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Yapay zekanın tıp alanındaki ana ilgi alanı, teşhis ve tedavi önerileri sunabilecek yöntemler geliştirmek gibi görünse de hekim ve hemşire klinik karar destek sistemleri, eczane karar destek sistemleri, hasta bakımı, klinik veri havuzu oluşturulması, birimler ve kurumlar arası veri paylaşımı, depolama, yorumlayabilmeye sürecine katkı ile beraber olarak iş zekası ve makine öğrenmesi gibi sayısız alanı kapsar. Tıbbi laboratuvarlar otomasyon, uzman sistemler ve yapay zekaya doğru güçlü bir yönelimle karşı karşıya olmanın yanısıra uzman sistemlere yönelik artan bir ihtiyaç yaşamaktadır. Klinik mikrobiyoloji laboratuvarları antimikrobiyal dirence karşı mücadelede yer alabilecek veri zincirlerinin tespitinde merkezi bir unsurdur. Yapay zekanın klinik mikrobiyoloji laboratuvar kullanımına entegrasyonun amaçları arasında bireysel epidemiyolojik sürveyans, araştırma uygulamalarına ayrıntılı destek sağlamanın yanı sıra bireysel hasta bakım kalitesini artırmak yer alır. Çalışmamızda klinik mikrobiyoloji ve antibiyotik direncinin işlenmesi konusunda farklı yapay zeka çalışma prensip ve yöntemleri gözden geçirilerek, bu yöntemleri irdeleyen önemli klinik çalışmalar incelenmiştir.
The main area of interest of artificial intelligence in the field of medicine appears to be developing methods that can offer diagnosis and treatment recommendations, but doctors and nurses cover countless fields such as clinical decision support systems, pharmaceutical decision support systems, patient care, clinical data pool creation, the contribution to the process of units and institutions data sharing, storage, interpretation, as well as business intelligence and machine learning. Medical laboratories are facing a strong direction towards automation, specialized systems and artificial intelligence, as well as a growing need for specialized systems. Clinical microbiology laboratories are a central element in the detection of data chains that may be involved in the fight against antimicrobial resistance. The objectives of integration of artificial intelligence into the clinical microbiology laboratory use include individual epidemiological monitoring, providing detailed support for research practices as well as improving the quality of individual patient care. In our study, the various principles and methods of study of artificial intelligence in the processing of clinical microbiology and antibiotic resistance have been reviewed, and important clinical studies on these methods have been studied.
Although the main interest of artificial intelligence in medicine seems to be to develop methods that can offer diagnostic and therapeutic recommendations, it includes numerous areas such as physician and nurse clinical decision support systems, pharmacy decision support systems, patient care, clinical data pooling, data sharing between units and institutions, storage, interp- retation, business intelligence and machine learning. In addition to having a strong orientation towards automation, expert systems and artificial intelligence, medical laboratories have an increasing need especially for expert systems. Clinical microbiology laboratories are a central element in the identification of data chains that may be involved in the fight against antimicrobial resistance. By the integration of artificial intelligence to clinical microbiology laboratory use, it is aimed to provide detailed support to individual epidemiological surveillance, research appli- cations and to improve individual patient care quality. In our study, the principles and methods of the study of artificial intelligence in clinical microbiology and antibiotic resistance processing were reviewed and important clinical studies were examined.
Alan : Sağlık Bilimleri
Dergi Türü : Uluslararası
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