Systematic review of dermoscopy and digital dermoscopy/ artificial intelligence for the diagnosis of melanoma

Background Dermoscopy improves diagnostic accuracy of the unaided eye for melanoma, and digital dermoscopy with artificial intelligence or computer diagnosis has also been shown useful for the diagnosis of melanoma. At present there is no clear evidence regarding the diagnostic accuracy of dermoscopy compared with artificial intelligence. Objectives To evaluate the diagnostic accuracy of dermoscopy and digital dermoscopy/artificial intelligence for melanoma diagnosis and to compare the diagnostic accuracy of the different dermoscopic algorithms with each other and with digital dermoscopy/artificial intelligence for the detection of melanoma. Methods A literature search on dermoscopy and digital dermoscopy/artificial intelligence for melanoma diagnosis was performed using several databases. Titles and abstracts of the retrieved articles were screened using a literature evaluation form. A quality assessment form was developed to assess the quality of the included studies. Heterogeneity among the studies was assessed. Pooled data were analysed using meta‐analytical methods and comparisons between different algorithms were performed. Results Of 765 articles retrieved, 30 studies were eligible for meta‐analysis. Pooled sensitivity for artificial intelligence was slightly higher than for dermoscopy (91% vs. 88%; P = 0·076). Pooled specificity for dermoscopy was significantly better than artificial intelligence (86% vs. 79%; P < 0·001). Pooled diagnostic odds ratio was 51·5 for dermoscopy and 57·8 for artificial intelligence, which were not significantly different (P = 0·783). There were no significance differences in diagnostic odds ratio among the different dermoscopic diagnostic algorithms. Conclusions Dermoscopy and artificial intelligence performed equally well for diagnosis of melanocytic skin lesions. There was no significant difference in the diagnostic performance of various dermoscopy algorithms. The three‐point checklist, the seven‐point checklist and Menzies score had better diagnostic odds ratios than the others; however, these results need to be confirmed by a large‐scale high‐quality population‐based study.     

基于多层感知器神经网络的住院病案内涵质量预测模型研究

目的/意义探讨影响住院病案内涵质量的重要因素,为病案内涵质控提供预测模型,提高住院病案内涵质量。方法/过程收集2022年6—11月上海市第一人民医院经质控督查的住院病案590份,单因素分析初筛影响因素,构建住院病案内涵质量的多层感知器神经网络预测模型。结果/结论预测模型的AUC为0.940,95%CI为0.928～0.951,灵敏度为93.73%,特异度为78.22%。影响病案评为甲级的独立因素集中在手术安全核查表、首次主任查房分析、手术护理记录单等方面。多层感知器神经网络内涵质量预测模型的预测效能较好,能够为住院病案内涵质量管理提供理论参考。     

以ChatGPT为代表的大语言模型在临床医学中的应用综述

目的/意义 近年来大语言模型技术飞速发展,其在临床中的应用亟待总结分析。方法/过程 在相关文献分析基础上,梳理以ChatGPT为代表的大语言模型在临床问诊、病史采集及文本撰写、临床辅助决策、个性化精准医疗、医患沟通及患者心理支持、学术研究、医学教育、医院管理等临床领域的应用情况。结果/结论 大语言模型具备全面颠覆医疗生态圈的潜力,虽然面临诸多挑战,但终将造福医患。     

我国医学人工智能发展监管现状及政策建议

目的/意义 医学人工智能应用监管碎片化及数据的高门槛现状与医学人工智能发展需求之间矛盾日益突显。在各国积极部署数字健康战略的背景下,如何主动作为、为我国医学人工智能发展做好政策护航已成为亟待解决的问题。方法/过程 基于面向“产-学-研-用-管”多领域开展的调查研究及深度访谈资料,系统梳理医学人工智能特点及发展诉求,从国家战略和行业政策层面对当前监管体系现状进行分析。结果/结论 提出“构建医学人工智能监管衔接机制”的整体政策建议,从组织衔接、数据衔接、人才衔接和技术支撑4个角度展开,为破除当前发展壁垒、促进我国医学人工智能健康蓬勃发展提供解决思路。     

评测纵览:面向“基因-疾病”的关联语义挖掘任务

介绍活跃基因注释语料库的设计目的和标注过程、特点,阐述标注规则、标签设计、标注实例,分析语料库在医药领域的应用场景,包括抗癫痫药物重定位、阿尔茨海默症关键基因提取等领域。     

智慧病房设计与应用

介绍智慧病房设计,包括整体架构、具体实现路径、实施方法、功能模块及应用效果,指出智慧病房结合人工智能技术有助于提升医院病房综合服务水平及患者就医体验。     

院内脓毒症风险管理系统设计与实现

介绍脓毒症防治现状及存在的问题,详细阐述应用人工智能技术构建院内脓毒症风险管理系统的方法,分析系统应用效果,指出该系统可实现患者住院期间脓毒症筛查指标、诊断指标的持续监测与及时预警。     

人工智能在眼部疾病中的应用及其挑战

随着人工智能技术的发展和普及,医学领域也出现了越来越多人工智能(AI)的身影。人工神经网络等新技术与临床的结合正成为研究热点,其中卷积神经网络(CNN)的深度学习算法在图像识别领域取得了巨大的成就,逐渐被用于糖尿病视网膜病变(DR)、年龄相关性黄斑变性(ARMD)、早产儿视网膜病变(ROP)、青光眼和白内障等多种眼科疾病的诊断和筛查中。目前针对不同眼科疾病,世界范围已有多个公开数据库,包括了眼底彩照、光学相干断层扫描(OCT)等多种图像资料,为眼科领域深度学习算法的训练和构建奠定了基础。同时算法本身也在不断优化,使相关AI产品的构建朝着更简便高效的方向发展,同时其临床运用也面临医学伦理和准入标准的问题。总之,深度学习算法的使用为几种常见眼科疾病的筛查诊断带来了巨大的改变也带来挑战,目前尚未大规模的投入临床应用中。本文针对人工智能在眼部疾病中的应用进展做综述,旨在总结这一领域的研究现状和现存问题,并提出对未来的展望。     

医疗材料光学字符识别要素提取数据集MedOCR

介绍医疗材料光学字符识别要素提取数据集MedOCR设计目的、标注过程及数据特点,详细阐述MedOCR数据集的数据来源、标注方法、材料示例,分析数据集测评结果及应用情况,指出研究人员可基于MedOCR开展医疗材料信息提取方向的研究。     

The primary use of artificial intelligence in cardiovascular diseases: what kind of potential role does artificial intelligence play in future medicine?

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