关于DICER clea，以下几个关键信息值得重点关注。本文结合最新行业数据和专家观点，为您系统梳理核心要点。
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另外值得一提的是，To meet the growing demand for radiology artificial-intelligence tools, a 3D vision–language model called Merlin was trained on abdominal computed-tomography scans, radiology reports and electronic health records. Merlin demonstrated stronger off-the-shelf performance than did other vision–language models across three hospital sites distinct from the initial training centre, highlighting its potential for broader clinical adoption.
展望未来，DICER clea的发展趋势值得持续关注。专家建议，各方应加强协作创新，共同推动行业向更加健康、可持续的方向发展。