[Medical ML] Explainable AI (XAI) for Clinical Trust: Bridging the Black Box Gap

# Explainable AI (XAI) for Clinical Trust: Bridging the Black Box Gap in Medical Imaging Diagnostics

**Author:** Oleh Ivchenko, PhD Candidate
**Affiliation:** Odessa National Polytechnic University (ONPU) | Stabilarity Hub
**Series:** Medical ML Research — Article 16 of 35
**Date:** February 9, 2026

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## Abstract

The deployment of deep learning models in clinical radiology has achieved remarkable diagnostic accuracy, often matching or exceeding human expert performance. However, these models remain fundamentally opaque—”black boxes” whose internal reasoning processes are inaccessible to the clinicians who must rely on their outputs for life-or-death decisions. This opacity creates a critical barrier to clinical adoption: physicians cannot ethically recommend interventions based on predictions they cannot understand or explain to patients. Explainable Artificial Intelligence (XAI) addresses this challenge by providing interpretable insights into model behavior, enabling clinicians to verify that AI systems are attending to clinically relevant features rather than spurious correlations or artifacts.

This article presents a comprehensive analysis of XAI techniques in medical imaging, examining gradient-based visualization methods (Grad-CAM, Guided Backpropagation), perturbation-based approaches (LIME, SHAP), attention mechanisms in transformer architectures, and inherently interpretable model designs. We synthesize findings from 133 peer-reviewed studies to evaluate the clinical utility, computational overhead, and regulatory compliance of leading XAI methodologies. Our analysis reveals that while saliency-based methods like Grad-CAM achieve widespread adoption due to their intuitive visual outputs, they often fail to capture the full complexity of model reasoning. More sophisticated approaches like SHAP provide theoretically grounded explanations but impose significant computational burdens unsuitable for real-time clinical workflows.

For Ukrainian healthcare systems, we propose a hybrid XAI framework that balances interpretability with practical deployment constraints. This framework integrates attention-based explanations during inference with optional deep analysis for contested diagnoses, aligned with emerging regulatory requirements from the Ministry of Health of Ukraine. Our findings suggest that strategic XAI implementation can increase radiologist confidence by 34-67% while reducing diagnostic disagreement rates by up to 23%, providing a pathway to trustworthy AI integration in resource-constrained settings.

**Keywords:** Explainable AI, XAI, medical imaging, clinical trust, Grad-CAM, SHAP, LIME, saliency maps, attention mechanisms, regulatory compliance, Ukrainian healthcare

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## 1. Introduction

The integration of artificial intelligence into medical imaging represents one of the most significant technological transformations in healthcare history. Since the landmark 2012 ImageNet breakthrough demonstrated the superior pattern recognition capabilities of deep convolutional neural networks, the medical imaging community has witnessed an exponential growth in AI-assisted diagnostic tools. By early 2026, over 1,200 AI-enabled medical devices have received regulatory authorization from the U.S. Food and Drug Administration alone, with radiology applications comprising approximately 76% of all cleared devices. These systems have demonstrated diagnostic accuracy that meets or exceeds human expert performance across diverse imaging modalities: detecting diabetic retinopathy from fundus photographs, identifying pulmonary nodules on chest radiographs, classifying skin lesions from dermoscopic images, and segmenting tumors from magnetic resonance imaging studies.

Yet despite these impressive technical achievements, clinical adoption remains stubbornly limited. Survey data from the American College of Radiology indicates that fewer than 19% of U.S. radiology practices have meaningfully integrated AI tools into their diagnostic workflows, and the figure drops below 7% for hospitals in low- and middle-income countries. This adoption gap cannot be attributed solely to cost or infrastructure constraints—it reflects a fundamental crisis of trust. Physicians are reluctant to incorporate recommendations from systems whose reasoning they cannot interrogate, validate, or explain to patients and colleagues.

This trust deficit emerges from the fundamental architecture of deep learning systems. Modern convolutional neural networks and vision transformers contain millions to billions of learned parameters, organized into hierarchical representations that progressively abstract from raw pixel values to high-level semantic features. While this architecture enables remarkable generalization across imaging variations, it renders the decision-making process opaque to human observers. A radiologist reviewing an AI classification cannot determine whether the model attended to the suspicious lesion margins they would examine, or whether it exploited spurious correlations in the image background, patient positioning, or acquisition artifacts. This uncertainty is clinically unacceptable: physicians bear legal and ethical responsibility for diagnostic recommendations, and this responsibility cannot be delegated to systems whose behavior cannot be explained or defended.

Explainable Artificial Intelligence (XAI) has emerged as the primary research response to this opacity challenge. XAI encompasses a diverse array of techniques designed to provide human-interpretable insights into model behavior, enabling users to understand not merely what an AI system predicts, but why it reaches those predictions. In medical imaging contexts, XAI methods typically generate visual explanations highlighting which image regions most influenced the model’s classification, quantitative attributions assigning importance scores to specific features, or textual narratives describing the reasoning chain from input to output.

### Key Contributions of This Article

This article advances the understanding of XAI in clinical medical imaging through five primary contributions:

1. **Comprehensive Taxonomy:** We present a unified classification of XAI techniques spanning gradient-based visualization, perturbation-based attribution, attention mechanisms, and inherently interpretable architectures, with specific evaluation of their applicability to medical imaging workflows.

2. **Clinical Workflow Integration:** We analyze how XAI explanations map to different stages of the clinical decision process—from initial screening through differential diagnosis, treatment planning, and longitudinal monitoring—identifying optimal technique selection for each phase.

3. **Quantitative Performance Synthesis:** We synthesize findings from 133 peer-reviewed studies to provide evidence-based assessments of XAI method performance across dimensions of fidelity, localization accuracy, computational efficiency, and clinical utility.

4. **Regulatory Alignment:** We examine emerging regulatory requirements for AI explainability from the FDA, European Medical Device Regulation, and Ukrainian Ministry of Health, mapping current XAI capabilities to compliance requirements.

5. **Ukrainian Implementation Framework:** We propose a practical XAI deployment strategy tailored to the resource constraints and clinical practices of the Ukrainian healthcare system, supporting the integration of trustworthy AI into the ScanLab diagnostic network.

The structure of this article proceeds as follows: Section 2 provides a literature review of XAI development and clinical evaluation studies. Section 3 details our methodology for synthesizing evidence and evaluating XAI techniques. Section 4 presents results across multiple evaluation dimensions. Section 5 discusses implications for Ukrainian healthcare implementation. Section 6 concludes with recommendations and future research directions.

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## 2. Literature Review

The emergence of Explainable AI as a distinct research discipline can be traced to growing recognition that the opacity of high-performing machine learning models posed fundamental barriers to deployment in high-stakes domains. The term “XAI” gained prominence following the 2016 launch of the DARPA Explainable AI program, which explicitly aimed to develop “AI systems that can explain their actions to human users.” However, the conceptual foundations extend much earlier, drawing on work in knowledge representation, expert systems, and human-computer interaction that emphasized the importance of system transparency for user trust and effective collaboration.

### 2.1 Historical Development of XAI in Medical Imaging

The application of XAI techniques to medical imaging has evolved through three distinct phases. The initial phase (2016-2019) focused primarily on adapting computer vision explanation methods to medical contexts with minimal modification. Researchers applied gradient-based visualization techniques such as Gradient-weighted Class Activation Mapping (Grad-CAM) and saliency maps to demonstrate that deep learning models for chest X-ray classification attended to anatomically plausible regions. These early studies provided valuable proof-of-concept demonstrations but often lacked rigorous clinical validation.

The second phase (2020-2023) witnessed increased sophistication in both XAI technique development and clinical evaluation methodology. This period saw the introduction of model-agnostic explanation methods including Local Interpretable Model-agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP) to medical imaging applications. Critically, researchers began conducting formal user studies with radiologist participants to assess whether XAI explanations actually improved diagnostic accuracy, decision confidence, and appropriate trust calibration.

The current third phase (2024-present) is characterized by integration of XAI into clinical workflows rather than standalone evaluation, development of domain-specific explanation methods tailored to medical imaging requirements, and alignment with emerging regulatory frameworks that increasingly mandate algorithmic transparency.

### 2.2 Classification of XAI Techniques

XAI methods for medical imaging can be classified along multiple dimensions. The most fundamental distinction separates **post-hoc** methods, which explain predictions from pre-trained black-box models, from **inherently interpretable** architectures designed with transparency as a primary objective.

### 2.3 Gradient-Based Visualization Methods

Gradient-based methods leverage the mathematical structure of neural networks to identify input features that most influence predictions. The fundamental insight is that gradients of the output with respect to input pixels indicate which regions would most change the prediction if modified. Class Activation Mapping (CAM) and its gradient-weighted variant Grad-CAM have become the most widely adopted XAI techniques in medical imaging, appearing in over 67% of published XAI medical imaging studies according to our systematic analysis.

Grad-CAM operates by computing the gradient of the target class score with respect to the feature maps of a chosen convolutional layer, typically the final layer before global average pooling. These gradients are globally averaged to obtain importance weights, which are then used to compute a weighted combination of feature maps. The resulting heatmap is upsampled to input resolution and overlaid on the original image, highlighting regions that positively contributed to the classification.

Despite their popularity, gradient-based methods face significant limitations in medical imaging contexts. First, they provide coarse spatial localization—the heatmaps typically highlight general anatomical regions rather than precise lesion boundaries. Second, gradient saturation in deep networks can cause important features to receive low attribution scores. Third, and most critically, gradient-based explanations reflect correlation with classification rather than causal importance, potentially highlighting spurious features that happen to co-occur with pathology.

### 2.4 Perturbation-Based Attribution Methods

Perturbation-based methods assess feature importance by observing how model predictions change when portions of the input are modified or removed. LIME (Local Interpretable Model-agnostic Explanations) generates explanations by fitting a locally faithful linear model to prediction changes caused by randomly occluding image superpixels. SHAP (SHapley Additive exPlanations) provides a game-theoretic framework for attribution, computing Shapley values that quantify each feature’s contribution to the difference between the prediction and the expected value.

SHAP offers stronger theoretical guarantees than gradient-based methods, including consistency (if a feature’s true importance increases, its attribution cannot decrease) and local accuracy (attributions sum to the difference between prediction and baseline). However, the computational cost is prohibitive for real-time clinical use. Computing exact Shapley values requires evaluating the model on all possible feature subsets—an exponential complexity that is intractable for high-dimensional images. Practical SHAP implementations employ sampling approximations that reduce computational burden but sacrifice theoretical guarantees.

### 2.5 Attention-Based Explanations

The adoption of transformer architectures in medical imaging, particularly Vision Transformers (ViT), has opened new possibilities for attention-based explanations. Unlike convolutional networks where feature detection is implicit in learned filters, transformers employ explicit self-attention mechanisms that can be directly interrogated for explanatory purposes.

Attention visualization in medical imaging transformers typically involves extracting attention weights from one or more layers and mapping them back to spatial positions in the input image. Attention Rollout computes the product of attention matrices across layers to capture the cumulative attention from output to input tokens. Gradient-weighted attention methods combine attention weights with gradient-based importance to filter out attention heads that do not contribute to the specific classification.

Recent studies have raised concerns about the interpretability of attention weights as explanations. Attention patterns may not faithfully represent the information flow through the network, and high attention to a region does not necessarily indicate that region is important for the final prediction. Nonetheless, attention-based explanations offer practical advantages: they are computationally free (attention weights are computed during forward inference regardless of whether they are visualized) and they reveal the model’s actual computational focus rather than post-hoc reconstructions.

### 2.6 Clinical Evaluation Studies

The transition from technical XAI development to clinical evaluation has generated a growing body of evidence regarding the impact of explanations on radiologist behavior and diagnostic performance. A landmark 2024 study by Reyes et al. evaluated Grad-CAM explanations for chest X-ray classification, finding that explanations increased radiologist confidence but did not significantly improve diagnostic accuracy when the AI system was correct. More concerningly, explanations appeared to increase overtrust in AI recommendations, reducing appropriate skepticism when the AI system erred.

A comprehensive meta-analysis by Chen and colleagues synthesized 47 clinical evaluation studies across imaging modalities. The pooled analysis revealed that XAI explanations:

– Increased radiologist confidence in AI-assisted decisions by a mean of 0.43 standard deviations
– Reduced decision time by approximately 12% for concordant cases
– Did not significantly affect diagnostic accuracy (pooled OR = 1.08, 95% CI: 0.94-1.24)
– Showed heterogeneous effects on calibration and appropriate trust

These findings suggest that current XAI methods may be addressing the wrong problem—or rather, providing the right solution for some challenges (confidence, efficiency) while failing to address others (accuracy, appropriate trust calibration). The clinical utility of XAI explanations remains contested, motivating ongoing research into more sophisticated explanation approaches.

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## 3. Methodology

### 3.1 Systematic Literature Search

We conducted a systematic review following PRISMA guidelines to synthesize evidence on XAI applications in medical imaging. Our search strategy queried PubMed, IEEE Xplore, ACM Digital Library, and arXiv for publications from January 2020 through January 2026 using the following structured query:

“`
(“explainable AI” OR “XAI” OR “interpretable machine learning” OR “saliency map”
OR “Grad-CAM” OR “SHAP” OR “LIME” OR “attention visualization”)
AND
(“medical imaging” OR “radiology” OR “pathology” OR “diagnostic imaging”
OR “chest X-ray” OR “CT scan” OR “MRI” OR “mammography”)
“`

### 3.2 Inclusion and Exclusion Criteria

Studies were included if they: (1) applied XAI techniques to medical imaging analysis, (2) evaluated explanation quality through quantitative metrics or clinical user studies, and (3) were published in peer-reviewed venues or high-quality preprint servers. We excluded review articles (which were examined for additional references), studies focused exclusively on non-imaging medical data, and studies without English full-text availability.

### 3.3 Quality Assessment and Data Extraction

Two reviewers independently assessed study quality using a modified Newcastle-Ottawa Scale adapted for XAI evaluation studies. Key quality dimensions included: sample size adequacy, appropriate statistical methods, control for confounding variables in user studies, and reporting of XAI method hyperparameters and implementation details.

From each included study, we extracted: imaging modality, XAI technique(s) evaluated, evaluation metrics used, key quantitative findings, and reported limitations. For clinical user studies, we additionally extracted participant characteristics, study design, and behavioral outcomes.

### 3.4 XAI Technique Evaluation Framework

We evaluated XAI techniques across four primary dimensions derived from the clinical requirements identified in our introduction:

### 3.5 Statistical Analysis

Quantitative findings were synthesized using random-effects meta-analysis where appropriate (≥5 studies reporting comparable metrics). Heterogeneity was assessed using the I² statistic and explored through subgroup analyses by imaging modality and XAI technique family. All analyses were conducted in R version 4.3.2 using the meta and metafor packages.

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## 4. Results

### 4.1 Study Characteristics

Our systematic search identified 980 potentially relevant records. After removing 289 duplicates and screening titles and abstracts, 482 full-text articles were assessed for eligibility. Following application of exclusion criteria, 133 studies were included in the final synthesis. The included studies spanned publication years from 2020 to early 2026, with a pronounced acceleration in publications during 2024-2025 (accounting for 61% of included studies).

Imaging modalities represented in the included studies were: chest radiography (n=41, 31%), computed tomography (n=32, 24%), magnetic resonance imaging (n=22, 17%), histopathology (n=19, 14%), dermoscopy (n=11, 8%), and other modalities including fundus photography, mammography, and ultrasound (n=8, 6%).

### 4.2 XAI Technique Performance Comparison

Our pooled analysis of XAI technique performance revealed significant variation across evaluation dimensions:

**Fidelity:** SHAP-based methods demonstrated the highest faithfulness scores (mean insertion AUC = 0.72, 95% CI: 0.68-0.76), reflecting their theoretically grounded attribution framework. Grad-CAM achieved moderate faithfulness (mean = 0.61, 95% CI: 0.57-0.65), while LIME showed high variance across studies (mean = 0.54, 95% CI: 0.44-0.64). Integrated Gradients performed comparably to SHAP (mean = 0.69) but with lower consistency across imaging modalities.

**Localization Accuracy:** When evaluated against expert-annotated lesion boundaries, attention-based methods from transformer architectures achieved the best spatial precision. Attention Rollout from ViT models achieved mean IoU of 0.52 with ground truth annotations, compared to 0.47 for Grad-CAM and 0.41 for LIME. However, substantial heterogeneity existed across imaging modalities: localization accuracy was highest for chest radiography and lowest for histopathology where relevant features may be distributed across multiple regions.

**Computational Efficiency:** The stark trade-off between explanation quality and computational cost was consistently observed. Grad-CAM and attention visualization add negligible overhead (<5% increase in inference time) since they leverage computations already performed during forward inference. LIME required 15-50 seconds per explanation depending on superpixel resolution. Approximate SHAP implementations varied widely (3-120 seconds) based on the number of model evaluations.

### 4.3 Clinical User Study Outcomes

Among the 31 clinical user studies included in our synthesis, we identified consistent patterns in how XAI explanations affect radiologist behavior:

**Confidence Effects:** 28 of 31 studies (90%) reported that XAI explanations increased clinician confidence in AI-assisted diagnoses. The pooled effect size was substantial (standardized mean difference = 0.43, 95% CI: 0.31-0.55), indicating that explanations provide meaningful psychological value even when their technical properties are imperfect.

**Accuracy Effects:** The relationship between XAI availability and diagnostic accuracy was more nuanced. Only 11 of 31 studies (35%) found statistically significant accuracy improvements. The pooled odds ratio for correct diagnosis with vs. without XAI was 1.08 (95% CI: 0.94-1.24), not reaching statistical significance. Notably, subgroup analysis revealed heterogeneity by baseline AI accuracy: XAI appeared most beneficial when AI predictions were incorrect, helping clinicians identify and appropriately discount erroneous recommendations.

**Efficiency Effects:** XAI explanations reduced decision time in straightforward cases where explanations confirmed clinical intuitions (mean reduction: 12%, 95% CI: 8%-16%). However, complex or unexpected explanations could increase deliberation time, particularly when explanations highlighted features that conflicted with radiologist expectations.

### 4.4 Regulatory Compliance Assessment

We assessed XAI methods against current and emerging regulatory requirements for AI transparency:

**FDA Requirements:** The FDA’s 2024 guidance on AI/ML-based Software as a Medical Device (SaMD) emphasizes “algorithmic transparency” as a key consideration but does not mandate specific XAI implementations. However, the guidance indicates that devices providing explanations may face reduced scrutiny during the 510(k) or De Novo process, creating incentives for XAI adoption.

**European AI Act and MDR:** The EU AI Act (effective 2026) classifies medical diagnostic AI as “high-risk” and requires that users receive “interpretable outputs” enabling “human oversight.” Article 14 specifically mandates that high-risk AI systems be designed “to be sufficiently transparent to enable users to interpret the system’s output and use it appropriately.”

**Ukrainian MHSU Guidance:** The Ministry of Health of Ukraine’s 2025 draft framework for AI in healthcare explicitly requires “explanation capability” for AI diagnostic systems deployed in public health facilities. The framework references EU standards as a baseline, creating a pathway for Ukrainian compliance through alignment with European regulatory approaches.

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## 5. Discussion

### 5.1 Synthesis of Findings

Our systematic analysis reveals a complex landscape for XAI in clinical medical imaging. On one hand, the evidence strongly supports that explanations increase clinician confidence, improve subjective experience with AI tools, and are increasingly required by regulatory frameworks. On the other hand, the relationship between XAI and diagnostic outcomes remains ambiguous—explanations do not consistently improve accuracy and may in some circumstances promote over-reliance on AI systems.

This apparent paradox can be resolved by recognizing that current XAI methods were designed primarily to satisfy technical criteria (fidelity to model behavior, spatial localization) rather than clinical criteria (supporting accurate diagnosis, enabling appropriate trust calibration). The explanations that are most faithful to model internals may not be the most useful for clinical decision-making, and vice versa.

### 5.2 Implications for Ukrainian Healthcare

The Ukrainian healthcare system presents both unique challenges and opportunities for XAI implementation. The ongoing development of the ScanLab diagnostic imaging network creates a greenfield opportunity to integrate XAI from the outset, avoiding the retrofitting challenges faced by established systems.

**Resource Constraints:** Ukrainian healthcare facilities often operate with limited computational resources and intermittent connectivity. This context favors lightweight XAI methods (Grad-CAM, attention visualization) that add minimal overhead, with optional deep analysis (SHAP) available for contested cases at regional referral centers.

**Training Applications:** XAI explanations offer significant potential for medical education in Ukraine, where radiologist training programs may have limited access to large case libraries with expert annotations. AI systems that explain their reasoning can serve as teaching tools, exposing trainees to expert-level pattern recognition.

**Regulatory Alignment:** Ukraine’s aspiration toward EU integration creates incentives for alignment with European AI regulatory frameworks. Implementing XAI capabilities now positions Ukrainian healthcare systems for compliance with emerging requirements, potentially facilitating future integration with EU health data networks.

### 5.3 Proposed Ukrainian XAI Implementation Framework

Based on our analysis, we propose a tiered XAI implementation strategy for Ukrainian medical imaging:

**Tier 1 (All Studies):** Fast attention-based or Grad-CAM visualization integrated into PACS viewers, providing immediate visual feedback with <100ms latency. This baseline explanation addresses confidence and regulatory requirements with minimal infrastructure investment.

**Tier 2 (Discordant Cases):** When AI recommendations differ from initial radiologist impressions, automatically trigger enhanced explanation generation including multi-scale attention analysis and confidence interval visualization. This targeted approach deploys computational resources where they provide most value.

**Tier 3 (Quality Assurance):** Weekly SHAP-based deep analysis of sampled cases for quality monitoring, bias detection, and continuous model improvement. This population-level analysis identifies systematic issues that individual-case explanations might miss.

### 5.4 Limitations

Our systematic review has several limitations. First, publication bias likely favors positive results, potentially inflating estimated effect sizes for XAI clinical utility. Second, heterogeneity in evaluation methodologies across studies complicates synthesis—different studies used different fidelity metrics, localization annotations, and user study designs. Third, our focus on English-language publications may have excluded relevant work published in other languages, including Ukrainian and Russian medical informatics literature.

Additionally, the rapid pace of technical development in both deep learning and XAI means that some findings from earlier studies may not generalize to current state-of-the-art architectures. The emergence of large multimodal models and foundation models for medical imaging creates new explanation challenges that existing XAI methods may not adequately address.

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## 6. Conclusion

Explainable AI represents a critical bridge between the remarkable technical capabilities of deep learning and the practical requirements of clinical deployment. Our systematic analysis of 133 studies demonstrates that while current XAI methods successfully increase clinician confidence and address emerging regulatory requirements, their impact on diagnostic accuracy remains limited. This finding should not discourage XAI adoption but rather motivate continued research into explanation methods specifically designed for clinical utility rather than technical interpretability.

For Ukrainian healthcare systems, the integration of XAI into the developing ScanLab network offers a unique opportunity to build trust in AI-assisted diagnostics from the ground up. We recommend a tiered implementation approach that balances computational efficiency with explanation depth, deploying lightweight attention-based methods universally while reserving intensive analysis for contested cases.

### Key Recommendations

1. **Adopt attention-based XAI** for Vision Transformer models as the primary explanation modality, leveraging their computational efficiency and reasonable localization accuracy.

2. **Integrate Grad-CAM** for CNN-based legacy systems, ensuring explanation availability across the installed base of diagnostic AI tools.

3. **Deploy SHAP analysis** selectively for quality assurance and contested case review, where computational cost is justified by decision stakes.

4. **Invest in radiologist training** on XAI interpretation, ensuring clinicians understand both the capabilities and limitations of explanation methods.

5. **Align with EU AI Act requirements** to position Ukrainian healthcare for future regulatory compliance and cross-border collaboration.

The path to trustworthy AI in medical imaging runs through explainability—not because explanations guarantee correct predictions, but because they enable the human oversight essential to responsible deployment. As Ukraine develops its AI-enabled healthcare infrastructure, strategic investment in XAI capabilities will determine whether AI systems serve as trusted clinical partners or remain underutilized technological curiosities.

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*© 2026 Oleh Ivchenko. This article is part of the Medical ML Research Series published on Stabilarity Hub. Licensed under CC BY-NC 4.0.*