Artificial intelligence is transforming the way we conduct diagnostics, offering unprecedented improvements in efficiency and accuracy. Its ability to process large datasets and identify complex patterns in medical images facilitates earlier diagnoses and more targeted treatments. This is particularly critical in areas like cancer, where operator-dependent bias could interfere with diagnosis and patient survival. However, this technological advancement demands rigorous ethical analysis, especially at critical post-analytical points, such as the opacity in result interpretation. In this context, the explainability of AI is central to unraveling the specific mechanisms behind its decision-making processes, promoting responsible adoption, while mitigating implicit biases that could exacerbate inequities in clinical settings. Furthermore, explainability helps dismantle the imprecise conceptualization of AI as a “robot with general intelligence and subjectivity”; instead, it frames it as a system of sophisticated mathematical models designed to solve complex problems, categorized under strategies known as “machine learning.” Clinicians must understand the evolution of technological tools and integrate them into their practice, recognizing both the limitations and capabilities of AI, as well as their role and responsibility in its application. Finally, updating competencies is crucial for healthcare professionals who need to incorporate knowledge of AI, presenting a challenge for universities to ensure ethical and effective clinical practice.