In healthcare, there’s growing concern over doctors becoming less clinically adept(熟练的，擅长的) as they increasingly rely on AI tools. But what about the trainees – medical students, residents and fellows – who are using these tools before they have built their own clinical judgment? The idea of deskilling(技能退化) implies that someone possessed an ability and then lost it. Here, the danger is not just deskilling but never-skilling(从未获得技能). Although a doctor who has forgotten how to reason is recoverable, one who never learned how may not be.(虽然忘记推理的医生可恢复，但从未学会推理的医生可能无法恢复。)
在医疗保健领域，人们越来越担心医生因日益依赖人工智能工具而变得临床技能下降。但那些在建立自己的临床判断力之前就使用这些工具的受训者——医学生、住院医师和专科培训医生——又该如何？"技能退化"这个概念意味着某人曾拥有某种能力，然后失去了它。而在这里，危险不仅是技能退化，更是从未获得技能。虽然一个忘记如何推理的医生是可以恢复的，但一个从未学会推理的人可能就无法恢复了。
OpenEvidence, essentially an AI chatbot for clinician(临床医生)s, has given this concern its most concrete form. About two-thirds of US doctors actively use OpenEvidence, asking about puzzling symptoms, drug interactions and clinical guidelines, getting responses within seconds, anchored in(基于，扎根于) the latest research. Trainees, unsurprisingly, have also begun to use this AI tool in many of the same ways – but at a far more formative(形成期的，发展中的) stage.
OpenEvidence，本质上是一个面向临床医生的AI聊天机器人，将这一担忧具体化了。大约三分之二的美国医生积极使用OpenEvidence，询问令人困惑的症状、药物相互作用和临床指南，并在几秒钟内获得基于最新研究的回答。不出所料，受训者也已开始以许多相同的方式使用这一AI工具——但处于一个更具形成性的阶段。
For example, trainees once asked to build a list of potential diagnoses(诊断（复数）) might struggle and offer an incomplete set, learning what they missed, sometimes painfully. Now, trainees can simply ask OpenEvidence and get a nearly perfect answer, complete with(包括，附带) possibilities they might have never considered and none of the embarrassment(尴尬，难堪) of having overlooked them. Repeating this answer on the wards(病房) may make the trainee look prepared and even impress the supervising(监督的，主管的) doctor.
例如，受训者过去被要求建立一份可能的诊断清单时，可能会苦苦思索并给出不完整的答案，从而了解自己遗漏了什么，有时过程很痛苦。现在，受训者只需询问OpenEvidence，就能得到一个近乎完美的答案，其中包含他们可能从未考虑过的可能性，而且没有因遗漏而产生的尴尬。在病房里复述这个答案可能会让受训者看起来准备充分，甚至给主管医生留下深刻印象。