The Role of Artificial Intelligence and Machine Learning in Modern Medicine: A Literature Review - Wiśniewski, Karol Paweł; Witkowski, Paweł; Bachurski, Patryk; Chmiel, Gabriela; Pierzga, Elisabetta; Międlar, Maja; Muda, Martyna; Pacek, Szymon; Zarański, Bartosz; Kalinowski, Paweł (Quality in Sport, 2026)
Artificial Intelligence (AI) and Machine Learning (ML) are rapidly transforming modern medicine by helping in processing great volumes of clinical data with exceptional speed and accuracy. As medical knowledge expands and guidelines change, AI tools support the management of information overload and improve clinical workflows. The aim of this article is to review concrete examples of AI and ML applications across various medical specialties, focusing of their ability to accelerate processes and enhance diagnostic accuracy. In radiology, AI models demonstrate better performance in chest imaging and comparable accuracy in mammography compared to doctors, while reducing the impact of human factors such as fatigue. In cancer care, AI allows for multi-omics integration, precise pathological evaluation (e.g. GastroMIL model) and prognostic forecasting. Dermatological studies reveal that AI algorithms can outperform dermatologists in classifying skin leisons (72,1% vs 65,78% accuracy). In cardiology, AI enhances risk stratification beyond traditional scales and demonstrates higher sensitivity in ECG interpretation compared to healthcare professionals. Through real-time monitoring of hemodynamic stability and postoperative pain management, anesthesiology has integrated AI into clinical practice to improve accuracy of detection of hypotension by 40%. Preoperatively, AI provides assistance to assess risk and offers assistance to the perioperative team during the surgical procedure. AI also improves medical record documentation and decreases the administrative burden of documentation on the physician. AI systems currently augment our clinical intelligence by overcoming limitations in human cognition such as fatigue and algorithmically processing large volume datasets on a daily basis to improve diagnostic accuracy, treatment personalization and efficiency of healthcare.