Demystifying Medical AI: How Explainable AI (XAI) Is Making Imaging Diagnosis Transparent and Trustworthy

Table of Contents

• Key Points
• Why This Research Matters: The Black Box Problem in Medical AI
• The Evolution of AI in Medical Imaging: From 1980s Rules to 2025 Multimodal Systems
• Study Methods: How This Systematic Review Was Conducted
• XAI Techniques: The Four Families of Explanations
• Applications of XAI in Medical Imaging
• Key Findings: What 133 Studies Reveal
• Challenges and Limitations: What Still Stands in the Way
• Clinical Implications: What This Means for Patients and Doctors
• Recommendations: The Path Forward for Transparent AI
• Frequently Asked Questions
• Source Information

Key Points

• XAI makes AI imaging tools transparent by showing which image areas influenced a diagnosis.
• Four families of XAI exist: saliency maps, attention mechanisms, LIME/SHAP, and rule-based methods.
• Across 133 studies, XAI increased clinician confidence but faced challenges in standardization and bias.
• XAI aids radiologists and pathologists, but doctors remain responsible for final treatment decisions.
• Explainable AI reduces errors by making AI reasoning auditable and helps comply with transparency regulations.

Why This Research Matters: The Black Box Problem in Medical AI

Over the past decade, artificial intelligence has made remarkable progress in medical imaging. AI systems using machine learning (ML) and deep learning (DL) — advanced computer algorithms that learn patterns from vast amounts of data — have become valuable tools for diagnosing breast cancer and brain cancer, detecting retinal (eye) disease, and segmenting (delineating) structures within medical images [1–4]. But there is a catch: many of these systems are "black boxes" that produce highly accurate results without explaining how they reached their conclusions.

This lack of transparency is deeply problematic in healthcare. When a doctor is deciding whether a patient needs surgery, chemotherapy, or no treatment at all, they need to understand why the AI flagged a suspicious lesion on a scan. A prediction without a rationale is difficult — and often impossible — to trust in high-stakes clinical decisions.

Explainable AI (XAI) was developed to solve this problem. XAI is a collection of techniques designed to make AI decision-making processes visible, understandable, and auditable. The Defense Advanced Research Projects Agency (DARPA), the U.S. government agency that helped create the internet, defines XAI's goal as producing AI models that are both highly accurate and interpretable, so that human users can understand, appropriately trust, and effectively manage AI partners [6].

There are also legal requirements driving this shift. The European Union's General Data Protection Regulation (GDPR) requires that algorithmic decision-making be transparent before it is used in patient care [7]. Similarly, the U.S. Department of Health and Human Services emphasizes that clinicians must understand regulatory standards to use Clinical Decision Support Systems (CDSS) efficiently and safely. Explainability helps doctors make sound clinical judgments while staying compliant with these regulations.

The Evolution of AI in Medical Imaging: From 1980s Rules to 2025 Multimodal Systems

The journey of AI in medicine has been a long one, marked by several paradigm shifts. The review traces this history through a timeline that helps contextualize why explainability has become urgent only recently.

• The 1980s: Rule-based expert systems appeared — simple algorithms that followed if-then rules for decision support. They were transparent by design but made many errors.
• The 1990s: Machine learning algorithms emerged, learning patterns directly from image datasets rather than following hand-coded rules.
• The early 2000s: Deep learning arrived, producing unprecedented accuracy in image analysis and paving the way for Convolutional Neural Networks (CNNs) — specialized architectures inspired by the visual cortex.
• 2012: The breakthrough of AlexNet, a powerful CNN, accelerated the dominance of black-box models, which achieved top performance but were increasingly difficult to interpret.
• 2015: Interpretability became a distinct research focus, giving rise to the independent field of XAI.
• 2018: Transformer architectures transformed AI, enabling new levels of performance and subtlety in image interpretation.
• 2022: Federated privacy-preserving XAI emerged, allowing AI to learn from distributed data without centralizing patient information.
• 2025 and beyond: Experts predict a phase of multimodal, interpretable AI characterized by a persistent trade-off between accuracy and explainability.

This historical arc matters for patients because it explains why AI has become both more powerful and more mysterious at the same time. Early AI was simple enough to be fully understood but not smart enough to be very useful. Modern AI is powerfully accurate but opaque — which is precisely why XAI is needed.

Study Methods: How This Systematic Review Was Conducted

The research team — Ahmed and colleagues from institutions in Pakistan, India, Jordan, Yemen, and South Korea — conducted a systematic literature review (SLR) following the rigorous PRISMA 2020 guidelines, the internationally accepted standard for evidence synthesis. Their goal was to answer four specific research questions:

1. What XAI techniques are currently being used in medical imaging, and which methods are most effective?
2. How are XAI approaches applied to real clinical use cases in radiology and pathology?
3. What challenges and limitations exist in the interpretability of AI medical imaging systems?
4. How do clinicians perceive XAI-based diagnostic tools' utility, effectiveness, and usability in real-world clinical settings?

The literature search covered major scientific databases, identifying records published between 2015 and 2025. The screening process was exhaustive and multi-stage:

• 980 records were initially identified
• 289 duplicates were removed
• 691 records proceeded to screening
• 209 were excluded during screening
• 482 full texts were assessed for eligibility
• 263 were excluded after full-text review
• 219 studies were considered, ultimately yielding 133 included studies

The review team used inter-rater consensus to ensure reliability of study selection, meaning multiple reviewers independently assessed studies and resolved disagreements through discussion. The final corpus of 133 studies formed the basis of all findings in this review.

One notable feature of this review is how it compares with earlier surveys published between 2020 and 2025. Table 1 in the original paper compares seven prior reviews [12, 19–25] against the proposed study. The authors note that earlier reviews often covered only saliency maps and attention mechanisms, had minimal or no focus on the clinical workflow, and gave low attention to regulatory and ethical issues. This review explicitly addresses those gaps.

XAI Techniques: The Four Families of Explanations

The review introduces a unified taxonomy that organizes explainability methods into four major families. Each family has its own strengths, limitations, and best-use scenarios.

Family 1: Saliency Maps and Heatmaps

Saliency maps are visual tools that highlight which parts of an image the AI model focused on when making its decision. Imagine a radiologist looking at a chest X-ray: a saliency map might glow brightly over a suspicious nodule, showing the doctor exactly where the AI's attention was directed. These maps are generated using gradient-based techniques, most notably Gradient-weighted Class Activation Mapping (Grad-CAM), which produces heatmap overlays that are intuitive even for non-technical users.

Strengths: Highly visual, immediately interpretable by clinicians, and useful for verifying whether the AI is looking at the correct anatomical region. Limitations: They show where the AI looked but not why that region was considered abnormal, and they can sometimes produce misleading or noisy visualizations.

Family 2: Attention Mechanisms

Attention mechanisms — originally developed for transformers, the architecture behind many modern language models — allow AI to weight different parts of an image dynamically. When a model processes a medical scan, it learns to "attend to" (focus on) the most diagnostically relevant regions and to ignore background noise. Unlike saliency maps, which are often applied after the fact, attention is built into the model's architecture itself, providing explanations as a byproduct of the calculation.

Strengths: Integrated directly into modern model architectures like Vision Transformers (ViTs), providing inherent interpretability without extra processing steps. Limitations: Attention weightings do not always match true causal importance, and can be difficult to validate.

Family 3: Model-Agnostic Methods (LIME and SHAP)

Model-agnostic methods work with any AI model, regardless of its internal architecture. Two of the most prominent are:

• Local Interpretable Model-Agnostic Explanations (LIME): This technique approximates the AI model's behavior locally by perturbing (slightly altering) the input and observing how the output changes, then building a simple, interpretable model that mimics the complex one in the vicinity of a given prediction.
• Shapley Additive Explanations (SHAP): Based on cooperative game theory, SHAP assigns each input feature an importance score that represents its contribution to the model's prediction. It provides mathematically principled explanations of why a particular image region pushed the model toward a particular diagnosis.

Strengths: Flexible, applicable to virtually any AI model, and SHAP offers theoretical guarantees of consistency. Limitations: Computationally expensive, and the explanations can be abstract and difficult to visualize for image data.

Family 4: Rule-Based and Symbolic Methods, Including Graph Neural Networks (GNNs)

Rule-based methods generate human-readable rules that explain AI decisions — for example, "if the lesion has irregular margins AND high vascularity, the model classifies it as malignant." The review also includes Graph Neural Networks (GNNs) in this family: GNNs are a newer architecture that models relationships between different regions of an image or between different structures in a scan, making them particularly well-suited for understanding anatomical and pathological connectivity.

Strengths: Produces explanations that closely resemble clinical reasoning, making them intuitively acceptable to physicians. Limitations: Rules can oversimplify complex diagnostic scenarios, and GNNs are still an emerging technology in medical imaging.

The review notes that deep learning models used in medical imaging — including CNNs, Recurrent Neural Networks (RNNs), Autoencoders, Generative Adversarial Networks (GANs), U-Net models, and Vision Transformers (ViTs) — each present unique interpretability challenges [17, 18]. The choice of XAI technique must match the underlying model architecture and the specific clinical question being asked.

Applications of XAI in Medical Imaging

The review examines XAI applications across three major imaging domains, paying attention to how each technique maps to specific impact points in the clinical workflow.

Radiology: X-ray, CT, and MRI

Radiology is the most mature application area for XAI. The review covers studies using X-ray, Computed Tomography (CT), and Magnetic Resonance Imaging (MRI). XAI techniques in radiology are applied to tasks including:

• Automated detection and segmentation of lesions and abnormalities
• Disease classification (for example, distinguishing benign from malignant findings)
• Prognosis prediction based on imaging features
• Quality control — verifying that the AI model is focusing on genuine pathologies rather than artifacts or unrelated anatomical structures

The authors emphasize that in radiology, saliency maps and Grad-CAM heatmaps are particularly valuable because radiologists are already trained to read visual images. When the AI's highlighted region aligns with a radiologist's own assessment, confidence in the diagnosis increases substantially. When the AI highlights an unexpected region, it prompts the radiologist to re-examine that area — potentially catching pathologies that might otherwise have been missed.

Pathology: Digital and Whole-Slide Images

Pathology has been transformed by digital scanning technology that converts glass microscope slides into massive digital images — called whole-slide images (WSIs) — that can be analyzed by AI. However, WSIs are enormous (often billions of pixels), making explanation particularly challenging. The review examines how XAI helps pathologists trust AI-assisted analysis of tissue samples, including cancer grading and staging.

The authors note that pathology is a distinct challenge because the granularity of tissue analysis requires explanations at multiple scales — from cellular-level features to tissue architecture. XAI techniques that work well at the image level in radiology may need adaptation for the hierarchical, multi-scale nature of pathology images.

Multimodal Imaging: PET-CT and MRI-PET

Modern diagnostics increasingly combine multiple imaging modalities — for example, Positron Emission Tomography (PET) combined with CT (PET-CT) or MRI fused with PET (MRI-PET). These combinations provide complementary information: PET shows metabolic activity while CT or MRI shows anatomical structure. Multimodal imaging is then often combined with clinical data, genomics, and laboratory results, creating a complex data landscape.

The review highlights multimodal transformers and GNNs as particularly promising for these applications because they can integrate and relate information across different data types. However, multimodal integration also compounds the interpretability challenge: explaining a decision that synthesizes a PET scan, an MRI, a biopsy report, and genetic testing is far more complex than explaining a single-image decision.

This area represents one of the newest frontiers in XAI research, and the review identifies it as a critical direction for future work.

Key Findings: What 133 Studies Reveal

The review's synthesis of 133 studies produces several important conclusions.

Finding 1: XAI significantly enhances clinical decision-making. The review reports that XAI makes the high-level reasoning of AI models readily available to users, which measurably increases clinician confidence in AI-assisted decisions. When doctors understand why an AI system made a recommendation, they are more likely to incorporate that recommendation appropriately into patient care.

Finding 2: XAI reduces clinical liability. By grounding automated diagnostic tools in ethical frameworks and clinical guidelines, XAI helps lower the risk of medical errors and provides a basis for accountability. Healthcare systems can audit AI decisions, document their rationale, and identify potential biases or systematic errors before they harm patients [14].

Finding 3: All four XAI technique families have demonstrated utility, but each has specific strengths. Saliency maps excel in radiography, model-agnostic methods like SHAP offer the broadest applicability, attention mechanisms provide integrated explanations for modern transformer models, and rule-based methods align most closely with how physicians articulate clinical reasoning.

Finding 4: The comparison of XAI methods enables informed decision-making. The review's comparison framework — identifying consistently effective techniques and their clinical workflow impact points — provides practical guidance for healthcare institutions deciding which XAI methods to implement.

Finding 5: The gap between AI research and clinical practice is narrowing but remains significant. While promising results have been achieved, the review finds that systematic application of XAI in day-to-day clinical practice is still limited. Most existing studies focus on isolated algorithms or specific clinical applications rather than comprehensive integration into actual clinical workflows.

Challenges and Limitations: What Still Stands in the Way

The review identifies four major categories of obstacles that must be overcome before XAI can be fully integrated into clinical medicine. It is important for patients to understand these limitations because they explain why AI systems are being deployed gradually and cautiously.

1. Standardization

There is currently no universally accepted standard for what constitutes a "good" explanation or how to measure interpretability. Different studies use different metrics, different visualization approaches, and different validation methods, making it difficult to compare results across studies. The review specifically notes that methodological standardization and comparative effectiveness of different XAI methods have received insufficient attention. This lack of standardization also extends to a notable gap: the evaluation of consistent interpretability metrics that would allow objective comparison of techniques.

2. Data Bias

AI algorithms learn from historical data, and if that data contains biases — for example, underrepresentation of certain demographic groups, specific imaging equipment, or particular disease presentations — the AI model will perpetuate or amplify those biases. XAI can help expose inherent biases, but it cannot eliminate them. The review calls for further research on fitting data bias to appropriate distributions and using interpretability to detect and correct biased behavior.

3. Clinician Perceptions and Education

Even the most transparent AI system is useless if clinicians do not trust it or understand its outputs. The review found that clinicians' perceptions of XAI-based tools and their pragmatic clinical impact have not been adequately examined in the literature. There is a need for studies that explore physicians' views on XAI technologies, their real-world usability, and how these tools integrate into existing clinical systems.

4. Complicated Data Integration

Real-world medical decisions are rarely based on a single image. They involve patient history, laboratory results, genomic data, and clinical examination findings — not to mention the technical challenges of heterogeneous imaging equipment and formats. Integrating XAI across this multimodal, heterogeneous data landscape remains one of the most complex challenges facing the field.

Clinical Implications: What This Means for Patients and Doctors

For patients, the growth of XAI means several meaningful things. First and foremost, XAI is designed to improve patient safety by reducing the risk of AI making an invisible, inexplicable error. When an AI system must justify its conclusions, systematic mistakes become detectable and correctable.

Second, XAI helps remove prejudice from data and incorrect predictions, which is especially important when AI is used in diagnostic contexts. The review notes that XAI "provides comfort of thought for patients' minds" [18] — in other words, when patients know that their doctor is using AI technology that can explain its reasoning, they can feel more confident in the diagnostic process.

Third, XAI is a critical enabler of personalized treatment. By clearly explaining which features of an image drive a particular diagnosis or prognosis prediction, XAI helps physicians tailor treatment plans to each patient's specific disease characteristics. This aligns with the modern trend toward precision medicine, where decisions are based on individual patient data rather than population averages.

For health systems, the review emphasizes that AI-based diagnostic tools — aided by XAI — can increase radiologists' and pathologists' working efficiency without sacrificing accuracy. When clinicians understand AI recommendations, they can make faster decisions without losing confidence.

The review also flags important practical issues. The automation of lesion segmentation, disease classification, and prognosis prediction can help clinicians manage heavy workloads, but this automation must be introduced carefully, with adequate training, transparent expectations, and clear accountability structures.

Recommendations: The Path Forward for Transparent AI

Drawing from their analysis, Ahmed and colleagues offer several concrete recommendations for researchers, healthcare institutions, and policymakers.

1. Develop standardized interpretability metrics. The field needs universally accepted ways of measuring and comparing the quality of explanations across different XAI methods. Without such standards, evidence cannot accumulate and best practices cannot emerge.
2. Create standardized XAI reporting frameworks. The review explicitly proposes a regular, standardized reporting framework for future XAI research, ensuring that studies report their methods, validation approaches, and clinical outcomes consistently.
3. Focus research on clinician perception and workflow integration. More studies are needed that evaluate how physicians actually experience XAI tools — their usability, their impact on decision-making speed and accuracy, and their integration into electronic health records and radiology workflows.
4. Prioritize research on multimodal data integration. As multimodal imaging (PET-CT, MRI-PET) and combined imaging-plus-clinical data become more common, XAI techniques must evolve to explain decisions that draw from multiple data sources simultaneously.
5. Address data bias as a first-class problem. XAI research must incorporate explicit strategies for detecting, quantifying, and mitigating data bias, rather than treating bias as an afterthought.
6. Explore newer architectures. The review recommends increased attention to Graph Neural Networks (GNNs) and multimodal transformers, which promise not only better performance on complex imaging tasks but also more inherent interpretability.
7. Build XAI into regulatory frameworks from the ground up. The review calls for detailed regulatory analysis integrating frameworks such as FDA clearances, the EU AI Act, and the Medical Device Regulation (MDR), with concrete clinical vignettes illustrating how explainability requirements operate in practice.

Ultimately, the authors conclude that achieving robust XAI in medical imaging requires a three-pronged effort: synthesizing current knowledge, applying XAI to real clinical use cases, and directly confronting the interpretability challenges inherent in medical image analysis. They note that their review "identifies research gaps and paves the way for robust medical imaging XAI solutions," suggesting that future work should focus on bridging the remaining distance between laboratory success and bedside utility.

Frequently Asked Questions

What is explainable AI (XAI) in medical imaging?

XAI stands for explainable artificial intelligence. These are techniques that make AI systems show why they reached a diagnosis from a medical image. Instead of a black-box result, XAI highlights which areas of an X-ray, CT, MRI, or pathology slide influenced the decision. This helps doctors understand and trust the AI’s recommendation.

Why does AI used for reading scans need to explain itself?

AI can be very accurate but still a black box, meaning it gives a result without showing its reasoning. In medicine, doctors need to know why a scan is flagged before deciding on surgery or chemotherapy. Legal rules like GDPR and safety standards also require transparency so that AI decisions can be checked and trusted.

How can explainable AI help my doctor diagnose me?

When AI highlights the exact area of concern on your scan, your doctor can compare this with their own interpretation. If they agree, confidence grows. If AI points to an unexpected spot, your doctor may re-examine that area. This teamwork aims to catch problems that might otherwise be missed.

Does explainable AI replace my radiologist or pathologist?

No. XAI is designed to support doctors, not replace them. The review shows it increases confidence and efficiency, but final decisions always remain with your clinician. AI assists by providing clear explanations, while your doctor combines this with your history, symptoms, and other tests to plan your care.

Are there any risks or limitations with explainable AI in imaging?

Yes. The review of 133 studies found four main challenges: there is no standard way to measure if an explanation is good; AI can inherit biases from historical data; some doctors may not trust or understand XAI outputs; and combining AI with many different data types is still difficult. These limits are why AI is introduced gradually.

What does ''black box'' mean in medicine?

A black box AI is a system that gives accurate results but does not reveal how it reached them. For example, an AI might correctly flag a suspicious lesion on a scan but cannot say why. In high-stakes decisions like whether you need surgery, a prediction without explanation is hard for doctors to trust.

How does explainable AI improve patient safety?

When AI must justify its findings, mistakes become easier to spot and correct. The review reports that XAI reduces clinical liability by grounding decisions in ethical and clinical guidelines. It also helps remove bias from data, meaning fewer incorrect predictions. Patients can feel more confident knowing the reasoning behind an AI-supported diagnosis.

What imaging and pathology records do I need to bring for a second opinion on an AI-assisted imaging diagnosis?

For a second opinion on an AI-assisted imaging diagnosis, the original imaging (X-ray, CT, MRI, or whole-slide pathology) is essential. Also bring any explainable AI outputs, such as saliency maps or heatmaps, that show which image regions the AI used for its conclusion. Because these tools highlight where the AI looked, a second expert can verify whether the finding is genuine or an artifact. Include the radiology or pathology report and note any known limitations, such as potential data bias. Diagnostic Detectives Network provides independent expert second opinions.

Source Information

Original Article Title: Explainable artificial intelligence (XAI) in medical imaging: a systematic review of techniques, applications, and challenges.

License: CC BY-NC-ND (open access)

Authors: Ahmed F, Naz NS, Khan S, Rehman AU, Ismael WM, Khan MA.

Journal: BMC Medical Imaging (2026) 26:37

DOI: https://doi.org/10.1186/s12880-025-02118-w

Study Type: Systematic literature review following PRISMA 2020 guidelines

Clinical Trial Number: Not applicable (this was a review of existing literature, not a clinical trial)

Funding/Institutional Affiliations: The authors are affiliated with the National College of Business Administration and Economics (Pakistan), Saveetha Institute of Medical and Technical Sciences (India), Applied Science Private University (Jordan), Chitkara University (India), Azal University for Human Development (Yemen), and Gachon University (Republic of Korea).

This patient-friendly article is based on peer-reviewed research published in BMC Medical Imaging, an open-access journal. The original article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. Patients and caregivers are encouraged to discuss any questions about AI-assisted imaging with their healthcare providers.