Uncertainty Reasoning with Large Language Models for Explainable Disease Diagnosis
Xiaoyang Fan, Yufan Cai, Zhe Hou, Jin Song Dong
Abstract
Clinical decision-making requires reasoning over incomplete, imprecise, and linguistically expressed patient narratives. While large language models (LLMs) excel at extracting latent information from natural language, they lack the verifiability and interpretability essential for trustworthy medical AI. We propose a neuro-symbolic reasoning framework that aligns LLMs with formal logic to enable explainable and formally verifiable medical diagnosis. Patient descriptions and clinical guidelines are embedded into a neural knowledge base, where LLMs extract structured medical entities, temporal relations, and fuzzy symptom patterns, which are decoded into a symbolic knowledge base expressed in fuzzy logic and declarative rules. We perform two-stage reasoning: (1) inductive symbolic generalization to capture diagnostic patterns from encoded narratives, and (2) inference verification via a logic programming engine to derive and validate diagnoses consistent with clinical standards. Each symptom is treated as a fuzzy predicate with probabilistic weights, and inference paths are auditable, adjustable, and compatible with physician feedback. Unlike purely statistical methods, our system supports iterative refinement: misalignment between LLM-generated diagnoses and ground truth can be traced, explained, and corrected through formal rules. By combining logic-based transparency, LLM adaptability, and probabilistic robustness, the framework enables human-aligned healthcare inference with strong generalization and verifiable, step-by-step reasoning chains. We validate our framework on public benchmarks, demonstrating effective reconciliation of symbolic reasoning and LLMs with real-world clinical narratives. Results show performance comparable to state-of-the-art LLMs, while additionally providing interpretable reasoning paths and formally verifiable diagnostic conclusions.
AI Impact Assessments
(1 models)Scientific Impact Assessment
Core Contribution
This paper proposes a neuro-symbolic framework that couples LLM-based extraction with fuzzy logic and Prolog-based symbolic reasoning for clinical diagnosis. The pipeline works as follows: (1) LLMs extract structured medical entities from free-text clinical notes, (2) these are converted into fuzzy predicates with probabilistic weights, (3) symbolic rules (Horn clauses) are compiled from clinical guidelines, (4) a Prolog-based engine performs weighted reasoning to rank diagnoses, and (5) an update mechanism allows both physician-driven rule editing and automated passive-aggressive weight updates. The key claimed novelty is the integration of fuzzy quantification, probabilistic inference, and a physician-in-the-loop feedback cycle within a single auditable pipeline.
Methodological Rigor
The methodology has several concerning weaknesses:
Evaluation design: The experimental evaluation is limited in scope and rigor. The primary comparison is against raw LLM prompting (GPT-4o, o4-mini, DeepSeek-R1), which is a weak baseline for a neuro-symbolic system paper. There is no comparison against established neuro-symbolic baselines (DeepProbLog, Logic Tensor Networks, Neural Theorem Provers—all cited in related work), nor against standard clinical NLP systems or ensemble methods. This makes it impossible to assess where the contribution stands relative to the actual state of the art in neuro-symbolic reasoning.
Dataset concerns: The first dataset (symptom_to_diagnosis) is procedurally generated, limiting its clinical validity. The iCliniq dataset is noisy medical Q&A data, and the authors acknowledge multiple data quality issues—then create a "trimmed" version that conveniently improves their numbers. This post-hoc data cleaning raises methodological red flags. The MIMIC-IV evaluation is more credible but details about the specific subset used, preprocessing, and ground truth extraction are sparse.
Performance claims: On iCliniq, the full hybrid system underperforms GPT-4o across all metrics. On the trimmed version, GPT-4o still wins. Only on MIMIC-IV does the hybrid system clearly outperform, and the paper attributes this to "clearer symptom signals"—which somewhat undermines the claim that the system handles uncertainty well. The improvements over symbolic-only baselines validate the fuzzy/probabilistic components but are expected given the additional information they incorporate.
Explainability evaluation: The "consistency check" uses GPT-4o to evaluate explainability scores, which is circular and methodologically weak—using an LLM to judge LLM-derived explanations. No human clinician evaluation of reasoning quality is reported, despite the paper's emphasis on physician-in-the-loop design. The error rate metric for symptom extraction is also evaluated by GPT-4o rather than clinical experts.
Formal verification claims: Despite prominent claims about "formally verifiable" diagnosis, no formal verification is actually demonstrated. The Prolog engine performs inference, but standard Prolog execution is not formal verification in the sense used in the formal methods community. No properties are formally specified or verified.
Potential Impact
The general direction—combining LLM flexibility with symbolic interpretability for clinical AI—is important and practically relevant. Healthcare settings genuinely need explainable, auditable AI systems. The physician feedback loop and versioned knowledge base are practically useful design choices. However, the current implementation is too preliminary to have significant real-world impact:
Timeliness & Relevance
The paper addresses a timely concern: LLM deployment in healthcare without adequate explainability and verification. The intersection of neuro-symbolic AI and clinical decision support is an active and important research area. However, the specific technical contributions (fuzzy Prolog reasoning, passive-aggressive weight updates) are relatively standard techniques being applied in combination rather than novel methodological advances.
Strengths
1. Well-motivated problem: The motivating example in Section 2 effectively illustrates the clinical need and system capabilities
2. Complete pipeline design: The end-to-end architecture from text to diagnosis with feedback loops is comprehensive
3. Ablation studies: The three ablation variants (symbolic-only, +probabilistic, +fuzzy) provide useful insights about component contributions
4. Cost analysis: The token usage and cost comparison adds practical value
5. Multi-dataset evaluation: Testing on three datasets with different characteristics provides some breadth
Limitations
1. No comparison with neuro-symbolic baselines: A critical omission that undermines the contribution claims
2. No human evaluation: For a paper centered on explainability and physician feedback, the absence of clinician evaluation is a major gap
3. Overstated formal verification claims: The paper repeatedly claims "formally verifiable" reasoning but provides no formal verification
4. Weak baselines: Comparing only against vanilla LLM prompting sets a low bar
5. Data cleaning concerns: The trimmed dataset approach appears to selectively remove cases where the system performs poorly
6. Reproducibility: Key implementation details are missing (prompt templates, specific Prolog rules, FAISS index construction details, hyperparameter selection rationale)
7. Limited novelty: Individual components (fuzzy logic, Prolog reasoning, LLM extraction, passive-aggressive updates) are all well-established; the integration, while useful, is primarily engineering
8. Static symptom modeling acknowledged: The authors note inability to handle temporal evolution, which is critical for clinical reasoning
Overall Assessment
This paper presents a reasonable engineering contribution in combining LLMs with fuzzy symbolic reasoning for clinical diagnosis. The direction is sound and practically motivated. However, the evaluation falls short of establishing the claimed contributions: formal verification is not demonstrated, explainability is not rigorously evaluated, and comparisons against relevant neuro-symbolic baselines are absent. The experimental results show the hybrid system performs comparably to—but generally not better than—GPT-4o alone, with the added value being interpretability that is never rigorously assessed by domain experts. The paper reads more as a system description with preliminary experiments than as a rigorous scientific contribution with validated claims.
Generated May 26, 2026
Comparison History (20)
Paper 2 addresses a foundational methodological issue in how LLM confidence and calibration are measured. While Paper 1 presents a valuable neuro-symbolic framework for medical AI, Paper 2's insights into evaluation protocol sensitivity will broadly impact foundational LLM research, uncertainty quantification, and AI safety across all domains, likely driving widespread adoption of its reporting checklist.
Paper 2 has higher potential impact because it provides a unifying formal framing (LLM Tree-of-Thoughts as classical heuristic search) that can generalize across many tasks, models, and domains, offering reusable taxonomy and design patterns and directly connecting NLP and automated planning communities. This breadth and timeliness (rapidly growing ToT/search-over-reasoning area) suggest wide downstream influence. Paper 1 is valuable and application-critical, but its contributions are more domain-specific (clinical diagnosis) and likely face higher deployment/data/regulatory constraints; impact may be strong but narrower.
Paper 1 offers higher potential scientific impact because it addresses a critical bottleneck in medical AI: the lack of verifiable and interpretable reasoning in LLMs. By introducing a neuro-symbolic framework combining fuzzy logic with LLMs, it provides a high-stakes real-world application (clinical diagnosis) with rigorous, auditable inference paths. While Paper 2 presents a valuable methodological improvement for RAG systems, Paper 1 tackles a deeply impactful, life-critical domain where solving the transparency and hallucination problems of LLMs can fundamentally transform clinical decision-making.
Paper 2 likely has higher impact due to a more novel, broadly applicable contribution: a learnable (RL-trained) policy for skill extraction and skill-bank maintenance from agent trajectories, validated on widely used, high-signal coding benchmarks with verifiable execution rewards. This targets a timely and fast-moving area (agentic LLMs, self-improvement, long-horizon software tasks) with clear real-world applicability. Paper 1 is valuable for trustworthy medical AI, but similar neuro-symbolic/LLM+logic directions are already crowded and clinical deployment faces higher barriers; reported gains are mainly interpretability at comparable accuracy.
Paper 1 offers a highly timely and innovative integration of formal methods with AI safety. By translating natural-language policies into First-Order Logic, it transforms LLM safety evaluation from ad-hoc red-teaming into a rigorous, coverage-driven software testing paradigm. This methodological leap addresses a critical bottleneck in deploying safe AI systems across all domains. While Paper 2 presents a valuable neuro-symbolic approach for healthcare, Paper 1's framework is fundamentally more broadly applicable to the entire foundation model ecosystem and tackles the urgent, cross-disciplinary challenge of verifiable AI alignment.
Paper 2 addresses a critical bottleneck in medical AI—verifiability and interpretability—by integrating LLMs with neuro-symbolic fuzzy logic. While Paper 1 offers strong algorithmic improvements for AI agents, Paper 2's focus on high-stakes clinical decision-making gives it a higher potential for transformative real-world impact and cross-disciplinary scientific significance in both computer science and healthcare.
Paper 1 demonstrates higher scientific impact due to its massive scale (2.8M ECGs, 1.3M patients), rigorous evaluation across 9 external cohorts and 89 downstream tasks, and direct clinical applicability to routine ECG interpretation. The ECGCLIP framework addresses a concrete clinical need—expanding ECG utility beyond common arrhythmias to rare diseases and echocardiographic screening—with strong generalization evidence. Paper 2 presents an interesting neuro-symbolic framework but offers only benchmark-comparable performance without clear advantages over existing LLMs, and lacks the clinical validation scale and real-world deployment potential of Paper 1.
Paper 1 likely has higher impact due to a concrete, scalable solution to a key bottleneck (verifiable RL training data for computer-use agents), with sizable artifacts (32k verified tuples, 110 environments, synthesized app hub) and strong benchmark gains plus demonstrated transfer. The co-generation pipeline for tasks/environments/rewards and planned open-sourcing can broadly accelerate CUA/RLVR research across agents, tooling, and UI automation. Paper 2 targets an important domain, but the abstract suggests a more incremental neuro-symbolic/fuzzy-logic integration with performance merely comparable to SOTA and fewer clearly specified methodological/benchmark innovations, limiting expected breadth and adoption.
Paper 2 addresses a critical bottleneck in medical AI—trustworthiness and explainability—by combining LLMs with neuro-symbolic reasoning and fuzzy logic. Its application in clinical decision-making offers profound real-world impact and addresses urgent societal needs. While Paper 1 introduces an innovative benchmark for GUI agents, Paper 2's methodological rigor in formal logic and its potential to safely integrate AI into healthcare systems give it a broader and more significant scientific and societal impact.
Paper 2 likely has higher scientific impact due to broader applicability and timeliness: improving reasoning efficiency in LLM post-training is widely relevant across domains and can be adopted by many model builders. CLORE’s content-level editing plus reference-free DPO is a clear methodological contribution with demonstrated gains on multiple benchmarks and compatibility with existing RL methods, supporting practical uptake. Paper 1 addresses an important healthcare need and offers interpretability/verification, but its impact may be narrower, harder to validate clinically, and more dependent on domain-specific integration and regulatory constraints.
Paper 2 establishes a fundamental theoretical impossibility result (a quadrilemma) about AI explainability that has broad implications across all of AI, not just one application domain. Such foundational impossibility theorems (akin to the No Free Lunch theorem or Arrow's impossibility theorem) tend to have outsized and lasting scientific impact because they reshape how entire research communities frame problems. It directly informs AI governance policy and redirects explainability research efforts. Paper 1, while valuable, presents an incremental engineering contribution combining known techniques (LLMs, fuzzy logic, symbolic reasoning) for a specific medical diagnosis application, with performance only comparable to existing methods.
Paper 1 addresses foundational challenges in multi-agent LLM systems—error propagation and token cost. Its broad applicability across various AI domains, combined with highly significant quantitative improvements (up to 30.7% better accuracy and 6.5x token reduction), gives it a wider potential scientific and practical impact compared to the domain-specific, albeit important, healthcare focus of Paper 2.
Paper 2 addresses a highly critical bottleneck in medical AI: the lack of interpretability and verifiability in LLMs. By combining LLMs with neuro-symbolic reasoning and fuzzy logic, it offers a broad, scalable solution for trustworthy clinical decision-making. While Paper 1 presents significant methodological advances in fMRI decoding, Paper 2 has broader potential real-world applications and cross-disciplinary impact in the rapidly growing field of safe, explainable healthcare AI.
Paper 1 demonstrates a novel, well-evaluated methodology (property-guided LLM synthesis with counterexample feedback) that yields dramatic concrete improvements: 7x fewer program generations and orders-of-magnitude less computation. It introduces a generalizable paradigm applicable beyond planning. Paper 2 combines known components (neuro-symbolic reasoning, fuzzy logic, LLMs) in a relatively incremental way for medical diagnosis, achieving only comparable performance to existing LLMs while adding interpretability. Paper 1's clear quantitative gains, broader methodological contribution, and potential to reshape LLM-based program synthesis give it higher impact.
Paper 1 addresses a fundamental challenge in medical AI—combining LLM capabilities with formal, verifiable reasoning for clinical diagnosis. It proposes a novel neuro-symbolic framework integrating fuzzy logic, symbolic reasoning, and LLMs, with broad implications for trustworthy AI in healthcare. The methodological contribution (two-stage reasoning with auditable inference paths) is more innovative and has wider cross-disciplinary impact (AI, medicine, formal methods). Paper 2, while practical, is a narrower engineering contribution focused on code translation for geospatial workflows, with limited generalizability beyond its specific domain.
Paper 1 introduces a highly novel, generalizable text-space optimizer for AI agents, drawing a strong parallel to deep learning weight optimization. Its extensive methodological rigor, demonstrated across numerous benchmarks and state-of-the-art models, ensures broad applicability and impact across the entire field of autonomous agents. While Paper 2 offers valuable contributions to medical AI interpretability, Paper 1's foundational approach to agent self-evolution will likely influence a wider array of disciplines and future AI architectures.
Paper 1 identifies a fundamental and counterintuitive failure mode of LLMs—inverse scaling on forecasting tasks with superlinear growth and tail risk—with broad implications across AI safety, finance, epidemiology, and LLM evaluation methodology. It introduces a new benchmark, provides mechanistic explanations via per-quantile decomposition, and challenges prevailing assumptions that more capable models are uniformly better. This finding has wide-reaching consequences for how LLM forecasting is evaluated and deployed. Paper 2 presents a useful but more incremental neuro-symbolic framework for clinical diagnosis that achieves performance comparable to (not exceeding) existing methods, limiting its impact.
Paper 1 likely has higher impact due to a more novel neuro-symbolic integration (LLMs + fuzzy logic + formal verification) addressing a high-stakes, regulation-sensitive domain (clinical diagnosis) where interpretability and verifiability are critical. Its methodological contribution (auditable reasoning chains, rule-based correction loops) can generalize to other safety-critical decision systems and aligns with timely needs in trustworthy AI. Paper 2 is useful and well-motivated for LLM-assisted qualitative analysis, but its multi-agent peer-debriefing refinement is more incremental and narrower in cross-field applicability.
Paper 2 addresses a critical real-world problem—explainable and verifiable medical diagnosis—combining LLMs with formal logic in a neuro-symbolic framework. Its impact spans AI, healthcare, and clinical decision support, with direct patient safety implications. The interpretability and auditability features address key barriers to deploying AI in medicine. Paper 1, while methodologically interesting for benchmarking LLM strategic reasoning, serves a narrower community (AI evaluation/game theory) and its procedurally generated card games, though clever, have less immediate real-world applicability compared to clinical diagnosis systems.
Paper 2 addresses the critical intersection of LLMs, explainability, and clinical decision-making—a high-stakes domain with broad societal impact. Its neuro-symbolic framework combining fuzzy logic with LLMs offers methodological novelty applicable beyond medicine. The focus on verifiability and interpretability in medical AI is timely given regulatory demands. While Paper 1 provides a valuable benchmark for urban computing, benchmarks typically have narrower impact than novel methodological frameworks. Paper 2's cross-disciplinary relevance (AI, medicine, formal logic) and alignment with urgent trustworthy AI needs give it higher potential impact.