XAI (Explainable AI) in healthcare XAI (Explainable AI) in healthcare Currently, Artificial Intelligence (AI) is playing a critical part in realizing important systems like education, healthcare, sustainable energy, transportation, and traffic systems. In the medical field, there are constant advances in the usage of AI methods. But in medical practice, AI applications/models need to be transparent and explicable, given that results from AI may cause dire consequences. It is important for medical practitioners to understand AI system reasoning as a prerequisite to gain confidence in AI prediction results. It is important to note that in medical practice, clinicians need reliable, accurate, and trustworthy results. In fact, they need concrete explanations of AI results. For instance, in disease diagnosis, clinicians need to understand XAI to identify features responsible for AI system results regarding a patient's disease. Doctors/Clinicians need more than accurate predictions, they need explanations. If an AI system diagonses cancer, the clinician needs to know which features or image regions led to that conclusion. This helps them to catch errors and biases early and verify the AI's reasoning aligns with medical knowledge. XAI focuses on making AI models transparent and trustworthy, so clinicians and patients can actually trust AI models, this is critical in healthcare. But sometimes these XAI driven models can hallucinate and provide decisions that can affect safety, ethics, and regulation. For example, XAI tools like Grad-CAM (heatmaps) show which regions influenced diagnoses like cancer detection, radiology, pathology and dermatology. Explanations highlight contributing factors related to prevention, early intervention, and disparities in healthcare access. Gaining the trust of healthcare professionals necessitates AI applications to be transparent about their decision-making processes and underlying logic. The Black-Box Problem in AI The Black-Box Problem in AI Deep learning models especially neural networks often function as black boxes. They produce highly accurate predictions, but offer little insight into how those predictions were made. Clinicians can’t trust what they don’t understand, bias and data leakage remain hidden until harm occurs. Sequential models (LSTMs, Transformers) analyze patient timelines, but doctors need to understand which factors drove the prediction, XAI helps bridge the gap between ML outputs and clinical intuition. CNNs can detect tumors better than humans but radiologists need to see where the model is looking. Grad-CAM / saliency maps highlight regions influencing predictions. Without explanations, biases and data leakage stay hidden until real harm occurs. where Common XAI techniques like Model Agnostic (SHAP, LIME) quantifies features contributions, local explanations for individual predictions respectively, Model Specific attention mechanisms (for EHR sequences, NLP), saliency maps/heatmaps for medical imaging and interpretable models like decision trees, rule-based systems that are transparent by design. Model Agnostic (SHAP, LIME) Model Agnostic (SHAP, LIME) Model Specific attention mechanisms (for EHR sequences, NLP) Model Specific attention mechanisms (for EHR sequences, NLP) SHAPley Additive Explanations: SHAPley Additive Explanations: import numpy as np
def shap_values(model, x, baseline, num_samples=1000):
    M = len(x)
    phi = np.zeros(M)
    for i in range(M):
        contrib = 0.0
        for _ in range(num_samples):
            mask = np.random.randint(0, 2, M)
            mask[i] = 0
            x_without = np.where(mask, x, baseline)
            f_without = model(x_without)
            mask[i] = 1
            x_with = np.where(mask, x, baseline)
            f_with = model(x_with)
            contrib += f_with - f_without
        phi[i] = contrib / num_samples
    return phi import numpy as np
def shap_values(model, x, baseline, num_samples=1000):
    M = len(x)
    phi = np.zeros(M)
    for i in range(M):
        contrib = 0.0
        for _ in range(num_samples):
            mask = np.random.randint(0, 2, M)
            mask[i] = 0
            x_without = np.where(mask, x, baseline)
            f_without = model(x_without)
            mask[i] = 1
            x_with = np.where(mask, x, baseline)
            f_with = model(x_with)
            contrib += f_with - f_without
        phi[i] = contrib / num_samples
    return phi Local Interpretable Model for local explanations: Local Interpretable Model for local explanations: import numpy as np
from sklearn.linear_model import LinearRegression
def lime_explanation(model, x, num_samples=500, noise=0.1):
    M = len(x)
    Z = []
    y = []
    for _ in range(num_samples):
        z = x + np.random.normal(0, noise, M)
        Z.append(z)
        y.append(model(z))
    Z = np.array(Z)
    y = np.array(y)
    reg = LinearRegression()
    reg.fit(Z, y)
    return reg.coef_, reg.intercept_ import numpy as np
from sklearn.linear_model import LinearRegression
def lime_explanation(model, x, num_samples=500, noise=0.1):
    M = len(x)
    Z = []
    y = []
    for _ in range(num_samples):
        z = x + np.random.normal(0, noise, M)
        Z.append(z)
        y.append(model(z))
    Z = np.array(Z)
    y = np.array(y)
    reg = LinearRegression()
    reg.fit(Z, y)
    return reg.coef_, reg.intercept_ GRAD-CAM for visual explanations GRAD-CAM for visual explanations import tensorflow as tf
import numpy as np
def grad_cam(model, img, class_index, conv_layer_name):
    grad_model = tf.keras.models.Model(
        [model.inputs],
        [model.get_layer(conv_layer_name).output, model.output]
    )
    with tf.GradientTape() as tape:
        conv_outputs, predictions = grad_model(img)
        loss = predictions[:, class_index]
    grads = tape.gradient(loss, conv_outputs)
    pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2))
    conv_outputs = conv_outputs[0]
    heatmap = tf.reduce_sum(tf.multiply(pooled_grads, conv_outputs), axis=-1)
    heatmap = tf.nn.relu(heatmap)
    heatmap /= tf.reduce_max(heatmap)
    return heatmap.numpy() import tensorflow as tf
import numpy as np
def grad_cam(model, img, class_index, conv_layer_name):
    grad_model = tf.keras.models.Model(
        [model.inputs],
        [model.get_layer(conv_layer_name).output, model.output]
    )
    with tf.GradientTape() as tape:
        conv_outputs, predictions = grad_model(img)
        loss = predictions[:, class_index]
    grads = tape.gradient(loss, conv_outputs)
    pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2))
    conv_outputs = conv_outputs[0]
    heatmap = tf.reduce_sum(tf.multiply(pooled_grads, conv_outputs), axis=-1)
    heatmap = tf.nn.relu(heatmap)
    heatmap /= tf.reduce_max(heatmap)
    return heatmap.numpy() The above methods are commonly used algorithms/methods in explainable AI. In XAI there are methods that are not being explored in practice but only for research activities, such as causal Casual explainability, Conterfactual explanations, Testing with Concept activation vectors, Global SHAP, Partial Dependence Plots (PDP), Accumulated Local Effects (ALE), Bayesian neural networks, Human-in-the-loop systems, Explanation logging, Neural Additive Models (NAMs), Generalized Additive Models (GAMs), Attention-based but constrained architectures, Time-aware SHAP, because of complexity and technical maturity, many advanced methods are research tools, not ready-to-deploy systems. Methods like Bayesian NNs or NAMs require high-quality, large-scale data, clinical datasets are often small (privacy constraints), heterogeneous, and noisy. , such as causal Casual explainability, Conterfactual explanations, Testing with Concept activation vectors, Global SHAP, Partial Dependence Plots (PDP), Accumulated Local Effects (ALE), Bayesian neural networks, Human-in-the-loop systems, Explanation logging, Neural Additive Models (NAMs), Generalized Additive Models (GAMs), Attention-based but constrained architectures , such as causal Casual explainability, Conterfactual explanations, Testing with Concept activation vectors, Global SHAP, Partial Dependence Plots (PDP), Accumulated Local Effects (ALE), Bayesian neural networks, Human-in-the-loop systems, Explanation logging, Neural Additive Models (NAMs), Generalized Additive Models (GAMs), Attention-based but constrained architectures Time-aware SHAP Time-aware SHAP Bayesian NNs NAMs Why XAI Explains Model Behavior, Not Medical Truth Why XAI Explains Model Behavior, Not Medical Truth XAI methods provide insights into how a model made a prediction, but they do not guarantee that the prediction given by the model is medically valid. XAI explains the model's reasoning, not clinically correct. This is critical in healthcare, where decisions directly affect patient safety. AI models learn patterns from data, and XAI reveals which patterns the model relies on. However, these patterns may reflect biases and correlations present in the training data, rather than genuine causal medical relationships. The solution for this relies on how we are gonna train a model that doesn't overfit, memorizing noise, model complexity, etc.... The Accuracy vs Interpretability Trade-Off The Accuracy vs Interpretability Trade-Off Interpretable models are always less accurate because they sacrifice accuracy. In structured healthcare data, simpler models often match deep learning. Overly complex models increase deployment and regulatory risk. In healthcare, the best model is the one clinicians actually use. For structured healthcare data, simpler models often perform on par with deep learning. Trust and usability matter more than marginal performance gains; it’s the one clinicians actually use. Conclusion Conclusion Explainable Artificial Intelligence is essential for the safe, ethical, and effective integration of AI into healthcare systems. Right now, in the modern age, people need explanations rather than vague results. Could XAI solve these problems? We couldn't be sure, as many legacy models like GPT-4o, Claude Sonnet have hallucinated on medical data, but by making XAI a gateway mechanism, can we ensure that they would not hallucinate? Still, we cannot trust an AI with medical data even if the model gives clear explanations, confident predictions, and visually convincing heatmaps. They still can be wrong, so medical experts must verify whether the AI’s reasoning reflects true medical cause-and-effect relationships, not accidental statistical trends. If we have sufficient data, an effective processing technique, a valid model architecture, appropriate evaluation metrics, and XAI as a gateway mechanism, we can build a safe, reliable, and unbiased AI medical model.

The is an opinion piece based on the author’s POV and does not necessarily reflect the views of HackerNoon.

This story contains AI-generated text. The author has used AI either for research, to generate outlines, or write the text itself. 

Explainable AI (XAI) in Healthcare: Trust, Transparency, and the Limits of AI Decisions

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