When AI Minds Get Fuzzy: The Limits of Clinical Reasoning

by

Author: Denis Avetisyan

New research reveals that artificial intelligence, despite vast medical knowledge, struggles to reliably interpret complex, real-world patient data.

This paper introduces the concept of AI-MASLD – AI-Metabolic Dysfunction-Associated Steatotic Liver Disease – to describe functional decline in large language models processing unstructured clinical narratives.

Despite advances in artificial intelligence, large language models (LLMs) still struggle with the complexities of real-world clinical data. This study, ‘AI-MASLD Metabolic Dysfunction and Information Steatosis of Large Language Models in Unstructured Clinical Narratives’, investigates the performance of leading LLMs when extracting information from noisy, unstructured clinical narratives, revealing a functional decline akin to metabolic dysfunction. We introduce the concept of “AI-MASLD” to describe this phenomenon, demonstrating that current LLMs exhibit significant limitations in translating theoretical knowledge into accurate clinical reasoning. Could this “information steatosis” necessitate a fundamental shift in how we deploy AI as a support-rather than a replacement-for human expertise in healthcare?

The Challenge of Clinical Complexity and LLM Performance

The escalating integration of Large Language Models into healthcare hinges on their capacity to interpret the nuanced language within patient records, which are often characterized by complexity and inconsistency. These clinical narratives, encompassing physician notes, discharge summaries, and radiology reports, present a unique challenge due to inherent ambiguities, redundancies, and variations in terminology. While LLMs demonstrate promise in automating tasks like information retrieval and summarization, their performance is significantly hampered by this ‘clinical noise’ – irrelevant details or imprecise phrasing that obscure critical information. Consequently, even advanced models struggle to consistently extract precise, actionable insights, raising concerns about their reliability in supporting clinical decision-making and potentially impacting patient outcomes. Addressing this challenge requires innovative approaches to pre-processing clinical text and enhancing LLM reasoning capabilities to effectively filter signal from noise.

Conventional natural language processing techniques frequently struggle when applied to clinical text due to the sheer volume of repetitive phrasing and extraneous information contained within patient reports. These systems often lack the nuanced understanding necessary to differentiate between essential medical details and commonplace descriptors, or to filter out redundant observations. Consequently, critical insights can become obscured, leading to inaccurate data extraction and potentially flawed interpretations. The prevalence of lengthy sentences, nested clauses, and verbose explanations-common in medical documentation-further exacerbates this challenge, demanding more sophisticated methods capable of pinpointing actionable intelligence amidst a sea of textual noise.

The effective application of Large Language Models in healthcare hinges on their ability to reliably extract meaningful data from complex clinical text, but inherent noise within these narratives presents a substantial challenge to diagnostic accuracy and, consequently, patient safety. Redundancy, irrelevant detail, and ambiguous phrasing can easily mislead an algorithm, potentially leading to incorrect interpretations and flawed clinical decision-making. This susceptibility to ‘noise’ underscores the critical need for evaluation metrics that move beyond simple accuracy scores and instead rigorously assess an LLM’s capacity to discern crucial information from extraneous content. Such metrics must effectively quantify a model’s robustness against realistic clinical variability and its ability to prioritize clinically relevant findings, ultimately ensuring responsible and trustworthy implementation of this technology in patient care.

Early evaluations of Large Language Models tasked with interpreting standardized medical inquiries demonstrated substantial performance variability. Scores on an inverse rating scale – designed to quantify accuracy and relevance – ranged dramatically, from a low of 16 out of 80 to a high of 32. This wide spread suggests that current models struggle to consistently extract meaningful insights from complex medical text, and that improvements in reasoning – the ability to discern critical information and synthesize it into a coherent understanding – are essential for reliable clinical application. The results emphasize the necessity for more rigorous benchmarking and the development of techniques that bolster an LLM’s capacity to navigate the nuances and ambiguities inherent in medical documentation.

AI-MASLD: A Framework for Understanding LLM Functional Decline

AI-MASLD, or AI-based Metabolic Dysfunction-Associated Steatotic Liver Disease, is a proposed conceptual framework for characterizing performance decline in Large Language Models (LLMs) when applied to complex medical information processing. This analogy draws from the human disease MASLD, where metabolic dysfunction leads to steatotic liver disease; similarly, AI-MASLD describes a functional impairment in LLMs arising from their handling of intricate medical data. The framework is intended to provide a structured way to understand and analyze limitations in LLM performance when faced with tasks requiring sophisticated reasoning and accurate medical assessment, rather than simply reflecting a lack of data or computational power.

AI-MASLD is characterized by two primary functional impairments: Information Steatosis and Algorithmic Fibrosis. Information Steatosis refers to the LLM’s generation of an excessive volume of factually correct, yet contextually irrelevant, data when processing complex medical queries. This contrasts with a lack of pertinent information. Algorithmic Fibrosis describes a diminished capacity for flexible risk assessment, resulting in overly rigid or inflexible categorization of patient risk profiles. Both phenomena contribute to decreased performance on tasks demanding nuanced reasoning and accurate prioritization, indicating a decline in the model’s ability to effectively synthesize and apply medical knowledge.

Cross-sectional analysis revealed that Large Language Models (LLMs) identified as exhibiting AI-MASLD demonstrated diminished performance on tasks demanding nuanced reasoning and accurate risk prioritization. These models consistently underperformed when required to synthesize complex medical information and differentiate between critical and non-critical factors. The observed difficulty manifested in an inability to effectively weigh probabilities, assess clinical significance, and formulate appropriate conclusions based on provided data, indicating a functional limitation in their analytical capabilities when confronted with realistic medical scenarios.

Cross-sectional analysis of LLM performance on complex medical tasks revealed a moderate average total score of 27 out of 80. However, significant functional decline was observed across models, with Qwen3-Max exhibiting the lowest score of 16/80. Gemini 2.5 achieved the highest score of 32/80, demonstrating a 16-point performance difference and illustrating the range of variability in LLM performance when processing nuanced medical information and prioritizing risks.

Assessing LLM Cognitive Abilities: Key Performance Indicators

The evaluation of Large Language Models (LLMs) focused on four distinct cognitive dimensions: Noise Filtering, Contradiction Detection, Emotion-Fact Separation, and Priority Triage. Noise Filtering assessed the model’s ability to discern relevant information from extraneous data. Contradiction Detection measured the LLM’s capacity to identify logically inconsistent statements within a given text. Emotion-Fact Separation evaluated the model’s skill in distinguishing subjective emotional expressions from objective factual claims. Finally, Priority Triage tested the LLM’s ability to rank information based on its importance or urgency, indicating a capacity for discerning critical data points.

Gemini 2.5 demonstrated the lowest overall performance among the evaluated Large Language Models (LLMs), achieving a cumulative score of 32 out of 80 on the inverse rating scale. This score represents the highest value on the scale, and thus indicates the weakest performance across all assessed cognitive dimensions – Noise Filtering, Contradiction Detection, Emotion-Fact Separation, and Priority Triage. The inverse scoring methodology was employed such that lower scores denote superior cognitive ability, with a perfect score of 0 representing optimal performance in all areas.

Qwen3-Max demonstrated the highest performance across assessed cognitive dimensions, achieving a total score of 16/80 on the inverse rating scale. This indicates superior capability in both Noise Filtering and Emotion-Fact Separation compared to other evaluated LLMs. Noise Filtering assesses the model’s ability to disregard irrelevant or distracting information, while Emotion-Fact Separation measures the capacity to distinguish objective factual statements from subjective emotional expressions. The comparatively low score signifies a stronger ability to accurately process and interpret information without being unduly influenced by extraneous data or emotional content.

Evaluation of DeepSeek 3.1 and GPT-4o on Contradiction Detection yielded an average score of 21/80 for DeepSeek 3.1, compared to 25/80 for GPT-4o; this 4-point difference on the inverse rating scale indicates that DeepSeek 3.1 demonstrated a statistically significant improvement in identifying contradictory statements within provided text. This suggests a more robust reasoning process, as accurate contradiction detection requires a model to not only understand individual statements but also to synthesize information and identify logical inconsistencies.

Mitigating AI-MASLD: A Path Towards Robust LLMs

Data Diet Control, as applied to Large Language Model (LLM) training, involves shifting the composition of training datasets to prioritize authentic, unstructured clinical data sources – such as physician notes, pathology reports, and radiology transcripts – over curated or synthetic data. Research indicates this approach mitigates AI-MASLD (AI-Mediated Algorithmic Symptom-Level Deterioration) by exposing the LLM to the complexities and nuances inherent in real-world clinical documentation. Specifically, the variability in language, formatting, and data completeness present in unstructured data forces the model to develop more robust generalization capabilities, reducing its reliance on patterns derived from potentially biased or oversimplified datasets. This ultimately improves the LLM’s ability to accurately interpret and process clinical information, leading to more reliable performance and reduced symptom amplification.

Reinforcement Learning from Human Feedback (RLHF) offers a method for refining Large Language Models (LLMs) to better identify and prioritize clinically significant warning symptoms. This process involves training the LLM using human feedback on its responses, specifically rewarding outputs that accurately highlight critical indicators of disease progression and penalizing those that omit or downplay them. By iteratively refining the model’s reward function through human evaluation, RLHF enables improved risk stratification; the LLM learns to more accurately categorize patients based on the severity and likelihood of adverse outcomes. This targeted training focuses the LLM’s attention on features most relevant to clinical decision-making, leading to more reliable and actionable insights.

Enhancing the ability of Large Language Models (LLMs) to accurately construct a Timeline Sorting of disease progression is a primary objective of our interventions. This involves improving the LLM’s capacity to correctly sequence events related to a patient’s medical history, from initial symptoms and diagnoses to treatment responses and disease stage. Accurate timeline construction is critical for differential diagnosis, predicting future health states, and personalizing treatment plans. The proposed Data Diet Control and Reinforcement Learning from Human Feedback (RLHF) strategies are specifically designed to improve the LLM’s understanding of temporal relationships within clinical data, enabling it to generate more coherent and clinically relevant disease progression timelines. Successful implementation is measured by improved accuracy in sequencing events and a reduction in diagnostic errors related to temporal misinterpretation.

Implementation of Data Diet Control and Reinforcement Learning from Human Feedback (RLHF) strategies aims to significantly improve the clinical utility of Large Language Models (LLMs). Current LLM performance, as measured by a composite scoring system, averages 27 out of 80 points; these interventions are projected to reduce this average score to below 20/80. This improvement indicates a heightened capacity for accurate and timely clinical support, achieved through enhanced disease progression timeline construction and more effective prioritization of critical warning symptoms. The anticipated reduction in scoring reflects increased reliability and robustness in LLM-driven clinical assessments and decision-making processes.

The study illuminates a critical failing within current Large Language Models: a disconnect between possessing vast knowledge and effectively applying it to nuanced, real-world clinical data. This echoes a sentiment articulated by John McCarthy: “It is better to solve one problem completely than to solve many problems incompletely.” The paper’s introduction of AI-MASLD-the functional decline stemming from processing unstructured clinical narratives-demonstrates precisely this. The models, despite their theoretical understanding, struggle with the ‘messiness’ of actual patient data. The research champions a focus on refining practical application-achieving complete solutions within specific domains-rather than pursuing broad, yet ultimately incomplete, capabilities. The emphasis isn’t simply on more data, but on extracting meaningful information, a principle of focused clarity.

Where Do We Go From Here?

The invocation of AI-MASLD – a deliberately cumbersome label, perhaps – serves not to diagnose a failing, but to name a limitation. The field had begun to assume fluency where only potential resided. These models, demonstrably capable of regurgitating medical knowledge, stumble when faced with the messy vitality of actual clinical notes. They built a framework to hide the panic, a probabilistic gloss over fundamental incomprehension. The real work, it seems, lies not in scaling parameters, but in acknowledging the irreducible complexity of human expression.

Future efforts would be better spent not on chasing ever-larger models, but on methods for responsible reduction. How can one distill the essence of a patient’s story without losing the crucial, often subtle, signals embedded within the narrative? The emphasis should shift from information extraction to information curation – a process of careful selection, rather than exhaustive capture. A smaller model, trained on meticulously prepared data, might prove far more valuable than a behemoth drowning in noise.

Ultimately, the challenge is not to create artificial doctors, but to build tools that augment, not replace, human reasoning. The pursuit of perfect simulation is a fool’s errand. Clarity, after all, is not achieved by adding layers of abstraction, but by stripping them away. Perhaps the most fruitful path forward involves embracing a little elegant simplicity.

Original article: https://arxiv.org/pdf/2512.11544.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

See also:

2025-12-15 09:31