Tıbbi Mikrobiyoloji ve Yapay Zekâ

Franco-Duarte R, Černáková L, Kadam S, Kaushik KS, Salehi B, Bevilacqua A, et al. Advances in Chemical and Biological Methods to Identify Microorganisms-From Past to Present. Microorganisms. 2019;7(5).

Kaul V, Enslin S, Gross SA. History of artificial intelligence in medicine. Gastrointest Endosc. 2020;92(4):807-12.

Humphries RM, Bragin E, Parkhill J, Morales G, Schmitz JE, Rhodes PA. Machine-Learning Model for Prediction of Cefepime Susceptibility in Escherichia coli from Whole-Genome Sequencing Data. J Clin Microbiol. 2023;61(3):e0143122.

Pearcy N, Hu Y, Baker M, Maciel-Guerra A, Xue N, Wang W, et al. Genome-Scale Metabolic Models and Machine Learning Reveal Genetic Determinants of Antibiotic Resistance in Escherichia coli and Unravel the Underlying Metabolic Adaptation Mechanisms. mSystems. 2021;6(4):e0091320.

Baddal B, Taner F, Uzun Ozsahin D. Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review. Diagnostics (Basel). 2024;14(5).

Xu C, Zhao LY, Ye CS, Xu KC, Xu KY. The application of machine learning in clinical microbiology and infectious diseases. Front Cell Infect Microbiol. 2025;15:1545646.

Alsulimani A, Akhter N, Jameela F, Ashgar RI, Jawed A, Hassani MA, et al. The Impact of Artificial Intelligence on Microbial Diagnosis. Microorganisms. 2024;12(6).

Viboud G, Asaro H, Huang MB. Use of matrix-assisted laser desorption ionization time of flight (MALDI-TOF) to detect antibiotic resistance in bacteria: A scoping review. Am J Clin Pathol. 2024;161(4):317-28.

Khalaf WS, Morgan RN, Elkhatib WF. Clinical microbiology and artificial intelligence: Different applications, challenges, and future prospects. J Microbiol Methods. 2025;232-234:107125.

Graf E, Soliman A, Marouf M, Parwani AV, Pancholi P. Potential roles for artificial intelligence in clinical microbiology from improved diagnostic accuracy to solving the staffing crisis. Am J Clin Pathol. 2025;163(2):162-8.

Adhikary K, Ganguly K, Maity M. General Microbiology and Parasitology, Virology and Mycology With Lab Manual2025.

Fang W, Wu J, Cheng M, Zhu X, Du M, Chen C, et al. Diagnosis of invasive fungal infections: challenges and recent developments. Journal of Biomedical Science. 2023;30(1):42.

Maturana CR, de Oliveira AD, Nadal S, Bilalli B, Serrat FZ, Soley ME, et al. Advances and challenges in automated malaria diagnosis using digital microscopy imaging with artificial intelligence tools: A review. Front Microbiol. 2022;13:1006659.

Burns BL, Rhoads DD, Misra A. The Use of Machine Learning for Image Analysis Artificial Intelligence in Clinical Microbiology. J Clin Microbiol. 2023;61(9):e0233621.

Durant TJS, Dudgeon SN, McPadden J, Simpson A, Price N, Schulz WL, et al. Applications of Digital Microscopy and Densely Connected Convolutional Neural Networks for Automated Quantification of Babesia-Infected Erythrocytes. Clin Chem. 2021;68(1):218-29.

Ma L, Yi J, Wisuthiphaet N, Earles M, Nitin N. Accelerating the Detection of Bacteria in Food Using Artificial Intelligence and Optical Imaging. Applied and Environmental Microbiology. 2022;89(1):e01828-22.

Maeda Y, Sugiyama Y, Kogiso A, Lim TK, Harada M, Yoshino T, et al. Colony Fingerprint-Based Discrimination of Staphylococcus species with Machine Learning Approaches. Sensors (Basel). 2018;18(9).

Memon S, Bibi S, He G. Integration of AI and ML in Tuberculosis (TB) Management: From Diagnosis to Drug Discovery. Diseases. 2025;13(6).

Burton RJ, Albur M, Eberl M, Cuff SM. Using artificial intelligence to reduce diagnostic workload without compromising detection of urinary tract infections. BMC Med Inform Decis Mak. 2019;19(1):171.

Mairi A, Hamza L, Touati A. Artificial intelligence and its application in clinical microbiology. Expert Rev Anti Infect Ther. 2025;23(7):469-90.

Belay WY, Getachew M, Tegegne BA, Teffera ZH, Dagne A, Zeleke TK, et al. Antimicrobial resistance with a focus on antibacterial, antifungal, antimalarial, and antiviral drugs resistance, its threat, global priority pathogens, prevention, and control strategies: a review. Ther Adv Infect Dis. 2025;12:20499361251340144.

Sati H, Carrara E, Savoldi A, Hansen P, Garlasco J, Campagnaro E, et al. The WHO Bacterial Priority Pathogens List 2024: a prioritisation study to guide research, development, and public health strategies against antimicrobial resistance. The Lancet Infectious Diseases. 2025;25(9):1033-43.

Salam MA, Al-Amin MY, Pawar JS, Akhter N, Lucy IB. Conventional methods and future trends in antimicrobial susceptibility testing. Saudi J Biol Sci. 2023;30(3):103582.

Stokes JM, Yang K, Swanson K, Jin W, Cubillos-Ruiz A, Donghia NM, et al. A Deep Learning Approach to Antibiotic Discovery. Cell. 2020;180(4):688-702.e13.

Peiffer-Smadja N, Rawson TM, Ahmad R, Buchard A, Georgiou P, Lescure FX, et al. Machine learning for clinical decision support in infectious diseases: a narrative review of current applications. Clin Microbiol Infect. 2020;26(5):584-95.

Rawson TM, Moore LSP, Hernandez B, Charani E, Castro-Sanchez E, Herrero P, et al. A systematic review of clinical decision support systems for antimicrobial management: are we failing to investigate these interventions appropriately? Clin Microbiol Infect. 2017;23(8):524-32.

Bignami EG, Berdini M, Panizzi M, Domenichetti T, Bezzi F, Allai S, et al. Artificial Intelligence in Sepsis Management: An Overview for Clinicians. J Clin Med. 2025;14(1).

Shimabukuro DW, Barton CW, Feldman MD, Mataraso SJ, Das R. Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial. BMJ Open Respir Res. 2017;4(1):e000234.

Finkelstein J, Gabriel A, Schmer S, Truong TT, Dunn A. Identifying Facilitators and Barriers to Implementation of AI-Assisted Clinical Decision Support in an Electronic Health Record System. J Med Syst. 2024;48(1):89.

Magrabi F, Ammenwerth E, McNair JB, De Keizer NF, Hyppönen H, Nykänen P, et al. Artificial Intelligence in Clinical Decision Support: Challenges for Evaluating AI and Practical Implications. Yearb Med Inform. 2019;28(1):128-34.

Kim SY, Kim DH, Kim MJ, Ko HJ, Jeong OR. XAI-Based Clinical Decision Support Systems: A Systematic Review. Applied Sciences. 2024;14(15):6638.

Robertson AJ, Mallett AJ, Stark Z, Sullivan C. It Is in Our DNA: Bringing Electronic Health Records and Genomic Data Together for Precision Medicine. JMIR Bioinform Biotech. 2024;5:e55632.

Bajwa J, Munir U, Nori A, Williams B. Artificial intelligence in healthcare: transforming the practice of medicine. Future Healthc J. 2021;8(2):e188-e94.

Cheah BCJ, Vicente CR, Chan KR. Machine Learning and Artificial Intelligence for Infectious Disease Surveillance, Diagnosis, and Prognosis. Viruses. 2025;17(7).

Hu WH, Sun HM, Wei YY, Hao YT. Global infectious disease early warning models: An updated review and lessons from the COVID-19 pandemic. Infect Dis Model. 2025;10(2):410-22.

Arnold A, McLellan S, Stokes JM. How AI can help us beat AMR. NPJ Antimicrob Resist. 2025;3(1):18.

El Arab RA, Almoosa Z, Alkhunaizi M, Abuadas FH, Somerville J. Artificial intelligence in hospital infection prevention: an integrative review. Front Public Health. 2025;13:1547450.

Marra AR, Langford BJ, Nori P, Bearman G. Revolutionizing antimicrobial stewardship, infection prevention, and public health with artificial intelligence: the middle path. Antimicrob Steward Healthc Epidemiol. 2023;3(1):e219.

Abdulmalek S, Nasir A, Jabbar WA, Almuhaya MAM, Bairagi AK, Khan MA, et al. IoT-Based Healthcare-Monitoring System towards Improving Quality of Life: A Review. Healthcare (Basel). 2022;10(10).