# NSIDDx: A Design Framework for Neuro-Symbolic, Practitioner-First Differential Diagnosis in Low-Resource Settings

arXiv: https://arxiv.org/abs/2609.00256 · HTML: https://mlsanity.com/paper/2609.00256/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 20 references that are in the map.
- First posted on arXiv 31 Aug 2026.

## Builds on (most cited first, 9 of 9)

- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [Capabilities of GPT-4 on Medical Challenge Problems](https://mlsanity.com/paper/2303.13375/) (2023)
- [MedKGI: Iterative Differential Diagnosis with Medical Knowledge Graphs and Information-Guided Inquiring](https://mlsanity.com/paper/2512.24181/) (2025)
- [CUPCase: Clinically Uncommon Patient Cases and Diagnoses Dataset](https://mlsanity.com/paper/2503.06204/) (2025)
- [Knowledge Graph Augmented Large Language Models for Disease Prediction](https://mlsanity.com/paper/2512.01210/) (2025)
- [Explainable Diagnosis Prediction through Neuro-Symbolic Integration](https://mlsanity.com/paper/2410.01855/) (2024)
- [Learning to reason about rare diseases through retrieval-augmented agents](https://mlsanity.com/paper/2511.04720/) (2025)
- [Health Sentinel: An AI Pipeline For Real-time Disease Outbreak Detection](https://mlsanity.com/paper/2506.19548/) (2025)

## Abstract (arXiv)

LLM-based diagnostic systems achieve high semantic accuracy on benchmarks, but open-ended evaluation on clinically uncommon presentations reveals a systematic gap between headline accuracy and verifiable clinical reliability. We evaluate an LLM+rare-disease-RAG pipeline across two cohorts and show that the paradigm produces confident outputs that are frequently unverifiable and systematically resistant to clinician interrogation. We present NSIDDx (Neuro-Symbolic Integrated Differential Diagnosis System), a design framework arguing that DDx systems in low-resource settings must treat the clinician as an active reasoning agent. We instantiate this through a neuro-symbolic pipeline with ternary symptom encoding, contradiction detection, audit strings, and practitioner override - running offline on consumer hardware. We distill five design principles for clinician-in-the-loop clinical NLP and invite the prospective studies needed to validate the claim at scale.