How AI systems must prove trust, transparency and reliability before clinical use

A team of researchers has developed a new blueprint designed to close the widening gap between AI research and its safe, trustworthy use in hospitals. Their study, titled "Before the Clinic: Transparent and Operable Design Principles for Healthcare AI," outlines concrete technical and procedural steps that artificial intelligence developers must complete before any system reaches clinical evaluation. The authors argue that without a standardized "pre-clinical phase," the healthcare sector risks deploying opaque, unreliable AI tools that fail to meet the expectations of doctors and regulators alike.

The paper marks one of the first attempts to define an actionable pre-clinical playbook for healthcare AI, focusing not just on model performance, but on whether systems can be understood, trusted, and verified before touching real patients.

Bridging the gap between theory and clinical reality

The research addresses a long-standing problem in healthcare technology: many AI models perform well in labs yet struggle to transition safely into hospitals. Existing frameworks for explainable artificial intelligence (XAI) and AI governance, such as the EU AI Act and the NIST AI Risk Management Framework, offer broad principles on transparency and accountability. But as researchers note, they rarely tell engineering teams what exactly to build or test before clinical evaluation.

To fix this, the study introduces two core design pillars: Transparent Design and Operable Design. Together, these define the minimum technical readiness criteria for any AI system entering the clinical pipeline. Transparent Design ensures that clinicians can interpret and understand a model's behavior, while Operable Design guarantees that the system behaves predictably and robustly under real-world conditions.

By clearly defining these elements, the authors create a roadmap that connects AI theory, clinical expectations, and governance compliance. The framework acts as a bridge between research prototypes and regulated clinical tools, a step the authors describe as essential for maintaining both patient safety and public trust.

Transparency as the foundation of trust

The first principle, Transparent Design, focuses on cognitive clarity. It breaks down into two key dimensions: interpretability (understanding why a model made a certain decision) and understandability (grasping how the model functions overall).

Clinical users must be able to identify both case-level explanations and system-level reasoning, the study explains. To achieve this, developers should integrate several interpretability artifacts during the pre-clinical phase:

• Feature Attribution: pinpointing which data inputs most influenced a specific prediction, such as a patient's vital signs or lab results.
• Temporal Explanations: showing how patient conditions over time affected risk scores or model outputs.
• Modality Attribution: clarifying which type of data, text, images, or numerical readings, played the dominant role in a decision.

These methods make AI reasoning traceable, allowing clinicians to see how predictions align with their own diagnostic logic. The framework also calls for transparent fusion mechanisms, where combined data sources in multimodal systems must be mathematically documented and open for inspection.

In addition to individual cases, global explainability is equally critical. Development teams must document training datasets, model architecture, and version histories, producing comprehensive "model cards" that describe intended use, population coverage, and known limitations. This ensures that future clinical evaluators can trace every aspect of a system's lineage.

Finally, the researchers highlight two essential validation requirements: faithfulness and stability. Explanations must accurately reflect model logic (faithfulness) and remain consistent under small data changes (stability). Together, these checks guard against misleading or inconsistent explanations, a major issue in current AI interpretability research.

Operability as a measure of reliability

While transparency focuses on understanding, Operable Design ensures technical integrity. This principle assesses whether an AI model remains stable, calibrated, and resilient under real-world uncertainty. It includes three core components: calibration, uncertainty, and robustness.

Calibration tests whether predicted probabilities truly match observed outcomes. In healthcare, this is crucial: an AI model predicting a "70 percent chance" of cardiac arrest must align closely with actual clinical frequencies. Techniques such as Expected Calibration Error (ECE), Brier Score, and temperature scaling can adjust overly confident models, particularly deep neural networks known for inflated probabilities.

Next, the study stresses uncertainty awareness - the ability of AI systems to signal when they are unsure. Two uncertainty types are defined:

• Aleatoric uncertainty, stemming from noise or randomness in patient data.
• Epistemic uncertainty, arising when the model encounters unfamiliar or underrepresented data.

By quantifying both, developers can design systems that defer to human oversight when confidence falls below safe thresholds. The authors point to conformal prediction frameworks as a way to implement these safeguards systematically.

Finally, robustness ensures that systems perform reliably under imperfect conditions. The paper outlines several robustness tests that should be part of every pre-clinical pipeline:

• Handling missing data without catastrophic failure.
• Evaluating performance across demographic subgroups to identify bias.
• Assessing temporal and geographic shifts, ensuring models trained at one hospital generalize to another.

Pre-clinical robustness evaluation, the authors argue, not only anticipates deployment challenges but also aligns with upcoming post-market monitoring rules under the EU AI Act and FDA guidance on machine learning practices.

A framework aligned with policy and practice

The framework directly maps to established governance and research models. Transparent Design corresponds to the interpretability and understandability dimensions in Combi et al.'s XAI framework, while Operable Design extends the technical robustness requirements of the EU Trustworthy AI guidelines. The study also cross-references clinician feedback documented by Tonekaboni et al., showing that doctors view uncertainty and calibration as integral to trustworthy AI.

The authors clearly state that their framework does not replace clinical evaluation, but rather defines the work that must precede it. They identify clear transition points to next-stage standards such as DECIDE-AI, CONSORT-AI, and SPIRIT-AI, which guide early trials and usability studies.

By establishing what can be built and verified before patient contact, Transparent and Operable Design provide a foundation for future clinical testing. They also help research teams understand when a model is ready to advance, avoiding premature deployment while preventing excessive development delays.