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    content="Analyzing LLM misalignment in rheumatoid arthritis diagnosis. Our study shows that while LLMs predict disease accurately, their reasoning is often incorrect, as verified by medical experts.">

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  <meta property="og:title" content="Right Prediction, Wrong Reasoning: LLM Misalignment in RA Diagnosis" />
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    content="LLMs achieve high accuracy in rheumatoid arthritis diagnosis but fail in reasoning. Verified by medical experts, our research exposes a critical flaw in AI-driven healthcare.">
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            <h1 class="title is-1 publication-title">Right Prediction, Wrong Reasoning</h1>
            <div class="is-size-5 publication-authors">
              <!-- Paper authors -->
              <span class="author-block">
                <a href="https://umakantamaharana.github.io/" target="_blank">Umakanta Maharana</a><sup>1</sup>,</span>
              <span class="author-block">
                <a href="SECOND AUTHOR PERSONAL LINK" target="_blank">Sarthak Verma</a>,<sup>2</sup> </span>
              <span class="author-block">
                <a href="THIRD AUTHOR PERSONAL LINK" target="_blank">Avarna Agarwal</a><sup>2</sup>,
              </span>
              <span class="author-block">
                <a href="THIRD AUTHOR PERSONAL LINK" target="_blank">Prakashini Mruthyunjaya</a><sup>2</sup>,
              </span>
              <span class="author-block">
                <a href="THIRD AUTHOR PERSONAL LINK" target="_blank">Dwarikanath Mahapatra</a><sup>3</sup>,
              </span>
              <span class="author-block">
                <a href="https://drsakirsrheumatology.com/" target="_blank">Sakir Ahmed</a><sup>2</sup>,
              </span>
              <span class="author-block">
                <a href="https://murarimandal.github.io/" target="_blank">Murari Mandal</a><sup>1</sup>,
              </span>
            </div>

            <div class="is-size-5 publication-authors">
              <span class="author-block"><sup>1</sup><a href="https://respailab.github.io">RespAI Lab, KIIT Bhubaneswar</a><br><sup>2</sup>KIMS
                Bhubaneswar<br><sup>3</sup>Monash University, Australia</span>
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          <h2 class="title is-3">Abstract</h2>
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            <p>
              Large language models (LLMs) offer a promising pre-screening tool, improving early disease detection and
              providing enhanced healthcare access for underprivileged communities. The early diagnosis of various
              diseases continues to be a significant challenge in healthcare, primarily due to the nonspecific nature of
              early symptoms, the shortage of expert medical practitioners, and the need for prolonged clinical
              evaluations, all of which can delay treatment and adversely affect patient outcomes. With impressive
              accuracy in prediction across a range of diseases, LLMs have the potential to revolutionize clinical
              pre-screening and decision-making for various medical conditions. In this work, we study the diagnostic
              capability of LLMs for Rheumatoid Arthritis (RA) with real world patients data.
              Patient data was collected alongside diagnoses from medical experts, and the performance of LLMs was
              evaluated in comparison to expert diagnoses for RA disease prediction. We notice an interesting pattern in
              disease diagnosis and find an unexpected <i>misalignment between prediction and explanation</i>. We
              conduct a series of multi-round analyses using different LLM agents. The best-performing model accurately
              predicts rheumatoid arthritis (RA) diseases approximately 95% of the time. However, when medical experts
              evaluated the reasoning generated by the model, they found that nearly 68% of the reasoning was incorrect.
              This study highlights a clear misalignment between LLMs high prediction accuracy and its flawed reasoning,
              raising important questions about relying on LLM explanations in clinical settings. <b>LLMs provide
                incorrect reasoning to arrive at the correct answer for RA disease diagnosis.</b>

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            <h2 class="subtitle has-text-centered">
              Overview of the framework for RA patients diagnosis. We conduct experiments with four different
              architectures: LLM without knowledge base (from PreRAID), LLM+knowledge base+RAG, 2 LLM Agents+knowledge
              base+RAG, 3 LLM Agents+knowledge base+RAG. We store the final diagnosis and the reasoning tokens generated
              by each architecture.
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              Overview of the framework for RA patients diagnosis. We conduct experiments with four different
              architectures: LLM without knowledge base (from PreRAID), LLM+knowledge base+RAG, 2 LLM Agents+knowledge
              base+RAG, 3 LLM Agents+knowledge base+RAG. We store the final diagnosis and the reasoning tokens generated
              by each architecture.
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              Accuracy of disease prediction across all the models by providing all the knowledge bases. Orange, sky
              blue, deep blue and light blue color represents the accuracy in llm without RAG, single agent+RAG, 2
              agents+RAG, and 3 agents+RAG, respectively.
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              Reasoning performance across different LLM models in the 2 LLM agents+RAG setup. This figure compares the
              reasoning quality of various LLM models: GPT-3.5 Turbo, GPT-4o, GPT-4o-mini, Gemini 1.5 Flash, Gemini 2.0
              Flash, and QWEN-2-7B, within the 2 LLMagents+RAG framework.
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              Comparison of diagnostic accuracy and expert-rated reasoning ratings for Gemini 2.0 Flash and GPT-4o
              across four architectures: LLM without RAG, LLM+RAG, 2 LLM agents+RAG, and 3 LLM agents+RAG
            </h2>
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      <pre><code>
        @misc{maharana2025rightpredictionwrongreasoning,
          title={Right Prediction, Wrong Reasoning: Uncovering LLM Misalignment in RA Disease Diagnosis},
          author={Umakanta Maharana and Sarthak Verma and Avarna Agarwal and Prakashini Mruthyunjaya and Dwarikanath Mahapatra and Sakir Ahmed and Murari Mandal},
          year={2025},
          eprint={2504.06581},
          archivePrefix={arXiv},
          primaryClass={cs.AI},
          url={https://arxiv.org/abs/2504.06581},
    }
      </code></pre>
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