MEDICAL DIAGNOSTICS WITH THE HELP OF ARTIFICIAL INTELLIGENCE

Аннотация

This article reviews recent advances in medical diagnostics using artificial intelligence (AI) and their practical applications. The article provides detailed information on how AI is used in medicine, in particular in the diagnosis and treatment of diseases, its advantages and limitations. It discusses how AI technologies, including machine learning, deep learning, and data analysis, can improve the decision-making process of doctors. The article also provides examples of the effectiveness of diagnostic systems based on AI and their application in clinical practice. The ethical and legal aspects of the use of AI in medicine are also considered, and future development prospects are discussed. Keywords: artificial intelligence, medical diagnostics, disease detection, treatment, SI technologies, legal aspects. Introduction. Artificial intelligence (AI) is revolutionizing the medical field, particularly in the diagnosis and treatment of diseases. These technologies can help doctors make more accurate and effective decisions. One of the main advantages of AI is the ability to quickly analyze large amounts of data [1]. In medicine, this includes information from various sources, such as patient records, laboratory results, and imaging studies (e.g., X-rays, MRIs). In thediagnosis ofdiseases, AI algorithms, for example, use deep learning techniques to identify specific features from images. Such systems are used, for example, in the field of oncology to detect cancer cells. Studies show that AI systems can sometimes perform better than human doctors [2]. This helps to quickly assess patients' conditions and develop a treatment plan. In addition, AI allows for the development of individualized treatment approaches in medicine. For example, by analyzing genetic data, individual treatment strategies can be developed for a patient's diseases. This increases the effectiveness of treatment for patients and reduces side effects. However, the application of artificial intelligence in medicine also has a number of limitations. SI systems require large amounts of high-quality data. If the data is incorrect or incomplete, the accuracy and reliability of these systems can decrease. Also, the process of collecting and storing the data necessary fortheoperation of artificial intelligence isoften complex and time-consuming. Artificial intelligence-based systems must use personal patient data, which raises issues of confidentiality and data protection. These issues must be addressed in order to maintain trust between doctors and patients. Also, artificial intelligence systems cannot fully replace the experience of human doctors. Human factors, such as understanding the patient's condition and emotional approach, are very important in medicine. Artificial intelligence systems are limited to analyzing dataand donottake human emotions into account. The use of artificial intelligence in medicine is expected to expand in the future. As technology advances, AI systems can provide more accurate and reliable results. However, ethical and legal issues need to be considered in this process [3]. Medical professionals and researchers should be careful when implementing AIinpractice. www.wordlyknowledge.uz ILMIYMETODIK JURNAL JOURNAL OFIQRO –ЖУРНАЛ ИҚРО –IQRO JURNALI –volume 16,issue02,2025 ISSN:2181-4341, IMPACT FACTOR( RESEARCH BIB )–7,245,SJIF–5,431 Artificial intelligence (AI) technologies, including machine learning, deep learning, and data analysis, can radically improve the decision-making process of doctors in the medical field. These technologies help doctors make more accurate and effective decisions, while also helping them develop thebest treatment strategies for patients. Machine learning algorithms provide the ability to quickly and efficiently analyze large amounts of data. In medicine, doctors must review a large number of patient data, laboratory results, and imaging tests (e.g., X-rays, MRIs). Machine learning algorithms analyze this data and help identify important features in diagnosing diseases or assessing apatient’s condition. For example, using ML models to diagnose conditions such as heart disease or diabetes can help doctors make faster and more accurate diagnoses. Deep learning (DL) is a powerful technique for analyzing image data. DL algorithms are used in oncology, for example, to detect cancer cells. Studies have shown that deep learning models can sometimes outperform human doctors [4]. This can help doctors quickly assess patients’ conditions and develop treatment plans. Such technologies can help doctors detect diseases in theearly stages, which can improvetreatment effectiveness. Data analytics can also improve doctors’ decision-making. AI systems can collect and analyze large amounts of patient data, and provide doctors with accurate recommendations. For example, by analyzing a patient’s history and genetic information, AI systems can suggest individualized treatment strategies. This increases the effective