Year: 2026
Type: review
Abstract: Artificial intelligence (AI) is increasingly influencing healthcare, with growing relevance in infectious diseases and clinical microbiology. This review summarizes current applications of machine learning (ML), deep learning (DL), and large language models (LLMs) in these areas while critically evaluating their strengths and limitations. Although AI has shown promising performance in image analysis, molecular diagnostics, risk prediction, and clinical decision support, its routine implementation remains constrained by limited external validation, heterogeneous datasets, algorithmic bias, and challenges in generalizability and explainability. The review also discusses AI's emerging role in scientific writing and the ethical and practical considerations associated with its clinical adoption. Overall, current evidence suggests that AI should be regarded as a complementary decision-support technology whose safe and effective integration depends on rigorous validation, high-quality data, and appropriate clinical oversight.
Authors: Elif Agüloğlu Bali, Yasemin Çakır-Kıymaz, İrem Akdemir, Eda Alp, Feyza Nur Akçin Balan, Merve Büyükkörük, Ravi Kumar Chaudhary, Güle Çınar, Daniele Roberto Giacobbe, Ezgi Gülten, Taliha Karakök, Murat Hakan Kır, Oliver Nolte, Ahmet Kağan Özdemir, Özlem Güler, Eren Öztürk, İbrahim Yıldız, Gökmen Zararsız, Önder Ergönül
Institutions: Ministry of Health, Sivas Cumhuriyet Üniversitesi, Ankara University, İstanbul Başakşehir Çam ve Sakura Şehir Hastanesi, Government Institute of Medical Sciences, Greater Noida, Ospedale Policlinico San Martino, University of Genoa, Istanbul University-Cerrahpaşa, Istanbul University, University of Zurich, Ankara Yıldırım Beyazıt University, Kocaeli Üniversitesi, Adnan Menderes University, Erciyes University, Koç University
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W7216567968