Anibal James, Gunkel Jasmine, Awan Shaheen, Huth Hannah, Nguyen Hang, Le Tram, Bélisle-Pipon Jean-Christophe, Boyer Micah, Hazen Lindsey, Bensoussan Yael, Clifton David, Wood Bradford
Center for Interventional Oncology, Clinical Center, National Institutes of Health (NIH), Bethesda, MD USA.
Computational Health Informatics Lab, Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK.
Npj Health Syst. 2024;1(1):1. doi: 10.1038/s44401-024-00001-4. Epub 2024 Dec 5.
Artificial intelligence (AI) methods have been proposed for the prediction of social behaviors that 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.
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理解的社会行为。这引发了关于尊重、隐私以及对患者数据控制权的新的伦理问题。围绕用于社会行为验证的临床人工智能系统的伦理问题可分为两大类：（1）此类系统中存在不准确/偏差的可能性，以及（2）引入用于“事实核查”的自动化人工智能系统对患者与医疗服务提供者关系中的信任产生的影响，特别是在数据/模型可能与患者相矛盾的情况下。此外，本报告使用患者语音样本模拟了验证系统的滥用情况，并识别出大型语言模型（LLM）对患者报告信息存在潜在偏差，倾向于多维数据和其他人工智能方法的输出结果（即“人工智能自我信任”）。最后，针对降低人工智能验证方法对患者造成伤害或破坏医疗系统目的的风险，提出了相关建议。
