人工智能和机器学习技术在解决复杂的世界问题方面具有巨大的潜力。他们可以通过从大量患者数据中“学习”提供可行的见解来促进临床决策。等等deep learning algorithms were proved able to accurately identify head CT scan abnormalitiesrequiring urgent attention, significantly increasing the efficiency of health services。

受到如此激动人心的发展的鼓励，越来越多地期望AI成为一种有前途的手段，可以在不久的将来实现高性能的药物，并希望能成为世界各地过度拉伸的卫生系统的救助，而在Covid-19 Plopicative之后。但是，这一承诺带来了临床决策的关键和令人震惊的警告：有一个共识，即AI模型，尤其是使用数据驱动技术的模型受到或自身引起的偏见和歧视，加剧了现有的健康不平等。

健康不平等

Recent studies show that without proper mitigation on potential bias against underrepresented groups such as women and ethnic minorities, implementation of AI in healthcare can result in life or death consequences. Astudy by Straw and Wushowed that AI models built to identify people at high risk of liver disease from blood tests are twice as likely to miss the disease in women as in men。

这是因为数据驱动的AI模型通过从他们分析的数据中找到“模式”来推断，但诸如种族和种族基础之类的差异长期存在于健康和护理中。Without effective mitigation approaches,inferences learnt from such biased data are inevitably channeling embedded inequities into the decisions they make.

Apart from data-embedded structural health inequities, under-representation of minorities in health datasets creates a real technical challenge for machine learning to come up with sensible conclusions for such groups, creating another potential source of inequities exacerbated by AI.少数群体的样本数量不足将导致计算模型为其绘制不准确的预测。不幸的是，这种情况对于医疗保健数据集中的少数民族群体普遍存在，这可能不是always reflect the actual population diversity:machine learning models trained on such data would draw inaccurate conclusions regarding the incidence or the risk of a disease within a specific population subset.

In addition to those embedded in data, biases could also arise from methodological choices for AI development and deployment. Obermeyer and colleaguesanalysed an algorithm widely employed in the US这将健康费用用作健康需求的代理，他们证明它错误地得出结论，黑人患者比同样病的白人患者更健康。从技术上讲，bias might be unintentionally and easily induced in the feature selections (the choice of the input variables) and label determinations (the choice of target variables) of AI model developments.

Patient care

除了潜在的偏见和不平等现象外，与在医疗保健中使用AI有关的道德问题已经建立了良好的问题，还有许多其他挑战可能会对患者护理产生重大影响。AI不仅有可能影响诊断，而且还影响了系统尺度的预防，治疗和疾病管理，从而引发了有关其在公共卫生中的作用的更广泛问题，例如预期流行病和提供患者的支持。人工智能是数据驱动的，医疗保健数据通常很难（甚至不可能）匿名，这引起了对患者隐私和数据保护的担忧。

在AI驱动的决策中，法律问责制和道德责任的问题已经提出，引起人们对医生关系的潜在变化的关注。AHallowell等人的研究。最近出版BMC医学伦理探索在AI医疗工具中可以放置哪些条件下的信任，这强调了关系和认知信任至关重要，因为临床医生的积极经验与未来患者对AI工具的信任直接相关。这项研究强调了在设计值得信赖的，有信心的AI过程时需要进行故意和细致的步骤。

越来越多数据的医学实践可能需要下一代医学专业人员的新技能，对医学教育的未来产生了影响。为了解决这些问题，实施关键框架，例如嵌入医学AI发展的伦理是必须的。

需要减轻或克服道德问题的工具

All in all, the use of AI in medicine, although it may bear high reward, is currently associated with high risk, as its consequences and implications are high-stakes including widening the social gap in health services and further fragmenting an already-divided society. Some tools are already availablefor general bias audit或特别是designed for healthcare application，以及重点的研究社区正在出现，例如卫生公平集团在艾伦·图灵学院和独立社区卫生公平数据科学。

Metrics such as fairness, accountability, methods for bias mitigation, and explainability or interpretability are major aspects that impact the perception and use of AI in ethically-sensitive fields such as medicine, and methods endowed with these features might play a significant role in overcoming mistrust, although they could not guarantee trust.

但是，我们仍然没有清楚地了解数据所包含的和AI引起的医疗保健及其对我们社会的影响：AI模型的偏见没有得到量化，并以与准确性相同的热情关注，更不用说有效了部署前的缓解方法。同样，与该领域的令人印象深刻的扩展相比，从多种实际和理论观点来看，关于AI在医疗保健中使用的影响的研究仍然相对有限：需要对这些新技术进行公平和批判性的考虑。

由于这些原因，一个跨朱纳尔的收藏，人工智能健康与医学的伦理学，已与BMC医学伦理和BMC Medical Informatics and Decision Making。该集合欢迎研究针对这些与这些与伦理有关的特征的基于AI的医学决策方法的技术评估和评估的研究，以及关于基于AI的医学方法的更多理论考虑。这包括但不限于呈现能够满足此类道德要求和能够减轻诸如偏见，新颖道德与道德的指标的阐述，医生和公众对公众的研究的阐述，阐述诸如偏见的问题和工具的新型方法的介绍。AI实施，围绕AI相关的隐私和监视的道德规范以及围绕医疗AI实施的伦理挑战。