With the ongoing development of deep learning, an increasing number of AI
models have surpassed the performance levels of human clinical practitioners.
However, the prevalence of AI diagnostic products in actual clinical practice
remains significantly lower than desired. One crucial reason for this gap is
the so-called `black box' nature of AI models. Clinicians' distrust of black
box models has directly hindered the clinical deployment of AI products. To
address this challenge, we propose ContrastDiagnosis, a straightforward yet
effective interpretable diagnosis framework. This framework is designed to
introduce inherent transparency and provide extensive post-hoc explainability
for deep learning model, making them more suitable for clinical medical
diagnosis. ContrastDiagnosis incorporates a contrastive learning mechanism to
provide a case-based reasoning diagnostic rationale, enhancing the model's
transparency and also offers post-hoc interpretability by highlighting similar
areas. High diagnostic accuracy was achieved with AUC of 0.977 while maintain a
high transparency and explainability.

通过引入透明度和后置解释性来增强深度学习模型的临床医学诊断适用性，提出了对比诊断，一个简单而有效的可解释诊断框架，实现了高可靠性的诊断并保持了高透明度和解释性。

对比诊断：使用对比学习提高肺结节诊断的可解释性

ContrastDiagnosis: Enhancing Interpretability in Lung Nodule Diagnosis  Using Contrastive Learning

The past decade has observed a great advancement in AI with deep
learning-based models being deployed in diverse scenarios including
safety-critical applications. As these AI systems become deeply embedded in our
societal infrastructure, the repercussions of their decisions and actions have
significant consequences, making the ethical implications of AI deployment
highly relevant and important. The ethical concerns associated with AI are
multifaceted, including challenging issues of fairness, privacy and data
protection, responsibility and accountability, safety and robustness,
transparency and explainability, and environmental impact. These principles
together form the foundations of ethical AI considerations that concern every
stakeholder in the AI system lifecycle. In light of the present ethical and
future x-risk concerns, governments have shown increasing interest in
establishing guidelines for the ethical deployment of AI. This work unifies the
current and future ethical concerns of deploying AI into society. While we
acknowledge and appreciate the technical surveys for each of the ethical
principles concerned, in this paper, we aim to provide a comprehensive overview
that not only addresses each principle from a technical point of view but also
discusses them from a social perspective.

这项研究综合了当前和未来的人工智能应用所面临的伦理问题，从技术和社会角度对 AI 部署中的公平性、隐私和数据保护、透明度与可解释性等伦理原则进行了综述。