mlsanityData to 7 Oct 2026

Explainable Artificial Intelligence for Medical Applications: A Review

arXiv 2412.01829 ↗First posted 15 Nov 2024cs.LG cs.CVcited by 12

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Abstract (arXiv)

The continuous development of artificial intelligence (AI) theory has propelled this field to unprecedented heights, owing to the relentless efforts of scholars and researchers. In the medical realm, AI takes a pivotal role, leveraging robust machine learning (ML) algorithms. AI technology in medical imaging aids physicians in X-ray, computed tomography (CT) scans, and magnetic resonance imaging (MRI) diagnoses, conducts pattern recognition and disease prediction based on acoustic data, delivers prognoses on disease types and developmental trends for patients, and employs intelligent health management wearable devices with human-computer interaction technology to name but a few. While these well-established applications have significantly assisted in medical field diagnoses, clinical decision-making, and management, collaboration between the medical and AI sectors faces an urgent challenge: How to substantiate the reliability of decision-making? The underlying issue stems from the conflict between the demand for accountability and result transparency in medical scenarios and the black-box model traits of AI. This article reviews recent research grounded in explainable artificial intelligence (XAI), with an emphasis on medical practices within the visual, audio, and multimodal perspectives. We endeavour to categorise and synthesise these practices, aiming to provide support and guidance for future researchers and healthcare professionals.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 53 of its 202 references that are in the map. First posted on arXiv 15 Nov 2024.

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53 of 202 references.

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38 more
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50 most cited of 53 All 53 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 5; 2026: 6.

12 papers.

  1. 2025Fleming-R1: Toward Expert-Level Medical Reasoning via Reinforcement Learningcited by 9
  2. 2024Towards Friendly AI: A Comprehensive Review and New Perspectives on Human-AI Alignmentcited by 2
  3. 2025Pulmonary Tuberculosis Edge Diagnosis System Based on MindSpore Framework: Low-cost and High-precision Implementation with Ascend 310 Chipcited by 0
  4. 2025On the notion of missingness for path attribution explainability methods in medical settings: Guiding the selection of medically meaningful baselinescited by 0
  5. 2025Rethinking Explainable Disease Prediction: Synergizing Accuracy and Reliability via Reflective Cognitive Architecturecited by 0
  6. 2025RAxSS: Retrieval-Augmented Sparse Sampling for Explainable Variable-Length Medical Time Series Classificationcited by 0
  7. 2026Exploring Trust Calibration in XAI - The Impact of Exposing Model Limitations to Lay Userscited by 0
  8. 2026XAI-Grounded Explanation Generation for Speech Deepfake Detection with Training-Free Multimodal Large Language Modelscited by 0
  9. 2026KANEx: Translating Kolmogorov-Arnold Networks' Interpretability to Medical Explainabilitycited by 0
  10. 2026Crushing the Evidence: A Dual-Penalty Evasion Framework for Fooling White-Box Explainable AI Auditorscited by 0
  11. 2026Who Are You Explaining To? A Multi-Agent System for Audience-Aware XAI Narrativescited by 0
  12. 2026A New Transformer-Based Approach for Audio-Based Kinship Verification and a New Uncontrolled Mandarin Kinship Speech Datasetcited by 0