The field of explainability in artificial intelligence (AI) has witnessed a growing number of studies and increasing scholarly interest. However, the lack of human-friendly and individual interpretations in explaining the outcomes of machine learning algorithms has significantly hindered the acceptance of these methods by clinicians in their research and clinical practice. To address this, our study employs counterfactual explanations to explore "what if?" scenarios in medical research, aiming to expand our understanding beyond existing boundaries on magnetic resonance imaging (MRI) features for diagnosing pediatric posterior fossa brain tumors. In our case study, the proposed concept provides a novel way to examine alternative decision-making scenarios that offer personalized and context-specific insights, enabling the validation of predictions and clarification of variations under diverse circumstances. Additionally, we explore the potential use of counterfactuals for data augmentation and evaluate their feasibility as an alternative approach in our medical research case. The results demonstrate the promising potential of using counterfactual explanations to enhance trust and acceptance of AI-driven methods in clinical research.

翻译：人工智能（AI）可解释性领域的研究数量与学术关注度持续增长。然而，机器学习算法结果解释缺乏人性化与个性化，严重制约了临床医生在科研与临床实践中对这些方法的接受程度。为应对这一挑战，本研究采用反事实解释探索医学研究中的"假设"场景，旨在超越现有儿童后颅窝脑肿瘤磁共振成像（MRI）特征诊断认知边界。通过案例分析，所提出的概念提供了一种审视替代性决策场景的新途径，可生成个性化且情境化的见解，从而在不同条件下验证预测结果并阐明变量差异。此外，我们进一步探索了反事实解释在数据增强方面的应用潜力，并评估其作为医学研究案例中替代方法的可行性。结果表明，反事实解释有望增强临床研究中对AI驱动方法的信任度与接受度。