Explainable AI in Healthcare: AI for Supply Chain Operations
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Kara L. Nadeau, Healthcare Industry Contributor
Friday, May 8, 2026
Kara L. Nadeau, Healthcare Industry Contributor
Healthcare supply chain leaders are under increasing pressure to make faster, more informed decisions in complex, high-stakes environments. As artificial intelligence (AI) becomes more embedded in supply chain workflows, one question continues to surface:
Can we trust what AI is telling us—and understand why?
The lack of transparency in AI systems can lead to trust issues among supply chain leaders, who may see AI as a 'black box' that produces outputs without revealing the reasoning behind them. This hesitation can slow adoption and limit the value of AI in supply chain management. This is where AI explainability becomes essential.
Explainable AI (XAI) can clarify the logic behind predictions, such as explaining changes in estimated time of arrival (ETA) for shipments. By showing the reasoning behind its outputs, XAI transforms AI from a passive forecasting tool into a collaborative decision-making partner, helping supply chain leaders understand the causes of changes in operational metrics.
Likewise, AI explainability refers to the ability to clearly understand how an AI system arrives at its insights, signals or recommendations. Explainable AI enables stakeholders to explain predictions made by AI systems, which enhances trust and accountability.
In healthcare supply chain operations, this means teams can:
Explainable AI (XAI) is essential in healthcare because it allows stakeholders to understand the decision-making processes of AI systems. Explainable AI in healthcare is not about replacing human judgment. It helps teams understand how complex algorithms influence recommendations and outcomes, making AI-driven insights visible, understandable and actionable within existing workflows.
AI explainability matters because supply chain decisions directly impact operational performance, financial outcomes and collaboration across providers and suppliers. The advantages of explainable AI include improved accuracy, fairness, and transparency, which help organizations make better decisions and foster confidence among users and stakeholders. Understanding the importance of transparency and education in trustworthy AI deployment is critical for fostering trust, safety, and effective use of AI tools in healthcare.
Risk
Without explainability, teams may act on insights they cannot validate. This can introduce operational risk, especially when managing exceptions, disruptions or inventory decisions. There is a gap between current practices and optimal transparency, and this gap can affect trust and compliance in healthcare AI adoption.
Compliance
Healthcare organizations operate in highly regulated environments. Transparent AI supports auditability by showing how decisions were informed and ensuring processes align with internal policies. The benefits of explainable AI governance include increased transparency, accountability, and risk management, which are essential for compliance in healthcare.
Explainable AI helps organizations build trust in AI systems by providing clarity on how decisions are made, which is essential for mitigating risks and ensuring compliance with regulatory standards in supply chain operations.
Trust
Adoption depends on confidence. When teams understand how insights are generated, they are more likely to use them. Explainability helps build trust across supply chain, finance and executive leadership. Explainable AI provides assurance to users and stakeholders by making decision-making processes transparent and reliable, which fosters customer trust. Explainable AI also helps those affected by AI decisions understand the reasoning behind outcomes, which is crucial for building trust.
XAI improves diagnostic accuracy, fosters trust through transparent decision-making, and supports personalized treatment.
Explainability is not a feature—it is how intelligence is delivered within the workflow. By making AI-driven insights visible, understandable and actionable within existing workflows, organizations gain access to the underlying decision-making processes of AI models, enhancing transparency and control over AI systems.
Visibility into signals
Teams can see the inputs driving insights, such as:
Explainable AI enables teams to predict potential issues by understanding the data and logic behind AI recommendations.
Context for recommendations
Insights are not presented in isolation. Teams understand:
Traceable workflows
Every insight can be followed through the process:
It is also essential to monitor for drift in model performance and data over time, as changes in the environment or input data can impact the accuracy and fairness of AI models. Ongoing detection and mitigation of drift help maintain responsible and effective AI deployment.
This creates a more connected and transparent operating model that aligns with broader trends in healthcare supply chain transformation.
XAI mitigates the ‘black-box’ issue, allowing clinicians and supply chain leaders to audit and trust AI-driven predictions. AI tools designed for explainability also help clinicians communicate the reasoning behind AI suggestions to patients, supporting patient autonomy and informed consent.
Addressing the Black Box Problem
The “black box” problem in artificial intelligence (AI) arises when organizations cannot see or understand how AI systems arrive at their decisions. In healthcare supply chain operations, this lack of transparency can undermine trust, slow adoption, and introduce risk—especially when AI models influence critical business outcomes or patient care decisions.
Explainable AI is essential for addressing this challenge. By providing clear reasoning and transparent explanations for AI-driven insights, organizations can move beyond opaque models and foster greater accountability. This transparency is not just a technical requirement—it’s a foundation for responsible AI governance and ethical principles in healthcare.
Black Box Concerns
Some AI systems use opaque models that produce outputs without showing how they were generated, making them difficult to interpret. This limits confidence and slows adoption. The difference between traditional AI and explainable AI is significant—explainable AI provides greater interpretability, which helps build trust and supports better organizational decision-making.
Lack of Transparency
Disconnected systems and fragmented data can make it difficult to trace insights back to their source, creating uncertainty in decision-making. There is a gap between current practices and optimal transparency and education in healthcare AI, but many organizations are actively working to address this gap through improved governance, risk management, and staff training initiatives. Unreported or uncontrolled AI deployment can be problematic, leading to security vulnerabilities and compliance challenges. Educating healthcare staff on the responsible use of AI tools is critical to ensure safe and effective implementation.
Explainability in Order Automation
Techniques such as Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) are used in healthcare to clarify which features influence AI predictions, supporting transparency and informed decision-making. Comprehensive training for healthcare professionals on AI tools is essential to ensure they understand both the capabilities and limitations of these technologies, which can prevent misuse and promote effective adoption.
Order automation is a critical area where explainability directly improves performance. By making operational workflows more transparent and responsive, explainable AI enhances customer service, enabling teams to proactively address issues, maintain service levels, and improve trust.
Exception reasoning
AI helps identify and prioritize exceptions—but explainability ensures teams understand:
This helps reduce repeat issues and improve order accuracy.
Validation signals
Explainable AI surfaces signals that help teams assess risk before issues escalate, such as:
Explainable AI also helps teams determine whether outcomes are as expected, supporting proactive intervention and continuous improvement.
This supports earlier intervention and more predictable outcomes across the healthcare procurement contract-to-cash lifecycle.
Additionally, XAI can identify which patient factors most influenced a diagnosis, supporting clinical decision-making in healthcare supply chain contexts.
No. AI supports decisions—it does not replace them.
Human oversight remains essential to:
Humans play a critical role in interpreting AI decisions, ensuring accountability, and maintaining transparency throughout the explainability process. It is also important that AI explanations are understandable for non-technical stakeholders, such as procurement or operations teams, to encourage organizational trust and adoption.
This “human-in-the-loop” approach ensures that AI strengthens decision-making without removing control.
Explanations must be delivered at the point of care without overwhelming busy clinicians or causing alert fatigue.
GHX applies AI across its platform to deliver clear, actionable intelligence embedded within supply chain workflows—not as a separate tool, but as part of how teams work every day.
Powered by GHX ResiliencyAI, this intelligence helps teams better understand what’s happening across orders, invoices and payments, why issues are occurring, and where to focus next.
Clear insights
GHX solutions help surface:
Decision support within the workflow
Rather than making decisions, GHX solutions help teams:
This approach helps shift teams from reacting to issues after they occur to addressing them earlier with greater clarity.
Built for transparency and trust
With visibility into the signals and context behind each insight, teams can:
This approach aligns with GHX ResiliencyAI—intelligence designed to help providers and suppliers understand what’s happening, determine what it means and take informed action with greater confidence.
As AI becomes more embedded in supply chain operations, explainability will define its value within broader Healthcare Supply Chain 2.0 innovations.
Having a clear plan for responsible AI governance—including risk management, documentation, and regulatory compliance—is essential for successful and ethical adoption. Organizations must actively monitor and manage AI applications and AI tools, including auditing training data for bias and compliance, to ensure transparency, responsible implementation, and safety.
When teams can clearly understand insights, trust increases. When trust increases, adoption follows. And when adoption grows, organizations are better positioned to:
Looking to the future, the implementation of explainable AI governance requires establishing clear objectives, forming cross-functional committees, and adopting recognized standards to ensure responsible AI practices across the organization. As organizations focus on scaling AI, they must address governance, transparency, and risk management challenges—explainable AI is critical for building trust, monitoring model performance, and ensuring compliance at scale. There is often a trade-off between performance and interpretability, as high-performing complex models in XAI are frequently the most difficult to explain.
Explainable AI governance is a framework that standardizes how AI models are designed, deployed, and monitored to ensure transparency, interpretability, and accountability, especially in high-stakes contexts like healthcare. A well-structured explainable AI governance model helps organizations document behavior, track performance, and ensure alignment with ethical principles and legal requirements, which is crucial for building trust and reducing bias. Frameworks like the EU AI Act mandate transparency and accountability for high-risk medical AI systems, and new standards increasingly require such systems to be transparent and auditable.
Explainable AI is not just about transparency—it is about enabling better decisions, every day, supported by ongoing healthcare supply chain insights and resources.
Kara L. Nadeau has 25+ years’ experience as a writer/content creator for the healthcare industry, serving clients in fields including medical supplies and devices, pharmaceuticals, supply chain, technology solutions, and quality management.