解码黑箱：用于癌症诊断、预后和治疗规划的可解释人工智能（XAI）--最新系统综述。

IF 3.7 2区医学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

International Journal of Medical Informatics Pub Date : 2024-11-04 DOI:10.1016/j.ijmedinf.2024.105689

Yusuf Abas Mohamed , Bee Ee Khoo , Mohd Shahrimie Mohd Asaari , Mohd Ezane Aziz , Fattah Rahiman Ghazali

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引用次数: 0

摘要

目的：可解释人工智能（XAI）越来越被认为是癌症治疗的重要工具，在提高诊断、预后和治疗计划方面具有巨大潜力。然而，在癌症治疗的各个阶段对 XAI 进行整体整合的研究仍然不足。本综述通过系统评估 XAI 在这些关键领域的作用，确定关键挑战和新兴趋势，弥补了这一空白：按照 PRISMA 指南，在 Scopus 和 Web of Science 上进行了全面的文献检索，重点是 2020 年 1 月至 2024 年 5 月期间的出版物。经过严格筛选和质量评估后，选出 69 项研究进行深入分析：综述发现了 XAI 在癌症治疗中应用的关键差距，特别是 83% 的研究排除了临床医生，这引起了人们对现实世界适用性的担忧，可能会导致技术上合理但临床上不相关的解释。此外，87% 的研究缺乏对 XAI 解释的严格评估，从而影响了其在临床实践中的可靠性。SHAP、LIME 和 Grad-CAM 等事后视觉方法的主导地位反映了一种趋势，即由于特定的输入扰动和简化假设，解释可能存在固有缺陷。缺乏正式的评估指标和标准化限制了 XAI 在临床环境中的广泛应用，造成了人工智能开发与临床整合之间的脱节。此外，由于缺乏将这些工具整合到临床工作流程中的明确指南，因此将 XAI 见解转化为可操作的临床决策仍具有挑战性：本综述强调了加强临床医生参与、标准化 XAI 评估指标、以临床医生为中心的界面、情境感知 XAI 系统以及将 XAI 整合到临床工作流程中的框架的必要性，以便在癌症护理中做出明智的临床决策并改善疗效。

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Decoding the black box: Explainable AI (XAI) for cancer diagnosis, prognosis, and treatment planning-A state-of-the art systematic review

Objective

Explainable Artificial Intelligence (XAI) is increasingly recognized as a crucial tool in cancer care, with significant potential to enhance diagnosis, prognosis, and treatment planning. However, the holistic integration of XAI across all stages of cancer care remains underexplored. This review addresses this gap by systematically evaluating the role of XAI in these critical areas, identifying key challenges and emerging trends.

Materials and methods

Following the PRISMA guidelines, a comprehensive literature search was conducted across Scopus and Web of Science, focusing on publications from January 2020 to May 2024. After rigorous screening and quality assessment, 69 studies were selected for in-depth analysis.

Results

The review identified critical gaps in the application of XAI within cancer care, notably the exclusion of clinicians in 83% of studies, which raises concerns about real-world applicability and may lead to explanations that are technically sound but clinically irrelevant. Additionally, 87% of studies lacked rigorous evaluation of XAI explanations, compromising their reliability in clinical practice. The dominance of post-hoc visual methods like SHAP, LIME and Grad-CAM reflects a trend toward explanations that may be inherently flawed due to specific input perturbations and simplifying assumptions. The lack of formal evaluation metrics and standardization constrains broader XAI adoption in clinical settings, creating a disconnect between AI development and clinical integration. Moreover, translating XAI insights into actionable clinical decisions remains challenging due to the absence of clear guidelines for integrating these tools into clinical workflows.

Conclusion

This review highlights the need for greater clinician involvement, standardized XAI evaluation metrics, clinician-centric interfaces, context-aware XAI systems, and frameworks for integrating XAI into clinical workflows for informed clinical decision-making and improved outcomes in cancer care.

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来源期刊

International Journal of Medical Informatics 医学-计算机：信息系统

CiteScore

8.90

自引率

4.10%

发文量

217

审稿时长

42 days

期刊介绍： International Journal of Medical Informatics provides an international medium for dissemination of original results and interpretative reviews concerning the field of medical informatics. The Journal emphasizes the evaluation of systems in healthcare settings. The scope of journal covers: Information systems, including national or international registration systems, hospital information systems, departmental and/or physician''s office systems, document handling systems, electronic medical record systems, standardization, systems integration etc.; Computer-aided medical decision support systems using heuristic, algorithmic and/or statistical methods as exemplified in decision theory, protocol development, artificial intelligence, etc. Educational computer based programs pertaining to medical informatics or medicine in general; Organizational, economic, social, clinical impact, ethical and cost-benefit aspects of IT applications in health care.