The increasing integration of artificial intelligence (AI) into medical diagnostics necessitates a critical examination of its ethical and practical implications. While the prioritization of diagnostic accuracy, as advocated by Sabuncu et al. (2025), is essential, this approach risks oversimplifying complex socio-ethical issues, including fairness, privacy, and intersectionality. This rebuttal emphasizes the dangers of reducing multifaceted health disparities to quantifiable metrics and advocates for a more transdisciplinary approach. By incorporating insights from social sciences, ethics, and public health, AI systems can address the compounded effects of intersecting identities and safeguard sensitive data. Additionally, explainability and interpretability must be central to AI design, fostering trust and accountability. This paper calls for a framework that balances accuracy with fairness, privacy, and inclusivity to ensure AI-driven diagnostics serve diverse populations equitably and ethically.

翻译：人工智能（AI）在医学诊断中的日益深入应用，亟需对其伦理与实践影响进行批判性审视。尽管Sabuncu等人（2025）所倡导的诊断准确性优先原则至关重要，但该方法可能过度简化包括公平性、隐私与交叉性在内的复杂社会伦理问题。本反驳强调将多维健康差异简化为可量化指标的风险，并主张采用更具跨学科性的研究路径。通过融合社会科学、伦理学与公共卫生领域的洞见，AI系统能够应对交叉身份认同的复合效应并保护敏感数据。此外，可解释性与可解读性必须成为AI设计的核心要素，以促进信任与问责。本文呼吁建立一种平衡准确性、公平性、隐私性与包容性的框架，从而确保AI驱动诊断能够公正且合乎伦理地服务于多元化人群。