Attaching Importance to the Standardized Construction of Artificial Intelligence Database of Medical Imaging
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摘要:目的 调查临床医学(八年制)专业医学生甲状腺癌相关知识及甲状腺自查方法的掌握情况，为临床前医学教育和临床教学提供借鉴和指导。方法 2020年3月，采用便利抽样法选取北京协和医学院临床医学(八年制)专业三至六年级医学生为调查对象。将三、四年级医学生定义为临床前阶段医学生(preclinical medical students，PMS)，将五、六年级医学生定义为临床阶段医学生(clinical medical students，CMS)，采用自行设计的问卷对此两类医学生开展网络调查。结果 共发放问卷337份，回收有效问卷274份(PMS 129份，CMS 145份)。CMS在甲状腺癌预后(97.2% 比64.5%，P＜0.001)、诊断方式(95.6% 比33.1%，P＜0.001)及手术治疗指征(82.1% 比58.1%，P=0.001)方面的认知水平高于PMS；在甲状腺癌危险因素方面，二者的认知水平接近。相较于PMS，更高比率的CMS认为甲状腺结节出现恶变的概率在5%及以下(45.5% 比6.5%，P＜0.001)，且更高比率的CMS支持无症状及结节时也应进行甲状腺癌筛查(62.1% 比41.9%，P＜0.001)。CMS进行甲状腺自查的比率高于PMS(55.9%比12.1%，P＜0.001)，但进行规律自查的比率较低(19.8%，16/81)。结论 临床教学可显著提高医学生的甲状腺癌认知水平，但CMS对甲状腺结节持更加乐观态度，轻视自查，且对甲状腺超声筛查缺乏正确的成本-效益认识。建议今后应重视医学生的临床前通识教育，丰富临床阶段的实践内容，充分发挥其社会科普宣传效应。Abstract: Medical imaging is regarded as one of the most potential domains where artificial intelligence can be applied in practice. However, artificial intelligence is facing challenges resulting from continuous growth of data, such as lack of high-quality data, lack of standardization in domain, lack of effective data management and regulation. Therefore, it is necessary to construct a standardized medical imaging database complying with the national condition of China, laws/regulations, and using habits of researchers. FAIR data principle (findable, accessible, interoperable, and reusable) may play a key role in database construction, data acquisition, and regulating descriptions of medical imaging data. Looking forward to boosting the standardized construction of artificial intelligence databases of medical imaging under the combined efforts of national researchers.
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Key words:
- medical imaging /
- artificial intelligence /
- FAIR data principle /
- standardized database
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