PDF(Pubmed)
Abstract：
The application of Artificial Intelligence (AI) facilitates medical activities by automating routine tasks for healthcare professionals. AI augments but does not replace human decision-making, thus complicating the process of addressing legal responsibility. This study investigates the legal challenges associated with the medical use of AI in radiology, analyzing relevant case law and literature, with a specific focus on professional liability attribution. In the case of an error, the primary responsibility remains with the physician, with possible shared liability with developers according to the framework of medical device liability. If there is disagreement with the AI\'s findings, the physician must not only pursue but also justify their choices according to prevailing professional standards. Regulations must balance the autonomy of AI systems with the need for responsible clinical practice. Effective use of AI-generated evaluations requires knowledge of data dynamics and metrics like sensitivity and specificity, even without a clear understanding of the underlying algorithms: the opacity (referred to as the \"black box phenomenon\") of certain systems raises concerns about the interpretation and actual usability of results for both physicians and patients. AI is redefining healthcare, underscoring the imperative for robust liability frameworks, meticulous updates of systems, and transparent patient communication regarding AI involvement.
摘要：
人工智能（AI）的应用通过自动化医疗保健专业人员的日常任务来促进医疗活动。人工智能增强但不会取代人类的决策，从而使处理法律责任的过程复杂化。这项研究调查了与在放射学中使用人工智能相关的法律挑战，分析相关案例和文献，特别关注职业责任归属。如果出现错误，主要责任仍然是医生，根据医疗器械责任框架，可能与开发商分担责任。如果与AI的发现有分歧，医生不仅必须追求，而且必须根据现行的专业标准证明他们的选择。法规必须平衡AI系统的自主性与负责任的临床实践的需要。有效使用人工智能生成的评估需要了解数据动态和指标，如灵敏度和特异性，即使没有对基础算法的清晰了解：某些系统的不透明度（称为“黑盒现象”）引起了对医生和患者结果的解释和实际可用性的担忧。AI正在重新定义医疗保健，强调建立稳健的负债框架的必要性，细致的系统更新，以及关于AI参与的透明患者沟通。
