High Order Reasoning for Time Critical Recommendation in Evidence-based Medicine
On the map: LLM reasoning → Clinical and biomedical applications
Abstract (arXiv)
In time-critical decisions, human decision-makers can interact with AI-enabled situation-aware software to evaluate many imminent and possible scenarios, retrieve billions of facts, and estimate different outcomes based on trillions of parameters in a fraction of a second. In high-order reasoning, "what-if" questions can be used to challenge the assumptions or pre-conditions of the reasoning, "why-not" questions can be used to challenge on the method applied in the reasoning, "so-what" questions can be used to challenge the purpose of the decision, and "how-about" questions can be used to challenge the applicability of the method. When above high-order reasoning questions are applied to assist human decision-making, it can help humans to make time-critical decisions and avoid false-negative or false-positive types of errors. In this paper, we present a model of high-order reasoning to offer recommendations in evidence-based medicine in a time-critical fashion for the applications in ICU. The Large Language Model (LLM) is used in our system. The experiments demonstrated the LLM exhibited optimal performance in the "What-if" scenario, achieving a similarity of 88.52% with the treatment plans of human doctors. In the "Why-not" scenario, the best-performing model tended to opt for alternative treatment plans in 70% of cases for patients who died after being discharged from the ICU. In the "So-what" scenario, the optimal model provided a detailed analysis of the motivation and significance of treatment plans for ICU patients, with its reasoning achieving a similarity of 55.6% with actual diagnostic information. In the "How-about" scenario, the top-performing LLM demonstrated a content similarity of 66.5% in designing treatment plans transferring for similar diseases. Meanwhile, LLMs managed to predict the life status of patients after their discharge from the ICU with an accuracy of 70%.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning. Builds on 14 of its 46 references that are in the map. First posted on arXiv 5 May 2024.
Builds on
14 of 46 references.
References · 14
- 2017Attention Is All You Needcited by 60,361
- 2020Language Models are Few-Shot Learnerscited by 32,419
- 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
- 2022Large Language Models are Zero-Shot Reasonerscited by 4,374
- 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,780
- 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,234
- 2022Large Language Models Encode Clinical Knowledgecited by 1,830
- 2023Capabilities of GPT-4 on Medical Challenge Problemscited by 638
- 2023A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPTcited by 619
- 2021True Few-Shot Learning with Language Modelscited by 344
- 2023Better Zero-Shot Reasoning with Role-Play Promptingcited by 223
- 2023Regulating ChatGPT and other Large Generative AI Modelscited by 104
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2026Medical Reasoning with Large Language Models: A Survey and MR-Bench
- 2024Critique of Impure Reason: Unveiling the reasoning behaviour of medical Large Language Models
- 2022Can large language models reason about medical questions?
- 2026Advances in LLM Reasoning Enable Flexibility in Clinical Problem-Solving
- 2026Teaching LLMs How ICU Physicians Approach Clinical Reasoning Through OMOP-Aligned Retrieval Improves Reasoning Across Clinical Domains
- 2025Evaluating Large Language Models for Evidence-Based Clinical Question Answering
- 2025Reasoning LLMs in the Medical Domain: A Literature Survey
- 2026Can LLMs Reason Over Long Horizons? An Empirical Evaluation of Context Strategies for Longitudinal Clinical Reasoning