Anterior Launches Open-Source Framework to Make Healthcare AI More Consistent and Auditable
NEW YORK, Oct. 29, 2026 /PRNewswire/ -- Anterior, the clinician-led AI company helping health plans automate complex administrative and clinical workflows, today announced the open-source release of symbolic-reasoning, a framework built on Symbolic Reasoning Graphs (SRGs) and designed to help artificial intelligence systems consistently follow complex clinical policies and make their reasoning transparent and auditable.
The framework addresses a critical challenge in healthcare AI: ensuring that AI systems apply clinical guidelines reliably and consistently at scale. While large language models (LLMs) can analyze medical records and interpret complex information, they can also overlook requirements, interpret ambiguous policies differently, or reach different conclusions when evaluating the same case. In healthcare, these inconsistencies can delay patient care, create additional administrative work, or contribute to incorrect coverage determinations.
"AI has enormous potential to eliminate unnecessary administrative work in healthcare, but that potential depends on being able to trust how these systems reach their conclusions," said Anuj Iravane, AI Lead at Anterior. "Clinical guidelines are complex, and much of the expertise required to apply them lives in the judgment and experience of clinicians. We built Symbolic Reasoning Graphs to make that expertise explicit, so AI systems can follow the same agreed-upon rules consistently and show exactly how they arrived at a recommendation."
Unlike traditional AI approaches that interpret clinical policies each time a case is reviewed, SRGs convert policies into structured, executable rules that can be reviewed and validated by clinical experts in advance. AI models then focus on evaluating specific questions about a patient's medical record, while a deterministic system applies the established policy logic to those answers. This separation reduces inconsistencies in how policies are applied and produces an auditable record of the criteria evaluated and how each outcome was reached.
The framework is particularly relevant to prior authorization, where health plans must evaluate complex medical necessity criteria against lengthy clinical records. By making policy interpretations explicit and breaking reviews into individual, verifiable steps, SRGs help health plans improve consistency, identify errors, and maintain clinical oversight as they scale AI-powered workflows. SRGs help clinicians stay in the loop, as every recommendation shows its granular workings.
Anterior developed SRGs through its work with thousands of medical policies spanning prior authorization, quality measurement, claims and benefits checks, and payment integrity. The open-source release, available as a research preview, includes a version of Anterior's symbolic reasoning library, a dedicated language called srg-lang for representing complex policies, and a Model Context Protocol (MCP) server.
Although developed for healthcare, the framework can also be applied to other highly regulated industries, including financial services and legal services, where AI systems must reliably follow complex rules and demonstrate how conclusions are reached.
"We're open-sourcing this framework because consistent, auditable reasoning is a challenge that extends well beyond healthcare. Systems that reliably follow policy, and can prove it, should be foundational infrastructure for AI in high-stakes environments," said Vitali Avagyan, Member of Technical Staff at Anterior.
The open-source framework is available at https://github.com/anteriorai/symbolic-reasoning.
About Anterior
Anterior is a clinician-led company that uses responsible AI to automate and accelerate payer workflows. The platform is trusted by clinicians and loved by technology teams, helping health plans streamline operations while keeping clinical judgment at the center of care decisions. For more information, visit www.anterior.com and follow the company on LinkedIn and X.
SOURCE Anterior
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