Artificial intelligence (AI) methods have been proposed for the prediction of social behaviors which could be reasonably understood from patient-reported information. This raises novel ethical concerns about respect, privacy, and control over patient data. Ethical concerns surrounding clinical AI systems for social behavior verification can be divided into two main categories: (1) the potential for inaccuracies/biases within such systems, and (2) the impact on trust in patient-provider relationships with the introduction of automated AI systems for fact-checking, particularly in cases where the data/models may contradict the patient. Additionally, this report simulated the misuse of a verification system using patient voice samples and identified a potential LLM bias against patient-reported information in favor of multi-dimensional data and the outputs of other AI methods (i.e., AI self-trust). Finally, recommendations were presented for mitigating the risk that AI verification methods will cause harm to patients or undermine the purpose of the healthcare system.

翻译：人工智能（AI）方法已被提出用于预测可从患者报告信息中合理推断的社会行为。这引发了关于患者数据尊重、隐私和控制权的新颖伦理关切。围绕临床AI系统用于社会行为验证的伦理关切可分为两大类：（1）此类系统内部存在不准确/偏见的可能性，以及（2）引入自动化AI系统进行事实核查对医患关系信任的影响，特别是在数据/模型可能与患者陈述相矛盾的情况下。此外，本报告通过患者语音样本模拟了验证系统的误用，发现大型语言模型（LLM）可能存在一种偏见：倾向于多维数据和其他AI方法的输出（即AI自信任），而非患者报告的信息。最后，本文提出了降低AI验证方法对患者造成伤害或损害医疗系统宗旨的风险的建议。