利用不完美性：MEDLEY：一种多模型方法，利用医学AI中的偏差
Leveraging Imperfection with MEDLEY A Multi-Model Approach Harnessing Bias in Medical AI
AI总结：
MEDLEY提出了一种多模型框架，通过保留模型多样性而非压缩共识，利用医学AI中的偏差提升诊断准确性与透明度。
摘要：
在医学人工智能中，偏差通常被视为缺陷需要消除。然而，人类推理本质上会融入受教育、文化和经验影响的偏差，这表明其存在可能是不可避免的，并且可能具有价值。我们提出了MEDLEY（具有利用多样性的医学综合诊断系统），一种概念框架，该框架协调多个AI模型，同时保留其多样化的输出，而不是将它们压缩成共识。与传统方法不同，MEDLEY将模型特定的偏差作为潜在优势，并将幻觉视为供临床医生验证的临时假设。使用超过30个大型语言模型开发了一个概念验证演示器，创建了一个最小可行产品，在合成案例中保留了共识和少数观点，使诊断不确定性和潜在偏差对临床监督透明。虽然尚未成为经过验证的临床工具，但该演示展示了如何在临床监督下通过结构化多样性增强医学推理。通过将AI的不完美性重新定义为资源，MEDLEY提供了一种范式转变，为开发可信的医学AI系统开辟了新的监管、伦理和创新途径。
英文摘要：
Bias in medical artificial intelligence is conventionally viewed as a defect requiring elimination. However, human reasoning inherently incorporates biases shaped by education, culture, and experience, suggesting their presence may be inevitable and potentially valuable. We propose MEDLEY (Medical Ensemble Diagnostic system with Leveraged diversitY), a conceptual framework that orchestrates multiple AI models while preserving their diverse outputs rather than collapsing them into a consensus. Unlike traditional approaches that suppress disagreement, MEDLEY documents model-specific biases as potential strengths and treats hallucinations as provisional hypotheses for clinician verification. A proof-of-concept demonstrator was developed using over 30 large language models, creating a minimum viable product that preserved both consensus and minority views in synthetic cases, making diagnostic uncertainty and latent biases transparent for clinical oversight. While not yet a validated clinical tool, the demonstration illustrates how structured diversity can enhance medical reasoning under clinician supervision. By reframing AI imperfection as a resource, MEDLEY offers a paradigm shift that opens new regulatory, ethical, and innovation pathways for developing trustworthy medical AI systems.
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