Imagine a world where doctors have AI assistants, helping them with everything from diagnosing illnesses to summarizing patient cases and even suggesting treatment plans. This isn't science fiction; it's the focus of exciting new research exploring how Large Language Models (LLMs) can revolutionize healthcare. Traditionally, medical LLMs have focused on patient interaction, like providing online consultations. But what if these powerful AI tools could be repurposed to assist doctors directly? Researchers are exploring precisely that, shifting the paradigm from "LLMs as Doctors" to "LLMs for Doctors." A key challenge is understanding the specific needs of doctors. To address this, researchers conducted a two-stage survey with healthcare professionals, identifying 22 key tasks where LLMs could provide the most significant assistance. These range from initial triage and summarizing patient dialogues to more complex tasks like differential diagnosis and recommending next steps for examination. Based on this research, a new Chinese medical dataset called DoctorFLAN was created. It contains a whopping 92,000 question-and-answer samples across those 22 tasks, covering 27 medical specialties. This dataset was used to train a new LLM called DotaGPT. To evaluate DotaGPT's performance, researchers created two benchmarks: DoctorFLAN-test for single-turn questions and answers, and DotaBench to assess more realistic, multi-turn conversations. The results? DotaGPT showed significant improvement over existing medical LLMs, especially in tasks like case summaries and preoperative education. While the initial results are encouraging, challenges remain. Certain tasks, like medication inquiry, still require further refinement to ensure absolute accuracy and patient safety. DotaGPT also faces limitations with other languages. The research highlights the potential of LLMs not to replace doctors, but to augment their abilities, leading to more efficient and potentially more accurate healthcare.