SymptomWise: A Deterministic Reasoning Layer for Reliable and Efficient AI Systems

Computer Science > Artificial Intelligence [Submitted on 7 Apr 2026] SymptomWise: A Deterministic Reasoning Layer for Reliable and Efficient AI Systems Isaac Henry, Avery Byrne, Christopher Giza, Ron Henry, Shahram Yazdani AI-driven symptom analysis systems face persistent challenges in reliability, interpretability, and hallucination. End-to-end generative approaches often lack traceability and may produce unsupported or inconsistent diagnostic outputs in safety-critical settings. We present SymptomWise, a framework that separates language understanding from diagnostic reasoning. The system combines expert-curated medical knowledge, deterministic codex-driven inference, and constrained use of large language models. Free-text input is mapped to validated symptom representations, then evaluated by a deterministic reasoning module operating over a finite hypothesis space to produce a ranked differential diagnosis. Language models are used only for symptom extraction and optional explanation, not for diagnostic inference. This architecture improves traceability, reduces unsupported conclusions, and enables modular evaluation of system components. Preliminary evaluation on 42 expert-authored challenging pediatric neurology cases shows meaningful overlap with clinician consensus, with the correct diagnosis appearing in the top five differentials in 88% of cases. Beyond medicine, the framework generalizes to other abductive reasoning domains and may serve as a deterministic structuring and routing layer for foundation models, improving precision and potentially reducing unnecessary computational overhead in bounded tasks. Comments: 18 pages, 1 figure, Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2604.06375 [cs.AI]   (or arXiv:2604.06375v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2604.06375 Focus to learn more Submission history From: Shahram Yazdani [view email] [v1] Tue, 7 Apr 2026 19:00:53 UTC (471 KB) Access Paper: view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-04 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)