mlsanityData to 7 Oct 2026

From Knowledge to Inference: Formalizing Specialized Public Health Reasoning on GlobalHealthAtlas

arXiv 2602.00491 ↗First posted 31 Jan 2026cs.CLcited by 0

On the map: LLM reasoning → Clinical and biomedical applications

Abstract (arXiv)

Public health reasoning requires population level inference grounded in scientific evidence, expert consensus, and safety constraints. However, it remains underexplored as a structured machine learning problem with limited supervised signals and benchmarks. We introduce GlobalHealthAtlas, a large scale multilingual dataset of 280,210 instances spanning 15 public health domains and 17 languages. We further propose a large language model (LLM) assisted construction and quality control pipeline with retrieval, deduplication, evidence grounding checks, and label validation to improve consistency at scale. Finally, we present a domain aligned evaluator distilled from high confidence judgments of diverse LLMs to assess outputs along six dimensions: Accuracy, Reasoning, Completeness, Consensus Alignment, Terminology Norms, and Insightfulness. Together, these contributions enable reproducible training and evaluation of LLMs for safety critical public health reasoning beyond conventional QA benchmarks. We publicly release project codebase, evaluator, and model at:: https://github.com/Jan8217/GlobalHealthAtlas, https://huggingface.co/aerovane0/GlobalHealthAtlas_Public_Evaluator and https://huggingface.co/aerovane0/GlobalHealthAtlas_Public_Model

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning. Builds on 21 of its 64 references that are in the map. First posted on arXiv 31 Jan 2026.

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21 of 64 references.

Closest ancestors · 2

Same direction, newest first

  1. 2025RephQA: Evaluating Readability of Large Language Models in Public Health Question Answering19 Sep
  2. 2024HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs25 Dec

Other references · 19

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  7. 2023Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicinecited by 319
  8. 2023AlpaCare:Instruction-tuned Large Language Models for Medical Applicationcited by 88
  9. 2024Health-LLM: Large Language Models for Health Prediction via Wearable Sensor Datacited by 65
  10. 2024Apollo: A Lightweight Multilingual Medical LLM towards Democratizing Medical AI to 6B Peoplecited by 38
  11. 2026TraceRouter: Robust Safety for Large Foundation Models via Path-Level Interventioncited by 8
  12. 2024Evaluating Large Language Models for Public Health Classification and Extraction Taskscited by 6
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  2. 2024STF: Sentence Transformer Fine-Tuning For Topic Categorization With Limited Datacited by 1
  3. 2025TheBlueScrubs-v1, a comprehensive curated medical dataset derived from the internetcited by 1
  4. 2025An AI-Based Public Health Data Monitoring Systemcited by 1
  5. 2025Blackbox Dataset Inference for LLMcited by 1
  6. 2025Beyond Traditional Surveillance: Harnessing Expert Knowledge for Public Health Forecastingcited by 1
  7. 2025PHORECAST: Enabling AI Understanding of Public Health Outreach Across Populationscited by 1

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  3. 2026Med-CoReasoner: Reducing Language Disparities in Medical Reasoning via Language-Informed Co-Reasoning
  4. 2025Combining Evidence and Reasoning for Biomedical Fact-Checking
  5. 2026Medical Reasoning with Large Language Models: A Survey and MR-Bench
  6. 2025Disentangling Reasoning and Knowledge in Medical Large Language Models
  7. 2025MedOmni-45deg: A Safety-Performance Benchmark for Reasoning-Oriented LLMs in Medicine
  8. 2026Rethinking LLM Verification: Evidence Structure, Uncertainty, and Selective Refinement