectious diseases
- Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
- Ondokuz Mayıs Üniversitesi Adresli: Evet
Özet
Artificial intelligence (AI) is increasingly applied in infectious diseases and clinical microbiology, encompassing diag-nostics, treatment, infection control, and antimicrobial stewardship, with transformative potential across many aspects of daily clinical practice. The integration of imaging modalities, molecular and microbiological tests, and host-response– based classifiers with AI algorithms enhances diagnostic accuracy and facilitates clinical decision-making. In the context of treatment, AI supports patient management by enabling personalized antibiotic selection, optimizing treatment duration, predicting resistance, and providing clinical decision support. For infection control, AI-driven applications such as early outbreak detection, real-time surveillance, hand hygiene monitoring, and environmental disinfection are becoming more prevalent. Despite these advancements, challenges persist, including data heterogeneity, limited algo-rithmic explainability, ethical and legal considerations, and concerns regarding patient privacy. With multidisciplinary collaboration, high-quality data generation, and robust regulatory frameworks, AI systems are anticipated to become reliable and effective decision-support tools in infectious diseases practice.