4.2 Clinical Examples
Clinical examples of artificial intelligence use in diagnosis and health management demonstrate significant advantages as well as potential limitations of these technologies.
One notable case is the use of AI in oncology diagnosis, where automated systems analyze medical imaging such as MRI or CT scans with a high degree of accuracy. For instance, machine learning algorithms have successfully detected early signs of breast and lung cancer, substantially reducing diagnosis time and improving treatment effectiveness.
Additionally, AI application in analyzing blood test data enables identification of hidden predispositions to various diseases such as diabetes or cardiovascular conditions. In these cases, systems not only interpret results but also generate personalized recommendations for prevention and treatment based on large volumes of medical data and current trends.
Particularly important are cases of AI integration into chronic condition monitoring, for example, managing patients with heart failure. Here, systems continuously track vital signs and predict potential deteriorations, allowing physicians to take timely action. In some cases, AI has been used to assess therapy effectiveness and adjust treatment plans, significantly improving quality of medical care.
Despite successes, clinical examples also include instances where AI use led to errors or false positives. Analyzing these cases helps develop more reliable algorithms, manage risks, and enhance safety in applying new technologies. Overall, such practices confirm AI’s potential to improve diagnostic processes and personalize treatment, providing physicians with valuable tools to increase diagnostic accuracy and healthcare efficiency.