人工智能医疗设备应用的伦理问题分析

人工智能医疗设备在医学诊断、治疗及康复领域中的应用越来越广泛,在给传统临床诊断和治疗带来技术突破的同时,也伴随着一些伦理风险和挑战,人工智能医疗设备应用的伦理问题越来越受到重视。本文综述了当前人工智能医疗设备应用的积极影响,对人工智能医疗设备应用产生的伦理问题进行分析,找到解决问题的对策,以期促进人工智能医疗设备的应用和发展。     

医学人工智能领域伦理治理重点研究

目的 探讨医学人工智能领域伦理治理的重点。方法 通过综合查阅相关文献,比较中外医学人工智能领域相关法律法规,分析潜在伦理问题的治理重点,并提出相应的治理策略。结果 目前中国医学人工智能领域相关法律法规和监管体系有待完善,伦理治理的重点应着眼于保护隐私权、保障算法的透明与公正、厘清责任界定和分配、明确公众认知和态度等核心问题。结论 政府和社会各界应积极借鉴国际立法经验,通过加强顶层设计、健全政策法规、重视公众反馈、强化跨学科合作,从政策制定、法律框架、科学研究和技术研发等方面构建一个全方位、多层次的伦理治理体系。     

人工智能医学应用的伦理问题

人工智能是指像人类一样能够理性思考和行动的智能系统。随着人工智能技术的兴起,其在医学领域得到日益广泛的应用。与传统医疗服务技术相比,人工智能的医学应用在提高医疗质量、降低医疗成本、优化治疗方案以及提高医疗效率方面都有较多优势。不过,人工智能的医学应用也提出了若干伦理挑战。如人工智能(只能执行特定任务或预定义)在医学领域应用中表现的安全性风险问题、患者隐私保护、应用的可及性和可负担性问题以及责任划分等方面的伦理挑战。     

世界卫生组织《医疗卫生中人工智能的伦理治理》指南及对中国的启示

随着深度学习和大数据技术的爆发式增长,人工智能渗透应用于医疗卫生各个领域,为患者带来高效优质的健康服务的同时,也涌现出一系列伦理和社会治理问题。为规避和消解医学人工智能发展中可以预见的伦理风险和治理挑战,世界卫生组织（WHO）于2021年6月28日首次发布《医疗卫生中人工智能的伦理治理》指南,旨在对临床实践中部署人工智...     

医学高技术的应用带来的医学伦理问题及其对策研究

该文从医学高新技术的临床运用带来的伦理困惑入手,探讨了如何防止高科技的应用引起的伦理缺陷。     

人工智能的伦理困境探讨

人工智能就是让机器或人所创造的其他人工方法或系统来模拟人类智能。人工智能发展迅速,在为人类带来福祉的同时也带来了若干伦理方面问题,诸如隐私伦理、公平伦理、责任伦理以及主体性伦理等。针对以上伦理困境,提出了相应的解决思路:规范伦理原则,借鉴国际已有的伦理准则,总结适合我国的伦理原则;限制人工智能,一方面限制人工智能本身,另一方面限制人工智能的范围;明确责任归属;明确价值导向以及健全规章制度。     

智慧医疗场景下人工智能应用伦理问题与治理路径探讨

随着人工智能在医疗健康领域的广泛应用,医学伦理学与科技伦理挑战也随之而来。作者归纳了人工智能在医疗健康领域的应用现状,探讨了目前人工智能技术应用于医疗领域导致的医生主体地位被削弱、数据或隐私安全风险增加、医患信任危机加剧、医疗资源分配不均加剧、事故责任界限模糊等伦理问题,剖析了上述伦理问题产生的原因,最后从强化医生主体地位、建立健全信息数据保护制度、增强患者对医务人员的信任感、提高医疗资源配置效率、完善事故责任界定体系等5个方面提出治理路径,以期为人工智能技术更好地助力医疗发展提供参考。     

医学客体与医学伦理问题的分类

根据马克思主义基本原理,医学伦理问题可以被划分为单客体伦理问题和双客体伦理问题,单客体伦理问题表现为非对抗形式和对抗形式;在双客体伦理问题中,医学实践的客体有一对,主要矛盾存在于两个客体之间。这种划分有助于我们把握具体伦理问题的症结和本质。     

医学科研活动的伦理审查问题

以医学伦理审查为指导,以医学科研活动过程——科研项目立项、实施、结题为线索,对医学科研活动中的伦理审查问题进行了探讨,并提出了在医学科学研究活动中应注意的医学科研选题立项、医学科研项目实施、医学科研结题等过程中的伦理审查问题。     

医学编辑工作中关注医学科研伦理问题的重要性

医学科研伦理审查自20世纪80年代末引入我国,至1998年我国颁布了《药品临床试验管理规范》,在医学科学研究中伦理问题日益受到关注。医学专业期刊作为传播最新研究成果的重要载体与医学科学研究中的伦理问题密切相关。由于我国医学科学研究中的伦理问题审查起步较晚,运作不够规     

Artificial Intelligence Meets Chinese Medicine

As an interdisciplinary subject of medicine and artificial intelligence, intelligent diagnosis and treatment has received extensive attention. The standardization of Chinese medicine (CM) diagnosis has been always a bottleneck in the modernization and globalization of CM. Studying the application technology of artificial intelligence in CM and solving the problems is an urgent need for the development of modern CM in the era of artificial intelligence. Taking the pneumonia with dyspnea and cough in CM as an example, this article gives an overview of intelligent medical technology and application development, brings forward the present technical problems faced and the new advances in intelligent technology on CM diagnosis and treatment.     

医学人工智能与生态动力学问题（一）

从医学人工智能研究对象的特殊性入手，分析了经典决定论工具的成长局限和不适应原理，提出新的生态动力学理论结构和操作方法，并据此建立起新的医学人工智能模型。     

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。     

Artificial Intelligence in Healthcare and Medicine: Promises,Ethical Challenges and Governance

Artificial intelligence (AI) is rapidly being applied to a wide range of fields, including medicine, and has been considered as an approach that may augment or substitute human professionals in primary healthcare. However, AI also raises several challenges and ethical concerns. In this article, the author investigates and discusses three aspects of AI in medicine and healthcare: the application and promises of AI, special ethical concerns pertaining to AI in some frontier fields, and suggestive ethical governance systems. Despite great potentials of frontier AI research and development in the field of medical care, the ethical challenges induced by its applications has put forward new requirements for governance. To ensure “trustworthy” AI applications in healthcare and medicine, the creation of an ethical global governance framework and system as well as special guidelines for frontier AI applications in medicine are suggested. The most important aspects include the roles of governments in ethical auditing and the responsibilities of stakeholders in the ethical governance system.     

大系统人工智能的医学路线

大系统医学人工智能在中国的第一次兴起,是伴随着微型计算机引入中国,时间点在上世纪八十年代,技术特征是模仿诊疗过程,典型应用集中在中医界,杰出代表是朱文锋教授[1]。但发展至上世纪九十年代中后期,由于大系统医学人工智能是一门复杂的交叉学科,对人才的复合技能要求非常高,特别是数学建模技能对于一些医学科研工作来说很难,从而使得医学人工智能的发展较为缓慢,科凌力智能也不例外,但一直在坚持。今天,阿尔法围棋（AlphaGo）的面世,又激起了我们坚持下去的勇气。     

医学人工智能领域的技术创新态势研究

目的:通过对医学人工智能领域获得授权的发明专利分析,揭示全球技术竞争态势。方法:对医学人工智能领域近20年全球发明专利授权情况进行分析,从发明专利授权数量与趋势、技术发源地、目标市场、专利权机构、发明人、技术热点等角度展示该领域的技术创新趋势,揭示中国的技术实力水平。结果与结论:全球医学人工智能领域发明专利授权量逐年增长,技术热点聚焦于医学影像、药物研发、辅助诊断三个方向。中国成为继美国之后的全球第二大技术发源地和目标市场,中国多个专利权机构和发明人的发明专利授权量跻身全球前列。     

Releasing of The White Paper on Medical Imaging Artificial Intelligence in China

In the afternoon of March 26,2019, The White Paper on Medical Imaging Artificial Intelligence in China was officially released in Beijing by the Chinese Innovative Alliance of Industry, Education, Research and Application of Artificial Intelligence for Medical Imaging (CAIERA). The white paper was co-operatively written by the medical imaging experts from the tertiary Chinese hospitals, the scientific experts from AI research institutions and the leading AI medical enterprises in China. The contents of the white paper not only cover the up-to-date application of AI in medical field, the latest advances of AI algorithms in medical image processing, the data requirement for medical AI development, and the current situation of structured data, but also expatiate the goal and challenge of clinical application for medical imaging AI development in 16 medical subject areas, which helps to identify the demands and opportunities for the AI industry. Forty representative enterprises of AI medical imaging in China were introduced in the white paper. The white paper points out the three key problems in development of AI products: the robustness, ease of usage and data security, which provides guidance of direction and strategy for the enterprises. Particularly, in view of the national policy on developing AI, the white paper gives a profound analysis on the challenges and the opportunities that medical imaging AI is facing. These contents agglomerate the cutting-edge efforts of experts in the industry-academia-research-application chain of medical AI, represent the mainstream voice of the society in China. The White Paper will play a guiding role in understanding the market demands and establishing standardized systems in the path of landing AI products in the field medical imaging. Full text of the White paper is publicly accessible from the CAIERA website, or by scanning the 2D code in the article.     

人工智能医疗器械质量管理标准化趋势分析

目的旨在研究人工智能(Artificial Intelligence,AI)医疗器械质量管理的发展趋势,促进标准规范研究。方法根据AI医疗器械相关监管、法规的最新动态,梳理质量管理面临的特殊问题;结合产品检测实践情况,分析AI医疗器械质量管理的需求与发展方向。结果在AI医疗器械的质量管理过程中,可溯源性、数据集管理、质量控制等方面都需进一步增强。结论本文研究的思路和方法有助于建立适合AI医疗器械的专用标准规范。     

张謇与中国传统的伦理智慧

张謇的伦理思想既有其鲜明的个性特征,同时蕴含着中国传统的伦理智慧。张謇对儒家的“中庸之道”终身服膺并身体力行,认为“天下事贵得其中,若趋于极端,往往不能成事”,在处世态度和人际交往上都反映了他凡事把握“度”,不走极端的理性思想和行为;张謇一生崇尚平实精神,对讲究“经世致用”的前辈先儒如汉代的田畴、明代的顾炎武颇为崇拜而奉为师法,这也决定了他的实践具有功用性和务实性的显著特点;张謇虽不信教却敬教,认为传统的佛教伦理道德可以端正世道人心,“济孔孟之教之术之穷”,因而主张“儒言佛言,共相维救”,他导扬佛教,但并没有把佛教伦理道德当成惟一的救世良方而影响他在文化教育方面的投入。     

Advances of Artificial Intelligence Application in Medical Imaging of Ovarian Cancers

Ovarian cancer is one of the three most common gynecological cancers in the world, and is regarded as a priority in terms of women's cancer. In the past few years, many researchers have attempted to develop and apply artificial intelligence (AI) techniques to multiple clinical scenarios of ovarian cancer, especially in the field of medical imaging. AI-assisted imaging studies have involved computer tomography (CT), ultrasonography (US), and magnetic resonance imaging (MRI). In this review, we perform a literature search on the published studies that using AI techniques in the medical care of ovarian cancer, and bring up the advances in terms of four clinical aspects, including medical diagnosis, pathological classification, targeted biopsy guidance, and prognosis prediction. Meanwhile, current status and existing issues of the researches on AI application in ovarian cancer are discussed.