Imagine an AI arguing a medical diagnosis. Not just offering a conclusion, but building a case with evidence, just like a doctor would in a case review. That's the fascinating challenge tackled in new research exploring how to judge the quality of AI-generated medical explanations. Evaluating these AI-generated arguments is tricky. Traditional methods, like comparing the AI's words to a "gold standard" text, fall short because there can be many valid ways to explain a medical decision. This research introduces a clever approach: proxy tasks. Instead of directly judging the explanation, they test how well the AI performs on related medical tasks when given the explanation as extra information. For example, how does the AI's performance on a medical question-and-answer task change when it also has its own generated explanation? The researchers used three proxy tasks: answering multiple-choice medical questions, detecting medical misinformation, and inferring conclusions from clinical trials. The results are promising. They built an AI "evaluator" trained on synthetic (AI-generated) explanations, and this evaluator’s judgments closely matched those of human medical experts. This suggests we can train AIs to judge the reasoning of other AIs, opening exciting new avenues for more reliable and robust automated evaluations. The study also cleverly introduced “control cases” – like feeding the AI nonsense medical text – to see if the evaluator could spot the difference. This is crucial for ensuring the AI doesn't just prioritize longer or more complex explanations, but actually focuses on the quality of the argument. This research is a significant step toward trustworthy medical AI. It's not just about getting the right answer; it's about understanding the "why" behind it, and ensuring AI can explain its reasoning in a way that's both understandable and accurate, paving the way for AIs that can truly assist medical professionals in complex decision-making.