Imagine an AI that could diagnose like a doctor. It's a compelling vision, and recent advancements in large language models (LLMs) have brought us closer than ever. But how do we truly measure an AI's clinical abilities, beyond just medical knowledge? A new research paper introduces "MedQA-CS," a groundbreaking benchmark that goes beyond multiple-choice medical quizzes and evaluates LLMs in a way that mirrors real-world clinical skills assessments. The research team created a framework inspired by medical education's Objective Structured Clinical Examinations (OSCEs), which present students with realistic patient scenarios requiring them to gather information, perform virtual exams, and propose diagnoses. MedQA-CS presents two key challenges to LLMs. First, acting as a medical student, the LLM must navigate complex patient interactions, asking relevant questions and justifying its clinical decisions. Second, the LLM plays the role of an examiner, scoring another LLM's performance, just like a seasoned physician evaluating a medical student. The results were intriguing. While LLMs have shown impressive performance on knowledge-based medical tests, their clinical skills lagged behind. This highlights a key difference: knowing medical facts isn't the same as applying that knowledge in a dynamic, patient-centered environment. This gap is where MedQA-CS shines, revealing the areas where AI still needs to improve to effectively assist in real-world clinical workflows. Interestingly, the researchers found that simply training LLMs on more medical data didn't necessarily improve their clinical skills. In fact, it sometimes even hindered their ability to follow clinical instructions. This suggests that improving AI's clinical reasoning requires more than just cramming in medical facts. The study also explored the potential of LLMs to act as automated clinical skills examiners. Some advanced LLMs, like GPT-4, showed promising results, scoring the performance of other LLMs in a way that closely aligned with human expert evaluations. This raises the possibility of creating AI-powered tools to help streamline the evaluation process in medical education. The MedQA-CS benchmark offers valuable insights for the future of AI in healthcare. It reveals the current limitations of LLMs and points towards new training strategies that combine domain knowledge with practical, patient-centered clinical reasoning. Ultimately, research like this paves the way for the development of AI systems that can truly augment and support healthcare professionals in delivering better patient care.