近年来，由于技术创新，医疗保健正在发生重大变化，其中人工智能（AI）是一个关键趋势。研究表明，特别是在放射诊断领域，人工智能有潜力提高准确性和效率。我们重点关注人工智能在诊断肺部病变方面的作用，根据胸部 X 光检查，这可能表明肺癌。尽管与胸部 CT 等其他方法相比，X 射线的灵敏度较低，但由于其常规使用，X 射线通常可以首先检测到肺部病变。我们提出了基于深度学习的解决方案，旨在改善肺部病变检测，特别是在疾病的早期阶段。然后，我们分享之前在两种不同临床环境中验证该模型的研究结果：患病率较低的综合医院和专门的肿瘤中心。基于与不同经验水平的放射科医生的结论的定量比较，我们的模型实现了高灵敏度，但比比较放射科医生的特异性较低。在临床需求和人工智能辅助诊断的背景下，医生的经验和临床推理起着至关重要的作用，因此我们目前更倾向于敏感性高于特异性的模型。即使不太可能的怀疑也会向医生提出。基于这些结果，可以预见，未来人工智能将作为评估专家的支持工具在放射学领域发挥关键作用。要实现这一目标，不仅需要解决技术方面的问题，还需要解决医疗和监管方面的问题。获得高质量和可靠的信息至关重要，不仅要了解机器学习和人工智能在医学上的好处，还要了解其局限性。

In recent years healthcare is undergoing significant changes due to technological innovations, with Artificial Intelligence (AI) being a key trend. Particularly in radiodiagnostics, according to studies, AI has the potential to enhance accuracy and efficiency. We focus on AI's role in diagnosing pulmonary lesions, which could indicate lung cancer, based on chest X-rays. Despite lower sensitivity in comparison to other methods like chest CT, due to its routine use, X-rays often provide the first detection of lung lesions. We present our deep learning-based solution aimed at improving lung lesion detection, especially during early-stage of illness. We then share results from our previous studies validating this model in two different clinical settings: a general hospital with low prevalence findings and a specialized oncology center. Based on a quantitative comparison with the conclusions of radiologists of different levels of experience, our model achieves high sensitivity, but lower specificity than comparing radiologists. In the context of clinical requirements and AI-assisted diagnostics, the experience and clinical reasoning of the doctor play a crucial role, therefore we currently lean more towards models with higher sensitivity over specificity. Even unlikely suspicions are presented to the doctor. Based on these results, it can be expected that in the future artificial intelligence will play a key role in the field of radiology as a supporting tool for evaluating specialists. To achieve this, it is necessary to solve not only technical but also medical and regulatory aspects. It is crucial to have access to quality and reliable information not only about the benefits but also about the limitations of machine learning and AI in medicine.