用真实脑肿瘤数据验证合成数据如何影响临床信任
Position Paper: Building Trust in Synthetic Data for Clinical AI
- 通过脑肿瘤分割实验测试合成数据质量对信任的影响
- 合成数据的质量、多样性与占比显著影响临床可信度
- 为医疗AI部署提供可信赖的合成数据实践指引
深度生成模型和合成医疗数据在解决医疗领域隐私、偏见及真实数据稀缺等关键问题上展现出巨大潜力。尽管该领域研究迅速发展且理论前景广阔，但其在临床场景中的实际应用仍受限。由于对合成数据可靠性与可信度的疑虑，临床医生普遍缺乏信任。本文主张建立对合成医疗数据的信任是推动其临床应用的关键。基于脑肿瘤分割的实证研究，我们发现合成数据的质量、多样性和比例直接影响临床人工智能模型的可信度。研究结果为提升合成数据驱动AI系统在真实临床流程中的部署与接受度提供了重要参考。
原文摘要 · Abstract (English)
Deep generative models and synthetic medical data have shown significant promise in addressing key challenges in healthcare, such as privacy concerns, data bias, and the scarcity of realistic datasets. While research in this area has grown rapidly and demonstrated substantial theoretical potential, its practical adoption in clinical settings remains limited. Despite the benefits synthetic data offers, questions surrounding its reliability and credibility persist, leading to a lack of trust among clinicians. This position paper argues that fostering trust in synthetic medical data is crucial for its clinical adoption. It aims to spark a discussion on the viability of synthetic medical data in clinical practice, particularly in the context of current advancements in AI. We present empirical evidence from brain tumor segmentation to demonstrate that the quality, diversity, and proportion of synthetic data directly impact trust in clinical AI models. Our findings provide insights to improve the deployment and acceptance of synthetic data-driven AI systems in real-world clinical workflows.
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