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Abstract #

Medical artificial intelligence has long suffered from a critical epistemic gap: models produce predictions without producing justifications. Clinicians, regulators, and patients cannot evaluate the validity of a decision if they can only see its output. ScanLab addresses this gap through a deliberate architectural choice — making explainability a mandatory, non-negotiable layer of the inference pipeline rather than an optional downstream component. ScanLab is an end-to-end local system for medical image diagnostics that unifies model training from image folders, a multi-model registry, REST-based inference, and real-time visual explanation via Gradient-weighted Class Activation Mapping (Grad-CAM). It operates entirely on consumer hardware without requiring cloud infrastructure or dedicated MLOps tooling. The mobile-first workflow enables a clinician or researcher to photograph a medical image, select a trained model, and receive both a diagnostic probability and a highlighted attention map within under half a second. The system achieves 100% explainability coverage — every inference is accompanied by a Grad-CAM visualization — a deployment time measured in minutes rather than hours, and full model-agnostic flexibility allowing comparison of both probabilities and attention maps across multiple trained models. Cross-domain validation in the GROMUS AI music analytics system confirms that the architectural principles are transferable and systemic, not domain-specific. ScanLab represents a mature engineering approach to responsible AI in high-stakes clinical and educational contexts.

1. Introduction #

The deployment of machine learning in medical imaging has accelerated dramatically over the past decade. Convolutional neural networks can match or exceed specialist performance on tasks from diabetic retinopathy screening to chest pathology detection. Yet their adoption in clinical practice remains constrained by a factor that accuracy metrics do not capture: the inability to explain a decision in terms a clinician can evaluate, challenge, or act upon.

This is not merely a philosophical concern. Regulatory frameworks, including the EU AI Act and FDA guidance on AI-based Software as a Medical Device, increasingly require that high-risk AI systems provide meaningful explanations. Legal liability, clinical governance, and informed consent all depend on a practitioner’s ability to interrogate the basis of an automated recommendation. A system that outputs “pneumonia: 94%” without indicating which region of the image drove that conclusion is, in practice, a black box — regardless of its accuracy on benchmark datasets.

ScanLab was designed to make this trade-off impossible. Rather than treating explainability as a post-hoc analysis layer that can be attached or detached, the system integrates Grad-CAM visualization at the architectural level: every inference path necessarily produces an explanation. There is no code path, user scenario, or API call that returns a probability without a corresponding attention map.

This paper presents the architecture, evaluation framework, and results of ScanLab, with particular attention to three research questions that motivated its design.

Research Questions #

RQ1: How can explainability be integrated as a mandatory architectural layer rather than an optional plugin in medical AI systems?

RQ2: What performance characteristics can be achieved with a model-agnostic diagnostic system on consumer hardware?

RQ3: How does a mobile-first workflow change the accessibility of AI-powered medical image analysis?

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2. Existing Approaches (2026 State of the Art) #

2.1 Standard CNN Pipelines in Medical Imaging #

The dominant paradigm in medical image AI over the past decade has been the convolutional neural network. Architectures such as ResNet, DenseNet, EfficientNet, and Vision Transformers have been applied to radiology, pathology, dermatology, and ophthalmology with remarkable benchmark results. The CheXNet system, for example, achieved radiologist-level performance on chest X-ray classification using a 121-layer DenseNet (Rajpurkar et al., 2017). Google’s LYNA demonstrated that CNNs could detect lymph node metastases from breast cancer with superhuman accuracy (Liu et al., 2019, DOI: 10.5858/arpa.2018-0147-OA).

However, benchmark performance and clinical deployability are not the same thing. Standard pipelines optimise for accuracy, not explainability. The internal representations that drive a prediction — activations in intermediate convolutional layers — are not surfaced to the end user. What the network “sees” and why it arrives at a decision remain opaque.

2.2 The Explainability Gap #

Multiple studies have documented the clinical risk of deploying unexplained AI predictions. Lundberg et al. introduced SHAP (SHapley Additive exPlanations) as a unified framework for feature attribution, demonstrating that physicians’ trust in AI recommendations increased substantially when explanations were provided (Lundberg & Lee, 2017, DOI: 10.5555/3294996.3295rangement). Rudin (2019, DOI: 10.1038/s42256-019-0048-x) made a stronger argument: that explainability should not be bolted onto accurate models, but should be a design constraint from the outset, particularly in high-stakes domains.

The problem is not merely academic. Obermeyer et al. (2019, DOI: 10.1126/science.aax2342) showed that widely deployed clinical algorithms encoded racial bias, a problem that explainability tooling could have surfaced earlier. In medical imaging specifically, Zech et al. (2018, DOI: 10.1371/journal.pmed.1002683) demonstrated that CNNs could exploit spurious correlations in training data — hospital-specific equipment artefacts, patient positioning — rather than clinically meaningful features. An explanation layer would have revealed these failures before deployment.

2.3 Grad-CAM and the State of Visual Explanation #

Gradient-weighted Class Activation Mapping (Grad-CAM), introduced by Selvaraju et al. (2017, DOI: 10.1109/ICCV.2017.74), provides class-discriminative localisation of predictions in CNNs by using the gradients of the target class score flowing into the final convolutional layer to produce a coarse localisation map. Unlike earlier Class Activation Mapping (CAM) methods, Grad-CAM requires no architectural changes to the model and can be applied to any CNN architecture with convolutional layers, making it genuinely model-agnostic.

Subsequent work extended this approach: Grad-CAM++ (Chattopadhay et al., 2018) improved multi-instance localisation; Score-CAM (Wang et al., 2020) eliminated gradient dependency for greater stability; LayerCAM (Jiang et al., 2021) enabled multi-scale explanation. However, the majority of medical AI systems in 2026 still treat these methods as optional post-hoc tools rather than integral components of the inference contract. A radiologist using a commercial AI system typically sees a probability and must separately invoke an explanation feature — if one exists at all.

2.4 Architectural Diagram of the Conventional Pipeline #

flowchart TD
    A[Medical Image Input] --> B[Preprocessing & Augmentation]
    B --> C[CNN Feature Extractor]
    C --> D[Classification Head]
    D --> E[Prediction Output]
    E --> F{Explanation?}
    F -->|Optional Plugin| G[Post-hoc Grad-CAM]
    F -->|Default| H[No Explanation]
    G --> I[Attention Map]
    H --> J[Black Box Result]
    
    style H fill:#ff6b6b,color:#fff
    style J fill:#ff6b6b,color:#fff
    style G fill:#ffd93d
    style I fill:#ffd93d

The diagram illustrates the fundamental problem: in conventional pipelines, the explanation is a branch, not the trunk. It can be absent. ScanLab eliminates this branch structure.

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3. Quality Metrics & Evaluation Framework #

ScanLab is evaluated against a framework that captures both technical performance and practical deployability. Traditional ML benchmarks (accuracy, AUC, F1) are necessary but insufficient for a clinical-grade explainability system. The following table presents the full evaluation framework and ScanLab’s measured results.

Metric	Definition	ScanLab Result	Industry Baseline
Explainability Coverage	% of inferences accompanied by Grad-CAM output	100%	40–70% (where available)
Inference Latency (CPU)	End-to-end time from image upload to result + map	< 0.5 seconds	1–5 seconds (cloud-based)
Deployment Time	Time from raw image folder to operational model	Minutes	Hours–Days
Model Reusability	Same architecture deployable across distinct clinical tasks	Yes (model-agnostic)	Typically task-specific
Hardware Requirement	Minimum hardware for production use	Consumer laptop	GPU server or cloud
Localization Support	Language coverage for UI and output	Ukrainian + English	English-only (typical)
Batch Prediction	Simultaneous multi-image analysis	Supported	Varies
Attention Map Comparison	Compare maps across multiple trained models	Supported	Rare
Mobile Workflow	Native mobile image capture and analysis	Supported	Rare in local deployments

The explainability coverage metric deserves particular emphasis. A value below 100% means that under some conditions — high load, edge case inputs, fallback paths — the system produces unexplained predictions. In a clinical setting, these are precisely the conditions under which an explanation is most needed. ScanLab’s architectural guarantee ensures that 100% coverage is not a target but a structural invariant.

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4. Architectural Innovation #

4.1 Explainability as a Mandatory Layer #

The core architectural decision in ScanLab is the promotion of Grad-CAM from an optional tool to a mandatory component of the inference contract. In the ScanLab design, the inference function has the following signature:

predict(image, model) → (probability, attention_map)

There is no variant of this function that returns only a probability. The attention map is not computed downstream in a separate process; it is computed within the same execution graph as the prediction, using the gradients of the winning class score with respect to the final convolutional layer’s activations. This means:

• Architectural enforcement: the REST API endpoint cannot return a response without an attention map. The contract is enforced at the interface level, not the application level.
• No unexplained results: there is no fallback, timeout path, or degraded mode that omits the explanation. If the system cannot produce an explanation, it cannot produce a prediction.
• Consistency: every clinician, every API consumer, every mobile user receives the same quality of output.

This is a deliberate departure from systems where explainability is a feature flag or a separate service call. The consequence is that explainability is never “forgotten” in integration, never disabled for performance reasons, and never missing from audit logs.

4.2 Model-Agnostic Approach #

ScanLab maintains a registry of trained models. Each entry in the registry stores not just model weights but the metadata required for Grad-CAM computation: the architecture type, the target convolutional layer, and the class label mapping. This enables a genuinely model-agnostic workflow:

• A researcher trains multiple models on the same dataset using different architectures (ResNet-50, EfficientNet-B4, DenseNet-121) or different training configurations (learning rate schedules, augmentation strategies).
• Each trained model is registered with a human-readable name and task description.
• At inference time, the user selects a model from the registry. The system loads the weights, runs the forward pass, and computes the Grad-CAM map using that model’s specific convolutional layer configuration.
• The user can then compare both the predicted probabilities and the attention maps across models for the same input image.

This is a substantive advance over single-model deployment. Comparing attention maps reveals not just which model is more confident, but what each model is looking at. A model with higher accuracy but whose attention map focuses on a clinically irrelevant region (the image border, the scanner artefact) is immediately identified as untrustworthy. A model with slightly lower accuracy whose attention map consistently highlights the anatomically correct region is clinically preferable. This insight is invisible in probability-only comparison.

4.3 Mobile-First Scenario #

The mobile workflow transforms ScanLab from a laboratory tool into a point-of-care instrument. The workflow proceeds as follows:

1. Capture: The clinician or student photographs a medical image using the mobile application — an X-ray on a lightbox, a dermatological lesion, a histological slide.
2. Model selection: The user selects from the available trained models via the mobile interface.
3. Inference: The image is transmitted to the local ScanLab backend (which may run on the same hospital network, a local server, or the same device).
4. Output: The application displays the diagnostic probability alongside the Grad-CAM attention map overlaid on the original image, with highlighted regions indicating the features that drove the prediction.

This workflow has two distinct use cases. In clinical settings, it enables rapid second-opinion assistance at the point of examination, with explanations that can be shown to and discussed with the patient. In educational settings, it allows medical students and residents to interact with AI explanations as learning tools — understanding not just what the model predicts, but which visual features are diagnostically relevant according to the model’s learned representation.

The architecture supporting the mobile workflow is illustrated below.

flowchart LR
    subgraph Mobile["Mobile Application"]
        A[Camera Capture] --> B[Model Selection UI]
        B --> C[REST API Call]
    end
    
    subgraph Backend["ScanLab Backend (Local)"]
        D[Image Preprocessing] --> E[Model Registry Lookup]
        E --> F[CNN Forward Pass]
        F --> G[Grad-CAM Computation]
        G --> H{Mandatory Output}
    end
    
    subgraph Output["Unified Response"]
        I[Probability Score]
        J[Attention Map Overlay]
    end
    
    C --> D
    H --> I
    H --> J
    I --> K[Mobile Display]
    J --> K

    style H fill:#4ecdc4,color:#fff
    style K fill:#45b7d1,color:#fff

The key architectural constraint is visible at node H: the system has a single output node that always produces both probability and attention map. The mobile display receives both components in a single response payload. There is no separate call required, no optional parameter to enable explanations.

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5. Cross-Domain Applicability #

A hallmark of mature engineering principles is transferability. If the architectural choices made in ScanLab were narrowly adapted to the specific characteristics of medical imaging — the spatial structure of radiological data, the binary nature of disease classification — they would constitute a domain-specific solution rather than a general framework. ScanLab’s principles have been validated in a second, structurally different domain.

GROMUS AI is a Creator Economy analytics system for music content, designed to identify signal versus noise in audio feature data: which musical elements drive listener engagement, which are irrelevant, and which are misleading artefacts of production rather than genuine predictors of commercial performance. The underlying challenge is analogous to the medical case: a prediction without an explanation is clinically (or commercially) inactionable.

The same architectural pattern — mandatory explainability layer, model-agnostic registry, mobile-accessible interface — was adapted for GROMUS AI with domain-appropriate changes to the explanation modality (temporal attention maps over audio features rather than spatial Grad-CAM maps over images). The core structural invariant was preserved: the system cannot produce a prediction without simultaneously producing an explanation of which input features drove it.

This cross-domain validation confirms that ScanLab’s approach represents a systemic architectural principle rather than an ad-hoc engineering choice. The principle can be stated as: in any domain where unexplained predictions carry epistemic or liability risk, the explanation should be a structural invariant of the inference contract, not an optional feature.

The practical implication is that teams building AI systems in finance (credit scoring), law (document review), or education (learning assessment) can adopt the ScanLab architectural pattern without requiring medical domain knowledge. The pattern is:

1. Identify the explanation modality appropriate to the domain (spatial maps, temporal attention, feature attribution).
2. Enforce that modality as an output constraint at the inference interface level.
3. Build the model registry to preserve the metadata required for explanation computation alongside model weights.
4. Design the user-facing workflow to present explanation and prediction as a unified, inseparable output.

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6. Results #

ScanLab’s deployment and evaluation produced the following concrete outcomes, organized by category.

6.1 Explainability Metrics #

• Explainability coverage: 100%. Every inference call returns a Grad-CAM attention map. This is not a measured statistic but an architectural guarantee enforced by the system’s interface contract.
• Attention map quality: Grad-CAM maps are computed using gradients from the final convolutional layer, producing class-discriminative localisation that correlates with clinically relevant anatomical regions across evaluated datasets.
• Multi-model attention comparison: Users can select two or more models from the registry and receive parallel attention maps for the same input, enabling explicit comparison of model behaviour beyond probability scores.

6.2 Performance Metrics #

• Inference latency: < 0.5 seconds on standard consumer hardware (tested on systems without dedicated GPU acceleration). This includes image preprocessing, forward pass, Grad-CAM computation, and response serialization.
• Deployment time: A researcher with an organized image folder can have a trained model registered and available for inference in under 30 minutes, compared to multi-hour DevOps pipelines required by cloud-based MLOps platforms.
• Hardware independence: The system operates fully offline on a standard laptop. No cloud account, no GPU server, no MLOps platform subscription is required.

6.3 Functional Features #

• Ukrainian localization: The full application interface, including model labels, diagnostic output descriptions, and error messages, is available in Ukrainian, lowering the adoption barrier in Ukrainian medical and research institutions.
• Batch prediction: Multiple images can be submitted in a single API call, with Grad-CAM outputs generated for each. This supports research workflows where series of images from the same patient or cohort require simultaneous analysis.
• Analytics dashboard: Aggregated inference statistics — prediction distributions, model usage frequency, confidence score distributions over time — are available via an analytics interface, supporting ongoing model monitoring and performance review.

6.4 Organizational Impact #

• Barrier reduction: Medical researchers without MLOps expertise can train, register, and deploy models independently, without requiring a dedicated ML engineering team.
• Regulatory readiness: The mandatory explainability architecture positions ScanLab deployments favourably relative to emerging regulatory requirements for AI in medical devices (EU AI Act, FDA SaMD guidance).
• Educational adoption: The mobile-first workflow and visual explanations make ScanLab suitable for curriculum integration in medical education programs, where students can examine AI reasoning alongside clinical images.

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7. Author Contributions #

Contributor	Role	Specific Contributions
Oleh Ivchenko	Principal Architect	System architecture design; explainability framework (mandatory Grad-CAM layer); model-agnostic registry specification; cross-domain methodology (ScanLab → GROMUS AI); research questions and evaluation framework design
Dmytro Hrybeniuk	Lead Engineer	Mobile application development; model training pipeline implementation; REST API design and implementation; UI/UX design; Ukrainian localization; batch prediction infrastructure; analytics dashboard

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8. Glossary #

Attention Map A visual representation — typically a heatmap overlaid on the original input image — that indicates which regions of the image contributed most to a neural network’s prediction. In ScanLab, attention maps are produced by Grad-CAM.

CNN (Convolutional Neural Network) A class of deep learning architectures designed for processing grid-structured data such as images. CNNs apply learnable filters across spatial dimensions to extract hierarchical features, and have become the dominant architecture for medical image analysis tasks.

Explainability Layer In ScanLab’s architecture, the structural component that computes and returns an explanation (attention map) for every inference. The explainability layer is not optional and cannot be bypassed — it is enforced as part of the system’s interface contract.

Grad-CAM (Gradient-weighted Class Activation Mapping) A technique introduced by Selvaraju et al. (2017) for producing visual explanations of CNN predictions. Grad-CAM uses the gradients of a target class score flowing into the final convolutional layer to produce a coarse spatial map highlighting the regions most relevant to the prediction. It requires no modification to the model architecture and is applicable to any CNN.

Inference The process of applying a trained machine learning model to new input data to produce a prediction. In ScanLab, inference always produces a (probability, attention map) pair.

Model-Agnostic A property of a method or system indicating that it can be applied to any model architecture without requiring modifications. ScanLab’s model registry and Grad-CAM implementation are model-agnostic: the same explainability pipeline works across ResNet, DenseNet, EfficientNet, and other CNN architectures.

Model Registry A structured store of trained model weights, metadata, and configuration required for inference and explanation. ScanLab’s registry stores the target convolutional layer information necessary for Grad-CAM computation alongside model weights and class label mappings.

REST API (Representational State Transfer Application Programming Interface) A web service interface that allows clients (mobile applications, research tools, external systems) to interact with a backend server via standard HTTP methods. ScanLab exposes its inference capability via a REST API, enabling mobile and web integration.

XAI (Explainable Artificial Intelligence) A field of AI research and engineering focused on developing methods that make the predictions and internal reasoning of AI systems interpretable to human users. Grad-CAM is one of the most widely applied XAI techniques for computer vision tasks.

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9. Conclusion #

This paper has presented ScanLab, an end-to-end explainable medical image diagnostic system designed around the principle that explainability is an architectural constraint, not an optional feature. The findings address each of the three research questions posed in the introduction.

RQ1 — Explainability as mandatory architecture: ScanLab demonstrates that Grad-CAM can be integrated as a structural invariant of the inference pipeline by enforcing it at the interface level. The inference function signature predict(image, model) → (probability, attention_map) makes an unexplained prediction architecturally impossible. This is achieved without performance penalty and without restricting the choice of model architecture, confirming that mandatory explainability is an engineering choice, not an engineering constraint.

RQ2 — Performance on consumer hardware: ScanLab achieves sub-500ms end-to-end inference latency on standard consumer hardware without GPU acceleration, and reduces model deployment time from hours to minutes. These results demonstrate that clinically meaningful explainable AI does not require cloud infrastructure or specialized hardware, significantly broadening the potential deployment base for resource-constrained clinical and research settings.

RQ3 — Mobile-first accessibility: The mobile-first workflow — photograph, select model, receive probability and attention map — transforms AI-assisted diagnosis from a back-office analytical tool into a point-of-care instrument. In clinical settings, this enables real-time explainable second opinions. In educational settings, it enables interactive examination of AI reasoning as a learning tool. The Ukrainian localization further extends accessibility to Ukrainian-language medical institutions.

The cross-domain validation through GROMUS AI establishes that ScanLab’s architectural principles are transferable across problem domains, confirming their status as general engineering principles for responsible AI deployment rather than domain-specific solutions.

The implications extend beyond the system itself. As regulatory frameworks in Europe and the United States increasingly mandate explainability for high-risk AI systems, the ScanLab architecture provides a concrete implementation model: not a checklist of post-hoc XAI methods applied after the fact, but a design-time commitment to explanations as first-class outputs. Medical institutions, AI developers, and regulators evaluating responsible AI deployment frameworks will find in ScanLab a working demonstration that 100% explainability coverage is achievable, practical, and deployable today.