让医疗AI的偏见变优势，通过多元模型协作提升诊断透明度
Leveraging Imperfection with MEDLEY A Multi-Model Approach Harnessing Bias in Medical AI
- 用多个大模型并行输出，不强制统一意见，保留各自偏差
- 将错误判断视为待验证假设，供医生审查，提升决策可解释性
- 适合关注AI可信度与临床协作的医疗从业者和研究者
医疗人工智能中的偏差传统上被视为需消除的缺陷。然而，人类推理本身受教育、文化与经验影响而带有偏见，这表明其存在可能不可避免且具有潜在价值。本文提出MEDLEY（医学集成诊断系统，借助多样性），一种概念框架，协调多个AI模型时保留其多样化输出，而非强制达成共识。不同于传统方法压制分歧，MEDLEY将模型特定偏差记录为潜在优势，并将幻觉视为需临床医生验证的暂定假设。基于30多个大型语言模型构建了一个原型系统，在合成病例中同时呈现共识与少数观点，使诊断不确定性与隐含偏差对临床监督者透明。尽管尚未经过临床验证，该演示展示了结构化多样性如何在医生监督下增强医疗推理。通过将AI的不完美重新定义为资源，MEDLEY推动了可信医疗AI发展的范式转变，开辟了新的监管、伦理与创新路径。
原文摘要 · Abstract (English)
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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