THE ROLE AND PROSPECTS OF ARTIFICIAL INTELLIGENCE IN MODERN MEDICINE

Аннотация

Artificial Intelligence (AI) has become a transformative force in modern medicine, offering new solutions for diagnosis, treatment planning, patient monitoring, and healthcare system optimization. This article explores the current applications of AI in clinical practice, its technological foundations, and the ethical, legal, and infrastructural challenges associated with its implementation. Drawing on real-world examples from radiology, pathology, surgery, and personalized medicine, the study evaluates AI's potential to enhance accuracy, reduce workload, and improve outcomes. Future prospects include AI-integrated decision support systems, robotic- assisted interventions, and predictive analytics based on big data and genomics. As AI continues to evolve, it is reshaping the role of the physician and necessitating a new paradigm of human-machine collaboration in healthcare delivery. Kеywоrds: Artificial intelligence; machine learning; clinical decision-making; predictive analytics; digital health; medical imaging; personalized medicine. INTRОDUСTIОN The integration of artificial intelligence into the field of medicine represents one of the most profound technological shifts in the 21st century. Driven by the rapid growth of computational power, data availability, and algorithmic sophistication, AI is increasingly deployed to solve complex clinical problems that were previously limited by human cognition and time constraints. Medical institutions and tech companies alike are investing in AI-based tools aimed at improving diagnostic precision, operational efficiency, and patient-centered care. From chatbots and symptom checkers to surgical robots and AI-enhanced imaging, the spectrum of applications is rapidly expanding. The adoption of AI is not just a matter of technical innovation—it also entails rethinking the ethical, legal, and educational frameworks within which medicine operates. MАTЕRIАLS АND MЕTHОDS One of the most mature applications of AI is in medical imaging. Deep learning algorithms, particularly convolutional neural networks (CNNs), have demonstrated performance on par with or even superior to radiologists in detecting conditions such as lung cancer on CT scans, diabetic retinopathy on fundus photographs, and breast cancer in mammograms. AI systems like Google’s DeepMind and IBM’s Watson https://scientific-jl.com/ped 2 0 Volume-87, Issue-1, August -2025 “PEDAGOGS” international research journal ISSN: 2181-3027_SJIF: 5.449 Health have been trained on millions of annotated images to identify subtle patterns beyond human detection. These tools not only improve accuracy but also help reduce the diagnostic workload in overburdened health systems. AI-powered CDSS assist physicians by analyzing large datasets and suggesting treatment options based on real-time evidence. Such systems can flag potential drug interactions, identify contraindications, and even recommend tailored therapies based on patient profiles. By providing risk scores and predictive analytics, these systems help clinicians prioritize patients who require urgent intervention and avoid preventable complications. For instance, the use of AI to predict sepsis 12–24 hours in advance based on EHR data has been implemented in several hospital networks, demonstrating life-saving potential. RЕSULTS АND DISСUSSIОN Surgical robotics, enhanced by AI algorithms, have revolutionized minimally invasive procedures. Systems such as the Da Vinci robot provide surgeons with real- time feedback, improved dexterity, and motion scaling, reducing hand tremors. Moreover, AI is being used to model patient-specific anatomy and simulate surgical outcomes, improving preoperative planning and intraoperative navigation. Recent innovations in autonomous robotic suturing and soft-tissue manipulation suggest that AI will soon play a more direct role in surgery itself. AI plays a crucial role in decoding the vast information stored in the human genome. Machine learning models are used to predict disease risk, identify therapeutic targets, and match patients with the most effective treatment regimens. In oncology, AI systems help identify gene mutations driving tumor growth and recommend precision therapies. For example, IBM Watson for Genomics can interpret genetic data and cross-reference it with clinical studies to suggest individualized cancer treatments. Despite its potential, the deployment of AI in healthcare raises important concerns. Algorithmic bias remains a critical issue, especially when training data lacks diversity, leading to disparities in diagnostic accuracy across different demographic groups. There are also challenges related to data privacy, informed consent, and cybersecurity, particularly as AI systems require access to massive quantities of sensitive health data. The question of liability—who is responsible if an AI system makes an incorrect recommendation—remains legally unresolved. Moreover, there is growing concern that the over-reliance on