Artificial intelligence for the early detection of colorectal cancer: A comprehensive review of its advantages and misconceptions

Colorectal cancer (CRC) was the second-ranked worldwide type of cancer during 2020 due to the crude mortality rate of 12.0 per 100000 inhabitants. It can be prevented if glandular tissue (adenomatous polyps) is detected early. Colonoscopy has been strongly recommended as a screening test for both early cancer and adenomatous polyps. However, it has some limitations that include the high polyp miss rate for smaller (< 10 mm) or flat polyps, which are easily missed during visual inspection. Due to the rapid advancement of technology, artificial intelligence (AI) has been a thriving area in different fields, including medicine. Particularly, in gastroenterology AI software has been included in computer-aided systems for diagnosis and to improve the assertiveness of automatic polyp detection and its classification as a preventive method for CRC. This article provides an overview of recent research focusing on AI tools and their applications in the early detection of CRC and adenomatous polyps, as well as an insightful analysis of the main advantages and misconceptions in the field.     

医学情报研究机构编发《妇幼保健管理舆情简报》的实践探索

本文以《妇幼保健管理舆情简报》的编发背景、栏目设置、主要内容、发行效果及改进措施为例，探讨了医学情报研究机构为妇幼卫生主管部门和妇幼保健机构提供决策与管理情报的工作模式，总结出了及时了解信息需求，组建相对稳定团队，获得单位重视支持的经验体会，为其他信息服务部门开展相关工作提供参考借鉴。     

The Meaning of Embodiment

There is substantial disagreement among philosophers of embodied cognitive science about the meaning of embodiment. In what follows, I describe three different views that can be found in the current literature. I show how this debate centers around the question of whether the science of embodied cognition can retain the computer theory of mind. One view, which I will label body functionalism, takes the body to play the functional role of linking external resources for problem solving with internal biological machinery. Embodiment is thus understood in terms of the role the body plays in supporting the computational circuits that realize cognition. Body enactivism argues by contrast that no computational account of cognition can account for the role of commonsense knowledge in our everyday practical engagement with the world. I will attempt a reconciliation of these seemingly opposed views.     

人工智能在病理学教学中的应用

病理学属于基础医学学科,它着重研究疾病的起因、形态结构改变以及由这些变化引起的机体功能改变,为临床诊断提供依据,是基础医学与临床医学之间的重要纽带和过渡,其教学质量对后续临床实践起到举足轻重的作用,病理学教学也从传统的教学模式发展出混合式教学、虚拟病理实验室、互联网+等多种教学模式。近年来随着人工智能在医疗医学行业的飞速发展,其技术已广泛应用到医疗领域的各个方面,培养适应智能化医疗时代的新型病理人才刻不容缓。文章研究从病理学科教学工作的特点和性质入手,结合我国病理学人工智能应用现状,分析了中国目前病理专业技能培养的现状,为人工智能在病理人才专业技能与素质培养的应用中提供启发。     

What about doctors? The impact of medical errors

Medical error is a distressing event to the patient and the health care providers. The impact of such events has been well studied on patients but poorly on health professionals. These events are still considered as a taboo in the medical culture and hence missed as great learning opportunities. They have negative impact on doctors' emotional wellbeing, general quality of life, and their professional practice and conduct. Medical errors and adverse events also affect the quality and cost of the health service. Health service administrations should provide healthcare professionals involved in such events with professional support and counselling services, and should consider and treat them as second victims.     

Differential diagnosis of jaw pain using informatics technology

This study aimed to deduce evidence‐based clinical clues that differentiate temporomandibular disorders (TMD)‐mimicking conditions from genuine TMD by text mining using natural language processing (NLP) and recursive partitioning. We compared the medical records of 29 patients diagnosed with TMD‐mimicking conditions and 290 patients diagnosed with genuine TMD. Chief complaints and medical histories were preprocessed via NLP to compare the frequency of word usage. In addition, recursive partitioning was used to deduce the optimal size of mouth opening, which could differentiate TMD‐mimicking from genuine TMD groups. The prevalence of TMD‐mimicking conditions was more evenly distributed across all age groups and showed a nearly equal gender ratio, which was significantly different from genuine TMD. TMD‐mimicking conditions were caused by inflammation, infection, hereditary disease and neoplasm. Patients with TMD‐mimicking conditions frequently used “mouth opening limitation” (P < .001), but less commonly used words such as “noise” (P < .001) and “temporomandibular joint” (P < .001) than patients with genuine TMD. A diagnostic classification tree on the basis of recursive partitioning suggested that 12.0 mm of comfortable mouth opening and 26.5 mm of maximum mouth opening were deduced as the most optimal mouth‐opening cutoff sizes. When the combined analyses were performed based on both the text mining and clinical examination data, the predictive performance of the model was 96.6% with 69.0% sensitivity and 99.3% specificity in predicting TMD‐mimicking conditions. In conclusion, this study showed that AI technology‐based methods could be applied in the field of differential diagnosis of orofacial pain disorders.