Explainable AI in Healthcare: Transparency, Interpretability, and Explainability

As artificial intelligence becomes increasingly integrated into healthcare, understanding how and why AI systems make decisions is critical. Let's explore the core concepts of explainable AI (XAI) and how they apply to clinical practice — with a focus on building trust, improving outcomes, and supporting safe adoption.

Theoretical Foundations and Importance

Transparency, interpretability, and explainability are interrelated concepts aimed at making AI decisions understandable and trustworthy. In healthcare, this is critical because AI-driven diagnoses or recommendations directly impact patient lives.

Making AI Understandable in Healthcare

Transparency is a big-picture goal in AI — it's a mindset that ensures AI systems are understandable, accountable, and trustworthy. We achieve transparency through two important aspects: interpretability and explainability. These terms are often used interchangeably, but they have distinct meanings, and there's no universal agreement on how to define them across different fields.

In general:

• Interpretability means understanding how an AI model makes its decisions.
• Explainability focuses on providing human-friendly reasons for why a specific decision was made.

In healthcare, both are essential. Medical professionals must understand and trust AI systems, especially when lives are at stake. Guidelines like the EU’s Trustworthy AI framework stress that AI systems must be transparent and accountable to be safely used in clinical environments.

Academic studies confirm that explainability is one of the key ingredients for successfully adopting AI. It helps bridge the gap between technical AI models and the healthcare professionals who rely on them. Some experts even argue that in high-risk settings like healthcare, we should build interpretable models from the ground up, instead of using "black box" systems and trying to explain them later. That's because post-hoc explanations (explanations added after the model is built) can sometimes be misleading.

The foundations of Explainable AI include both:

• Models that are easy to understand (e.g. decision trees, rule-based systems), and
• Tools that help explain more complex models.

Books like Explainable AI in Healthcare: Unboxing Machine Learning for Biomedicine (2023) explore how these ideas help connect clinicians and data scientists. The message is clear: XAI builds trust. When clinicians understand why an AI tool makes a recommendation, they are more likely to use it in practice. In fact, a recent review found that clear, relevant explanations can increase trust — but vague or poorly designed explanations may do more harm than good.

How AI Can Be Made Understandable

There are various ways to make AI models more interpretable and explainable. Some models are easy to interpret by design, while others require additional tools to explain how they work. These approaches generally fall into two categories:

1. Intrinsic Interpretability

These models are designed to be understandable from the start. Examples include:

• Decision trees
• Rule-based systems
• Linear models
• Case-based reasoning (learning from past examples)

Because the logic in these models is visible, it’s easy for a clinician to trace why a recommendation was made. For instance, a rule-based system might show the medical criteria that triggered a certain treatment suggestion.

2. Post-hoc Explainability

Some AI models, especially deep learning systems, are complex and act like "black boxes." For these, we use techniques after training to explain how they work. Common approaches include:

Feature Attribution (e.g. SHAP, LIME)

These tools help identify which features (e.g., symptoms, lab values, image pixels) most influenced a prediction. SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-Agnostic Explanations) don’t look inside the model. Instead, they treat it like a black box and build simple, local explanations for individual cases.

For example, a sepsis prediction tool might show that a high lactate level and rapid heart rate were the top reasons a patient was flagged as high risk.

These explanations don’t reveal how the entire model works, but they help clinicians understand individual predictions. This improves interpretability and helps align AI with clinical reasoning.

Visual Explanations for Medical Imaging (e.g. Grad-CAM)

In radiology and pathology, saliency maps and heatmaps help visualize what a deep learning model is "looking at." Grad-CAM, for example, can highlight a suspicious area on a chest X-ray that led the AI to suggest pneumonia. These tools help radiologists understand where the model is focusing its attention.

However, such visual explanations should be carefully validated. Some studies show they’re helpful; others warn they might highlight irrelevant areas if not properly tested. In short, these tools must be checked with clinicians to make sure they make sense in the real world.

Model-Built-In Interpretability (e.g. Attention, Prototypes)

Some models are designed with built-in interpretability:

• Attention mechanisms show which parts of a medical text (like a doctor’s note) the model focused on.
• Prototype-based systems compare a new patient to similar cases from the past and explain a prediction based on those similarities.
• Neuro-symbolic models combine rules with deep learning for better transparency.

These approaches aim to balance accuracy with interpretability — a critical trade-off in clinical settings. A simpler model that’s easier to explain may be slightly less accurate, but it’s often more usable in practice.

While often discussed under post-hoc methods, these features are sometimes integrated into model architecture and blur the line between design and explanation.

Designing XAI for Real Healthcare Use

Interpretability isn't just about algorithms — it’s also about how explanations are delivered to people.

• Explanations must be short, relevant, and use medical language clinicians understand.
• They should support — not disrupt — the clinical workflow.
• Ideally, explanations appear alongside AI predictions, as part of a decision report or a dashboard.

Studies recommend involving clinicians in the design process. This ensures the explanations truly help them make better decisions. When done well, XAI becomes a core part of safe and effective medical AI — not just an afterthought.

Where XAI Is Already Making an Impact

AI systems are already being used or tested in many areas of healthcare. To ensure these tools are trusted and adopted, it's important that they provide transparent and understandable explanations.

Medical Diagnosis from Images

In fields like radiology, pathology, and ophthalmology, AI helps detect diseases from medical images. But it's not enough for an AI to say what it sees — clinicians need to understand why the AI made a specific diagnosis.

For example, some AI systems highlight relevant areas in a scan before giving a diagnosis. This helps clinicians see what the AI saw — such as fluid, lesions, or tumors — and better judge the result.

In chest X-ray analysis, AI models are often paired with heatmaps that highlight parts of the image the model focused on. These visual explanations help validate the AI's output and can improve diagnostic accuracy.

Some systems now include XAI dashboards where radiologists can see not only the AI’s conclusion but also which regions of the image and which data features influenced that conclusion. This makes AI outputs more aligned with clinical reasoning.

Treatment Recommendations and Decision Support

AI is increasingly used to support doctors in choosing the right treatment or therapy — especially in areas like oncology or infectious diseases.

These systems need to be traceable: clinicians must understand why a particular treatment was suggested.

Some clinical decision support tools explain their recommendations by showing connections to clinical guidelines or similar past cases. For example, a prototype antibiotic advisor may show how a recommendation was based on infection type, allergy history, and local resistance data.

Earlier systems tried to provide supporting literature, but when explanations were unclear, doctors were less likely to trust them. Newer tools are more transparent by design, using rule-based logic or knowledge graphs to provide a clear explanation path — helping doctors treat the AI more like a colleague than a black box.

Risk Prediction and Early Warning Systems

AI models are also used to predict which patients are at risk of worsening conditions — such as sepsis, stroke, or ICU deterioration. In these high-stakes scenarios, explainability is crucial for clinical acceptance.

Some AI tools generate early warnings based on patterns in vital signs, lab results, and patient history, and present this information through interpretable dashboards. These dashboards often highlight the most influential factors contributing to the risk score — for example, elevated heart rate, low blood pressure, or abnormal lab values.

Advanced systems go a step further by providing real-time explanations alongside the prediction. Instead of just giving a number, they show the reasoning behind the alert, helping clinicians understand the "why" behind the AI’s output. This supports faster clinical decision-making and allows for more informed communication with patients and care teams.

Across different use cases, XAI-powered models enhance trust and usability by linking risk scores to specific, understandable clinical features — making them more valuable and safer in practice.

Why Clear AI Explanations Matter in Healthcare

These examples show a clear pattern: AI is more likely to be adopted and trusted in healthcare when it provides clear, meaningful, and clinically relevant explanations.

• Clinicians are more confident when they can see why the AI made a recommendation.
• Patients benefit when doctors can explain AI-supported decisions in understandable terms.
• Regulators require explainability for AI systems that affect patient care.

As this field evolves, successful AI tools will not only perform well but also explain themselves clearly — helping to build trust, improve outcomes, and ensure responsible use in clinical settings.