在应对医学 GPT 技术开发过程中的重重挑战与固有限制时，笔者建议采取全面且深入的革新策略，这要求从数据获取到系统运维的每一个环节都进行精细化重塑。数据的收集不再仅仅是量的积累，而是质的飞跃，意味着要从海量的医疗信息中精挑细选，确保每一份数据都具备高度代表性、准确性和可用性。这一过程不仅需要先进的技术手段支持，更需医学专家的深度参与，以实现数据的精准筛选与价值挖掘。在基座模型的选择上，应摒弃传统的简单问答框架，转而探索构建专家数字人分身的可能性。这样的转变能够让患者仿佛直接面对经验丰富的医生，获得更加个性化、专业化的医疗咨询服务，极大地提升了交互体验与信任度。同时，为了确保医疗信息的安全与准确性，建议应用基于AI的智能质量控制机制来替代过往的盲目依赖，通过算法自动审核与人工复核相结合的方式，严把质量关。此外，模型的训练评估与优化也应更加注重实践经验的融合。在循证医学的基础上，倡导将大医的临床智慧与经验融入模型之中，使医学 GPT技术不仅能够依据最新的科研成果，还能结合临床实际，为患者提供更为精准、个体化的诊疗建议。简言之，要实现从数据清洗到精选、从简单咨询到专家分身、从盲目信任到质控把关、从单纯循证到结合大医经验的四大转变。

In order to cope with the challenges and inherent limitations in the development of medical GPT technology, the  author suggests a comprehensive and in-depth innovation strategy, which requires a refined reshaping of every link from data  acquisition to system operation and maintenance. Data collection is no longer just a quantitative accumulation but a qualitative leap, which means carefully selecting from a vast amount of medical information to ensure that each piece of data is highly representative, accurate, and usable. This process requires not only the support of advanced technical means, but also the in-depth participation of  medical experts to achieve accurate data screening and value mining. In the selection of the pedestal model, the traditional simple  question and answer framework should be abandoned, and the possibility of building an expert digital human doppelganger should be  explored. This transformation allows patients to receive more personalized and professional medical consultation services as if they  were directly facing experienced doctors, which greatly improves the interactive experience and trust. At the same time, in order to  ensure the security and accuracy of medical information, it is recommended to apply an AI-based intelligent quality control mechanism  to replace the blind reliance in the past, and strictly control the quality through a combination of automatic review by algorithm and  manual review. In addition, the training, evaluation and optimization of models should also pay more attention to the integration of  practical experience. On the basis of evidence-based medicine, it advocates the integration of the clinical wisdom and experience of big  doctors into the model, so that medical GPT technology can not only provide patients with more accurate and individualized diagnosis  and treatment suggestions based on the latest scientific research results, but also combine with clinical practice. In short, it is necessary  to realize the four major transformations from data cleaning to selection, from simple consultation to expert clone, from blind trust to  quality control, and from simple evidence-based to combined with the experience of doctors.