Pulmonary embolism (PE) remains a diagnostic challenge in 2021. As the pathology is potentially fatal and signs and symptoms are nonspecific, further investigations are classically required. Based on the Bayesian approach, clinical probability became the keystone of the diagnostic strategy to rule out PE in the case of a negative testing. Several clinical probability assessment methods are validated: gestalt, the Wells score, or the revised Geneva score. While the debate persists as to the best way to assess clinical probability, its assessment allows for the good interpretation of the investigation results and therefore directs the correct diagnostic strategy. The wide availability of computed tomography pulmonary angiography (CTPA) resulted in a major increase in investigations with a moderate increase in diagnosis, without any notable improvement in patient outcomes. This leads to a new challenge for PE diagnosis which is the limitation of the number of testing for suspected PE. We review different strategies recently developed to achieve this goal. The last challenge concerns the implementation in clinical practice. Two approaches are developed: simplification of the strategies versus the use of digital support tools allowing more sophisticated strategies. Artificial intelligence with machine-learning algorithms will probably be a future tool to guide the physician in this complex approach concerning acute PE suspicion.

肺栓塞 (PE) 在 2021 年仍然是一个诊断挑战。由于病理学可能是致命的，并且体征和症状是非特异性的，因此通常需要进一步的调查。基于贝叶斯方法，临床概率成为在阴性检测情况下排除 PE 的诊断策略的基石。验证了几种临床概率评估方法：格式塔、Wells 评分或修订后的日内瓦评分。虽然关于评估临床概率的最佳方法的争论仍然存在，但其评估可以很好地解释调查结果，因此指导正确的诊断策略。计算机断层扫描肺血管造影 (CTPA) 的广泛应用导致调查的大幅增加，诊断的适度增加，患者预后没有任何显着改善。这给PE诊断带来了新的挑战，即疑似PE检测数量的限制。我们回顾了最近为实现这一目标而制定的不同策略。最后一个挑战涉及临床实践中的实施。开发了两种方法：简化策略与使用允许更复杂策略的数字支持工具。具有机器学习算法的人工智能可能会成为未来的工具，以指导医生采用这种复杂的方法来应对急性 PE 怀疑。最后一个挑战涉及临床实践中的实施。开发了两种方法：简化策略与使用允许更复杂策略的数字支持工具。具有机器学习算法的人工智能可能会成为未来的工具，以指导医生采用这种复杂的方法来应对急性 PE 怀疑。最后一个挑战涉及临床实践中的实施。开发了两种方法：简化策略与使用允许更复杂策略的数字支持工具。具有机器学习算法的人工智能可能会成为未来的工具，以指导医生采用这种复杂的方法来应对急性 PE 怀疑。