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Explainable Artificial Intelligence for Medical Applications: A Review

T0 review · 3 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read This review claims that medical AI can be made accountable with a single four-criteria taxonomy grid spanning 19 explanation techniques, and that current practice skews toward visual, post-hoc, model-specific methods.

desk verdict Useful survey with a solid taxonomy, but the 'over 100 papers' claim doesn't match the paper's own tables — a fixable overstatement, not a fatal flaw. read the letter →

arxiv 2412.01829 v1 pith:PGP3ZYAU submitted 2024-11-15 cs.LG cs.CV

Qiyang Sun , Alican Akman , Björn W. Schuller This is my paper · ORCID
classification cs.LGcs.CV
keywords ExplainableArtificialIntelligence(XAI)MachineLearningLiteratureReviewMedicalInformationSystemimagingaudioXAImultimodalpost-hocexplanation
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This review argues that medical AI's accountability gap can be addressed by a structured map of explanation techniques, and that the field currently over-relies on visual, post-hoc, model-specific methods. It consolidates four existing taxonomy criteria into one framework covering 19 XAI techniques. It then uses that framework to organize over 100 recent medical XAI studies across vision, audio, and multimodal data, drawing out trends and limitations. The value of the work is giving future researchers a shared vocabulary and grid for comparing explanation methods in clinical settings.

What carries the argument

The central organizing device is a four-criteria taxonomy grid, adapted from prior frameworks: perceptive interpretability versus interpretability by mathematical structures, ante-hoc versus post-hoc, model-agnostic versus model-specific, and local versus global. The paper maps 19 XAI techniques onto this grid and groups them by implementation principle into perturbation-based, backpropagation-based, gradient-based, instance-based, and other approaches. This grid carries the review's arguments by letting the authors compare disparate medical studies on the same terms and extract trends about what the field is and is not doing.

What would settle it

A formal systematic review of medical XAI published from 2018 to 2024 with a pre-registered protocol that found audio and multimodal explanation studies about as numerous as visual ones, or that found most techniques ante-hoc rather than post-hoc, would directly contradict the trends the paper reports.

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Extended reading notes

Core claim

On its own terms, the paper's discovery is that the scattered XAI literature for medicine can be unified: the four criteria (perceptive versus mathematical, ante-hoc versus post-hoc, model-agnostic versus model-specific, local versus global) are jointly enough to describe 19 commonly used techniques such as LIME, SHAP, Grad-CAM, LRP, and counterfactuals. Classifying those techniques by implementation principle into perturbation-based, backpropagation-based, gradient-based, instance-based, and other approaches reveals that most medical deployments are perceptive, post-hoc, and model-specific. Applied to over 100 studies from 2018 to 2024, the framework shows that visual explanations dominate, audio and multimodal XAI are underdeveloped, and no single technique covers all four criteria.

Load-bearing premise

The paper's trend claims rest on the assumption that its chosen studies, picked for anatomical coverage, technique variety, and recency rather than through a formal systematic protocol, represent the whole body of medical XAI work.

Editorial extensions

If this is right

  • Researchers entering medical XAI can use the 19-technique grid as a checklist, making it easier to see which explanatory perspective a new method covers and which it leaves out.
  • The observed dominance of visual, post-hoc, gradient-based explanations implies that audio and multimodal explanations are an open, comparatively empty niche for new work.
  • The finding that explanations are rarely combined suggests that multi-technique and multi-criteria evaluation, rather than any single saliency map, is the route to clinically usable explanations.
  • The paper's outlook implies that standardizing terminology and adopting quantitative evaluation frameworks would make medical XAI comparisons more meaningful.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The same four-criteria grid could be applied outside medicine to high-stakes domains such as credit or criminal justice, where the local/global distinction maps onto individual decisions versus policy-level audits; the authors do not draw this comparison.
  • A testable extension of the paper's scarcity claim is that audio XAI would grow faster if explainability shifted from pixel-level heatmaps to time-frequency segmentations and listenable sonified explanations, a direction the authors mention only as outlook.
  • The paper's own selection logic could be stress-tested by a formal systematic review with pre-registered inclusion criteria; the authors explicitly state that their choices were based on anatomical location, technique diversity, and publication timeframe rather than a systematic protocol.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. This paper is a narrative review of explainable artificial intelligence (XAI) in medical applications. It consolidates four existing taxonomy criteria (perception/mathematics, ante-hoc/post-hoc, model-agnostic/model-specific, local/global) into a framework of 19 XAI techniques, assigns those techniques to the criteria in Table 1, and organizes recent medical XAI studies into three application domains: visual (Table 2), audio (Table 3), and multimodal (Table 4). The authors state in Section 1 that they analyze over 100 papers from the past five years, and in Section 7 they claim to have gathered a 'multitude' of application cases spanning visual, audio, and multimodal domains. The paper also discusses challenges and future directions, including standardization, quantitative evaluation, formal XAI, and multimodal/personalized explanations.

Significance. If the central claims were fully supported, the consolidated taxonomy and cross-modality synthesis would be a useful entry point for researchers and medical practitioners navigating the XAI literature. The paper's Table 1 and Figure 3 provide a compact comparison of 19 XAI techniques, and the division into visual, audio, and multimodal applications is a sensible organizing principle. However, the review's quantitative claim of analyzing over 100 papers is contradicted by its own tables, which enumerate 87 unique studies, and the selection of studies is explicitly subjective as described in Section 4. These issues directly affect the claimed comprehensiveness and the generality of the empirical trends reported in Section 5. The taxonomy itself is a reasonable contribution, but the review's scope claims and the evidentiary support for its observations need to be reconciled with the presented data.

major comments (3)
  1. [§1, Introduction] Section 1 states 'we analyse over 100 papers published in the past five years', but Tables 2, 3, and 4 enumerate 71, 5, and 11 unique studies, respectively, totalling 87; no additional analyzed studies appear outside these tables, and the background citations in §4.2 (e.g., [1], [42], [6], [35], [67], [100], [118], [153], [160]) are not XAI application papers. The quantitative claim is therefore unsupported by the evidence presented, and the authors should either expand the included studies to exceed 100 or revise the claim to match the actual count.
  2. [§4, XAI Applications in Medicine Review] In Section 4 the authors write that they 'selected the representative XAI medical applications based on medical anatomical locations, diversity of XAI techniques, and publication timeframe' and that this selection rests on 'our subjective assessment'. No inclusion/exclusion criteria, search results, or screening decisions are reported, so the study set is not reproducible. Because the cross-domain trends in §5.2 (e.g., that audio and multimodal XAI are 'less abundant') are derived from this selected sample rather than from a systematic census, the observed imbalances may be artifacts of the selection rather than robust properties of the literature. The authors should either supply a reproducible selection protocol with a defined inclusion count or moderate the generality of the empirical observations.
  3. [§5.1, Observation on Criteria Taxonomy and Techniques] Section 5.1 makes unquantified frequency claims such as 'the majority of XAI techniques used in the medical field are perception-based' and 'The majority of XAI techniques currently used in medical AI applications are model-specific' without providing an aggregation of the per-study technique entries in Tables 2-4. Table 1 is a taxonomy of techniques rather than an application survey and cannot by itself substantiate these frequency statements. The authors should add explicit counts or a summary table cross-tabulating the reviewed studies by the four taxonomy criteria, or replace these claims with hedged observations that reflect the small, subjectively selected sample.
minor comments (5)
  1. [§1, Introduction] The phrase 'past five years' is inconsistent with the search window '2018 and 2024' described in Section 4, which spans six calendar years; please align the wording.
  2. [Multiple sections] Several typographical errors need correction: 'according approaches' (§1), 'explainablility' (§2.2), 'we are not reiterate' (§3.1), 'a implementation-based approaches' (§7), 'Data Sacarcity' (Table 4), and 'In 2022, 10% of patients assumed they did not feel involved' (§2.1).
  3. [Table 1] In Table 1, multiple techniques receive checks in both members of a binary criterion (e.g., CAM is checked for both Perception and Mathematics, and SM for both Local and Global); because §3.3 introduces each pair as an alternative, please add a note explaining that the criteria are treated as multi-label axes.
  4. [Tables 2-4] The 'Cons' column in Tables 2-4 is not defined; please spell it out in the table footnotes as, for example, 'shortcomings identified by the authors from the original paper's limitations section and their own assessment'.
  5. [§4, Selection methodology] The Boolean search string in §4 ('XAI' OR 'explainable artificial intelligence' AND 'healthcare' OR 'medicine') is missing parentheses and therefore has an ambiguous parse; adding explicit grouping would clarify the intended query.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: this is a narrative review that synthesizes external literature; the 'over 100 papers' count mismatch is a scope inconsistency, not a circular derivation.

full rationale

The paper is a review, not a derivation. Its taxonomy is assembled from four independently published frameworks ([183], [70], [34], and [106]), and the 19-technique table is an annotated synthesis; Section 3.4 explicitly says 'The contents of table are derived from previous research summaries and our subjective understanding,' so the classification is presented as interpretation rather than as a derived prediction. The application survey (Tables 2–4) summarizes external studies, and no parameter is fitted nor is any quantity 'predicted' from a fitted input. The authors' self-citations ([6], [18], [35], [151], [179], [200]) are used as example applications or background context, not as load-bearing justification for the taxonomy or for the survey's conclusions. The one substantive weakness—the Introduction and Section 7 claim 'over 100 papers' while Tables 2–4 contain roughly 87 unique entries—is a scope/consistency problem, not a circular derivation: the review does not define its conclusions into existence by assuming its own output. No equation is reused by construction, no fitted value is renamed as a prediction, and no uniqueness or ansatz is imported from the authors' prior work. Therefore no circular step is present.

Assumptions & free parameters 0 free parameters · 0 assumptions · 0 invented entities

This paper is a literature review. It introduces no free parameters, no postulates, and no new entities. All definitions and taxonomies are attributed to prior literature.

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Cite this review

Pith. "Pith review of Explainable Artificial Intelligence for Medical Applications: A Review." pith.science (2026). https://pith.science/paper/PGP3ZYAU

@misc{pith2026241201829,
  author       = {Pith},
  title        = {Pith review of: Explainable Artificial Intelligence for Medical Applications: A Review},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PGP3ZYAU}},
  note         = {Machine review of arXiv:2412.01829}
}
read the original abstract

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.

Figures

Figures reproduced from arXiv: 2412.01829 by the authors.

Figure 1
Figure 1. Overview of XAI-related publications retrieved from Google Scholar. Data collected on December 26, 2023, using the [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. The interrelationship among XAI-related terms [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Demonstration of some XAI techniques. Original images are from ISIC2016 Challenge skin lesion datasets [ [PITH_FULL_IMAGE:figures/full_fig_p013_3.png] view at source ↗

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Forward citations

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Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.