Explain or Expire: Why Trustworthy AI Starts with Transparency

Artificial intelligence has officially embedded itself in the healthcare enterprise—from diagnostic imaging to billing automation, and increasingly, to clinical decision support. But while the capabilities of AI are accelerating at an unprecedented rate, its trustworthiness remains on shaky ground. The issue at the center of that trust gap? Transparency.

The so-called “black box” problem—the inability to understand how an AI system arrived at its conclusion—has become more than a philosophical dilemma. In healthcare, where a single decision can ripple through a patient’s life, a lack of explainability isn’t just inconvenient. It’s unacceptable.

In short, if we want AI to stay in healthcare, it must learn to explain itself. If not, it risks regulatory, reputational, and even ethical extinction.

The Trust Cliff

Healthcare is a domain that runs on verification, not vibes. Clinicians are trained to weigh evidence, validate assumptions, and document decision-making processes. When AI offers up a recommendation—without showing its work—it fundamentally disrupts that process.

That disruption is no longer theoretical. A 2024 survey by the American Medical Informatics Association (AMIA) found that 68% of clinicians said they were hesitant to use AI-driven tools that lacked explainability, even when they showed high accuracy. In other words, even if the machine is “right,” if the clinician doesn’t know why it’s right, the tool stays on the shelf.

This is not stubbornness. It’s clinical prudence.

Explainability is more than a UX feature—it’s a critical element of trust. And without trust, adoption stalls. Worse, errors go undetected, and patients suffer.

Accuracy Isn’t Enough

The industry has spent the past five years chasing benchmarks: F1 scores, AUCs, sensitivity, specificity. But those metrics, while important, do not equate to safety or ethical soundness.

Consider a recent example: a hospital AI system designed to predict sepsis risk was found to over-prioritize white patients, due to training data imbalances. The model’s raw accuracy scores were strong—but its real-world outcomes reinforced systemic disparities.

In this case, the lack of model interpretability masked the problem. Clinicians had no window into how the algorithm weighted risk factors. The result? A tool that “worked” on paper but failed in practice.

Explainability tools such as SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-agnostic Explanations), and counterfactual analysis are beginning to provide much-needed transparency. But they are not yet widespread, nor easily understood by non-data scientists. That’s a problem we need to solve.

Regulation Is Catching Up

The regulatory landscape is no longer a Wild West. The FDA has moved aggressively to categorize and evaluate Software as a Medical Device (SaMD), especially for adaptive and continuously learning algorithms. Explainability, once a nice-to-have, is rapidly becoming table stakes.

In Europe, the AI Act now requires high-risk AI systems—including those used in healthcare—to provide documentation of their decision-making processes. This includes traceability, robustness, and clarity around inputs and outputs. U.S. regulators are watching closely.

Moreover, malpractice law is beginning to evolve. If a clinician follows an opaque AI recommendation and the outcome is negative, who is liable? The clinician? The hospital? The developer? Explainability doesn’t just protect patients—it protects providers.

The Role of Explainable AI (XAI)

Enter XAI—Explainable Artificial Intelligence. XAI aims to make the inner workings of AI models transparent and understandable, without significantly compromising performance.

There are two primary approaches:

• Intrinsic Explainability: Designing inherently interpretable models, like decision trees or rule-based systems. These are simpler but may lack the predictive power of deep learning models.

• Post-Hoc Explainability: Applying interpretability tools to black-box models after training, using methods like SHAP or feature importance ranking.

Both have their place. In low-risk use cases—billing optimization, supply chain predictions—post-hoc explainability may suffice. But in clinical scenarios where decisions can affect life and death, intrinsic transparency may be required.

Still, XAI has limitations. Even the best explanations can oversimplify complex model behavior. And clinicians don’t want more dashboards—they want clarity, relevance, and clinical alignment. That means explainability must be built into the workflow, not layered on top of it.

Cultural Change Is Essential

As much as we talk about algorithms and regulation, this is ultimately about people. We need to build a culture in healthcare that demands explainability—not just from AI vendors, but from ourselves.

• Health systems must insist on explainability as part of their AI procurement process.

• Clinicians must be involved in the model development lifecycle, not just handed the final product.

• Vendors must stop treating their models as proprietary secrets and start collaborating with transparency as a competitive differentiator.

We must move from “what can AI do?” to “what should AI do, and why?” That’s the hallmark of a mature healthcare system—not one that blindly adopts technology, but one that applies it ethically and intelligently.

A New Standard for AI in Healthcare

The bottom line? If your AI can’t explain itself, it doesn’t belong in the clinic.

Explainability should not be a retrofit. It should be designed into the system from the start. We’re not just building tools. We’re building a new kind of partnership between human intelligence and machine intelligence—one that depends on clarity, context, and trust.

In a world of growing complexity, we need not just smarter systems, but more understandable ones. And that might be the smartest move of all.