This study employs counterfactual explanations to explore "what if?" scenarios in medical research, with the aim of expanding our understanding beyond existing boundaries. Specifically, we focus on utilizing MRI features for diagnosing pediatric posterior fossa brain tumors as a case study. The field of artificial intelligence and explainability has witnessed a growing number of studies and increasing scholarly interest. However, the lack of human-friendly interpretations in explaining the outcomes of machine learning algorithms has significantly hindered the acceptance of these methods by clinicians in their clinical practice. To address this, our approach incorporates counterfactual explanations, providing a novel way to examine alternative decision-making scenarios. These explanations offer personalized and context-specific insights, enabling the validation of predictions and clarification of variations under diverse circumstances. Importantly, our approach maintains both statistical and clinical fidelity, allowing for the examination of distinct tumor features through alternative realities. Additionally, we explore the potential use of counterfactuals for data augmentation and evaluate their feasibility as an alternative approach in medical research. The results demonstrate the promising potential of counterfactual explanations to enhance trust and acceptance of AI-driven methods in clinical settings.

翻译：本研究采用反事实解释探索医学研究中的“如果……会怎样？”场景，旨在将认知拓展至现有边界之外。具体而言，我们以利用MRI特征诊断儿童后颅窝脑肿瘤为案例研究。人工智能与可解释性领域的研究数量与学术兴趣持续增长，然而机器学习算法输出结果缺乏人类友好的解释方式，严重阻碍了临床医生在医疗实践中接受这些方法。为此，我们的方法引入反事实解释，提供了一种审视替代性决策场景的新途径。这些解释能提供个性化且情境化的洞见，使得在不同条件下验证预测结果、阐明变异情况成为可能。更重要的是，我们的方法同时保持统计忠实性与临床忠实性，能够通过替代现实场景检验不同肿瘤特征。此外，我们探讨了反事实在数据增强方面的潜在用途，并评估其作为医学研究替代方法的可行性。研究结果表明，反事实解释在增强临床环境下AI驱动方法的信任度与接受度方面具有显著潜力。