Pattern Publishes Breakthrough Explainable AI Framework in Scientific Reports, Advancing Trust and Adoption in High-Stakes Industries

Accuracy and Interpretability position Pattern at forefront of growing demand for trustworthy AI

REDMOND, Wash., March 23, 2026 (GLOBE NEWSWIRE) -- Pattern Computer®, Inc. (“Pattern” or “the Company”), the global leader in Pattern Discovery, today announced the publication of its latest research, “Adaptive example selection for prototype-based explainable mitosis detection in digital pathology,” in Nature: Scientific Reports. The study introduces a novel explainable AI (XAI™) framework that combines high-performance deep learning with transparent, human-aligned reasoning, unlocking new opportunities across regulated and high-stakes industries.

Read the full publication: https://rdcu.be/e88Dk

Despite rapid advances, many AI systems remain “black boxes,” limiting their adoption in environments where decisions must be understood, trusted, and validated. Deep learning models in pathology often lack interpretability, making it difficult to understand how they transform inputs into diagnostic outputs, raising concerns about liability and reliability in clinical settings. XAI is critical for verifying AI logic, detecting unexpected behaviors, and conducting audits in cases of error. Pattern’s newly published framework addresses this challenge by making AI decisions both accurate and interpretable.

In its primary application, mitosis detection in digital pathology, the system achieves strong predictive performance while maintaining 96% fidelity between predictions and explanations. Each decision is supported by a small set of intuitive, comparable examples, enabling users to understand not just what the model predicts, but why.

Adaptive, Contrastive Example Selection: At the core of this innovation is adaptive, contrastive example selection, which presents both supporting and opposing evidence for every prediction. This enables a form of counterfactual reasoning, allowing users to see why a decision was made — and why alternatives were rejected — mirroring how experts evaluate evidence in practice.

“Understanding the decision-making process of black-box neural networks is essential for safety, accountability, and trust in medical AI, particularly for high-stakes tasks such as histopathology,” said Mark Anderson, Pattern’s Chair and CEO. “Techniques like Adaptive Example Selection (AES), a prototype-based XAI framework that retrieves supporting/contradicting real-world images to explain AI confidence in tasks like mitosis detection, help interpret these models by linking predictions to visual features or similar cases, ensuring human oversight and overcoming the ‘black box’ limitations.”

Anderson concluded, “This advancement positions Pattern at the forefront of the growing demand for trustworthy AI, enabling organizations to deploy AI systems with greater confidence in mission-critical environments. Pattern is now focused on scaling the framework to larger datasets, integrating it into real-time workflows, and advancing toward production deployment. The long-term vision is a universal explainable AI platform for transparent, accountable decision-making across industries. Our goal is to make AI systems not just accurate, but understandable and actionable. This work shows that performance and interpretability are not trade-offs — they can, and should, go hand-in-hand.”

Unlike traditional methods that rely on abstract feature importance or opaque internal signals, this framework uses real-world examples to provide clear, evidence-based insights. It maintains high fidelity while remaining intuitive to interpret. The study also highlights a key operational benefit: explainability can reveal hidden model weaknesses, enabling continuous improvement and more robust deployment. While validated in digital pathology, the approach is designed to scale across domains where transparency is essential, including medical imaging, drug discovery, manufacturing quality control, and digital forensics.

About Pattern
Pattern Computer, Inc. is a next-generation AI platform company which uses its Pattern Discovery Engine™ to solve the most important and intractable problems in business and medicine. These proprietary mathematical techniques in advanced AI can find complex patterns in very-high-order data that have eluded detection by much larger systems, including LLMs. As the Company applies its computational platform to the challenging fields of drug discovery and diagnostics, it is also making major Pattern Discoveries for partners in other sectors, including extended biotech, climate challenges and materials science, aerospace manufacturing quality control, veterinary medicine, air traffic operations, equity trading, AI regulatory compliance in the EU, and energy services. See www.patterncomputer.com.

CONTACT: Laura Guerrant-Oiye (808) 960-2642 – laura@patterncomputer.com

The foregoing contains statements about Pattern Computer’s future that are not statements of historical fact. These statements are “forward-looking statements” for purposes of applicable securities laws and are based on current information and/or management’s good faith belief as to future events. The words “believe,” “expect,” “anticipate,” “project,” “should,” “could,” “will,” and similar expressions signify forward-looking statements. Forward-looking statements should not be read as a guarantee of future performance. By their nature, forward-looking statements involve inherent risk and uncertainties, which change over time, and actual performance could differ materially from that anticipated by any forward-looking statements. Pattern Computer undertakes no obligation to update or revise any forward-looking statement.

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