The transition from qualitative to quantitative analysis marks a pivotal shift in medical sciences, moving from subjective assessments to objective, data-driven approaches. Historically, fields like laboratory medicine and genetics relied on qualitative methods, which often resulted in variability and inconsistency. The introduction of quantitative techniques, such as spectrophotometry in laboratory medicine and PCR in genetics, revolutionized diagnostics and research, leading to standardized criteria and personalized medicine. Radiology has embraced, and pathology is also embracing this transition, with quantitative imaging biomarkers and digital pathology enhancing diagnostic accuracy and treatment planning. Artificial Intelligence (AI) is accelerating this shift, transforming medical practice by analyzing vast amounts of data with unprecedented speed and precision. In radiology, AI algorithms automate image interpretation, reducing variability and improving diagnostic accuracy. In pathology, AI enhances the quantification of histological features, providing reproducible and objective data. AI-driven predictive models forecast disease progression and patient outcomes, guiding personalized treatment strategies. However, the integration of AI introduces challenges, particularly the "black-box" phenomenon, where complex AI algorithms make accurate predictions without transparent decision-making processes. This lack of transparency can hinder validation, accountability, and trust. Additionally, the potential for bias in AI models necessitates rigorous training on large cohorts of patients, internal and external validation, and testing. A balanced regulatory framework is crucial to ensure the safe and ethical use of AI in healthcare. This includes ensuring transparency, rigorous validation, ethical standards, and continuous monitoring. By achieving this balance, the continued evolution of quantitative analysis and AI integration can enhance patient outcomes, ushering in a new era of precision medicine where data-driven insights inform every aspect of healthcare, from prevention to diagnosis and treatment. In conclusion, the shift to quantitative analysis and the integration of AI, or better, its "embedding" in clinical workflows, represents a fundamental breakthrough in medical sciences, driving innovation and improving the precision and reliability of medical practice.  Kulcsszavak: Imaging, Quantitative Imaging, Computed Tomography