Table 1_Explainable AI in healthcare: a systematic review of XAI use cases in imaging, diagnostics, and rehabilitation.docx

BackgroundExplainable artificial intelligence (XAI) is used in healthcare to make machine-learning outputs more transparent and clinically usable. This is important because many machine learning models work like a “black box” which can hide bias, reduce trust in the model. XAI addresses this problem by showing which features or image regions influenced a result, either for one patient or across a dataset.
ObjectivesOur objective is to provide a clear, systematic review of how XAI is being used in healthcare. We summarize the main XAI methods, the data and models they are paired with, and how these explanations support clinical understanding across imaging, diagnosis, and rehabilitation.
MethodsWe performed a systematic review with narrative synthesis (2020–2025) of 36 empirical studies across three verticals–Imaging (n = 10), Diagnosis (n = 16), and Rehabilitation (n = 10) that are identified via PubMed/MEDLINE, IEEE Xplore, and Google Scholar, following PRISMA 2020 guidelines. We included research studies that employed XAI in the three mentioned verticals. We excluded review articles and viewpoint studies. Screening numbers were - records identified 1,481; duplicates removed 647; other removals 187; screened 647; excluded 532; reports sought 115; not retrieved 31; assessed 84; full-text excluded 48; included 36. From each study we extracted ML models, XAI methods, study design, methodologies, and dataset/source. Meta-analysis was not undertaken due to heterogeneity.
ResultsAcross 36 studies, SHAP was used in 21 studies, Grad-CAM in ~12/36, and LIME in ~11/36. A clear method-modality fit emerged with Imaging predominantly using saliency/heat-map methods, especially Grad-CAM, for spatial evidence. Diagnosis and Rehabilitation were dominated by feature-attribution tools like SHAP and LIME for global and case-level explanations. Many papers combined ≥ 2 explainers to cross-check interpretations namely SHAP+LIME, and Grad-CAM + LIME.
ConclusionRecent healthcare XAI demonstrates consistent method-modality fit and frequently combine two or more methods, helping translate opaque predictions into clinician-oriented reasoning. To enable trustworthy deployment, future work should pair these practices with standardized XAI reporting, faithfulness/stability assessments, and external, cross-site validation.

背景：可解释人工智能（Explainable Artificial Intelligence, XAI）在医疗领域中被用于提升机器学习输出的透明度，使其更适配临床使用场景。这一点至关重要，因为诸多机器学习模型如同"黑箱"一般，可能暗藏偏差，降低业界对模型的信任度。XAI通过展示影响某一结果的特征或图像区域（针对单个患者或整个数据集）来解决这一问题。
研究目标：本研究旨在系统且清晰地梳理可解释人工智能在医疗领域的应用现状。我们将总结主流的XAI方法、与之搭配使用的数据与模型，以及这些解释手段如何在医学影像、诊断与康复领域助力临床认知。
研究方法：本研究遵循PRISMA 2020指南，对PubMed/MEDLINE、IEEE Xplore及Google Scholar数据库中2020至2025年间的36项实证研究开展了带有叙述性综合分析的系统综述，这些研究涵盖三大应用方向：医学影像（n=10）、诊断（n=16）与康复（n=10）。研究纳入了上述三大方向中应用XAI的科研论文，排除综述类文章与观点性研究。文献筛选流程如下：初检检出文献1481篇；剔除重复文献647篇；其他类型剔除187篇；实际筛查文献647篇；排除532篇；获取115篇报告；未获取到31篇；评估84篇；排除全文48篇；最终纳入36篇。本研究从每篇纳入文献中提取了机器学习模型、XAI方法、研究设计、研究方法以及数据集/数据来源等信息。由于研究存在显著异质性，未开展元分析。
研究结果：在36项研究中，SHAP被应用于21项研究，Grad-CAM约应用于12/36项研究，LIME约应用于11/36项研究。研究呈现出清晰的方法-模态适配性：医学影像领域主要使用显著性/热图类方法，尤其是Grad-CAM，以获取空间层面的证据；诊断与康复领域则以特征归因类工具为主，如SHAP与LIME，用于全局层面与个案层面的解释。诸多文献结合了≥2种解释器以交叉验证解读结果，例如SHAP+LIME以及Grad-CAM+LIME。
结论：当前医疗领域的可解释人工智能研究呈现出稳定的方法-模态适配性，且常结合两种及以上的方法，助力将不透明的模型预测转化为面向临床医师的推理逻辑。为实现可信赖的落地应用，未来研究应将这些实践与标准化的XAI报告规范、忠实性/稳定性评估以及跨站点外部验证相结合。