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Leveraging Evidence-Guided LLMs to Enhance Trustworthy Depression Diagnosis

arXiv 2511.17947 ↗First posted 22 Nov 2025cs.AI cs.IRcited by 0

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Abstract (arXiv)

Large language models (LLMs) show promise in automating clinical diagnosis, yet their non-transparent decision-making and limited alignment with diagnostic standards hinder trust and clinical adoption. We address this challenge by proposing a two-stage diagnostic framework that enhances transparency, trustworthiness, and reliability. First, we introduce Evidence-Guided Diagnostic Reasoning (EGDR), which guides LLMs to generate structured diagnostic hypotheses by interleaving evidence extraction with logical reasoning grounded in DSM-5 criteria. Second, we propose a Diagnosis Confidence Scoring (DCS) module that evaluates the factual accuracy and logical consistency of generated diagnoses through two interpretable metrics: the Knowledge Attribution Score (KAS) and the Logic Consistency Score (LCS). Evaluated on the D4 dataset with pseudo-labels, EGDR outperforms direct in-context prompting and Chain-of-Thought (CoT) across five LLMs. For instance, on OpenBioLLM, EGDR improves accuracy from 0.31 (Direct) to 0.76 and increases DCS from 0.50 to 0.67. On MedLlama, DCS rises from 0.58 (CoT) to 0.77. Overall, EGDR yields up to +45% accuracy and +36% DCS gains over baseline methods, offering a clinically grounded, interpretable foundation for trustworthy AI-assisted diagnosis.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 33 references that are in the map. First posted on arXiv 22 Nov 2025.

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References · 11

  1. 2023Diagnostic Reasoning Prompts Reveal the Potential for Large Language Model Interpretability in Medicinecited by 83
  2. 2024The opportunities and risks of large language models in mental healthcited by 54
  3. 2023Explainable Claim Verification via Knowledge-Grounded Reasoning with Large Language Modelscited by 37
  4. 2023Large Language Models are Clinical Reasoners: Reasoning-Aware Diagnosis Framework with Prompt-Generated Rationalescited by 33
  5. 2024Few shot chain-of-thought driven reasoning to prompt LLMs for open ended medical question answeringcited by 31
  6. 2024Claim Verification in the Age of Large Language Models: A Surveycited by 24
  7. 2023Large Language Models Perform Diagnostic Reasoningcited by 18
  8. 2024ClaimVer: Explainable Claim-Level Verification and Evidence Attribution of Text Through Knowledge Graphscited by 11
  9. 2023MDDial: A Multi-turn Differential Diagnosis Dialogue Dataset with Reliability Evaluationcited by 10
  10. 2022Retrieval-Augmented and Knowledge-Grounded Language Models for Faithful Clinical Medicinecited by 4
  11. 2025Causal Machine Learning for Surgical Interventionscited by 1

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  1. 2026Dep-LLM: Training-Free Depression Diagnosis via Evidence-Guided Structured Multi-factor with Reliable LLM Reasoning
  2. 2025Standardization of Psychiatric Diagnoses -- Role of Fine-tuned LLM Consortium and OpenAI-gpt-oss Reasoning LLM Enabled Decision Support System
  3. 2025MDD-Thinker: Towards Large Reasoning Models for Major Depressive Disorder Diagnosis
  4. 2026Ask Before You Diagnose: Safe-Psych, a Sequential Evaluation Benchmark for LLMs in Psychiatry
  5. 2026CLExEval: A Human-in-the-Loop Framework for Qualitative Evaluation of LLM Clinical Reasoning
  6. 2026Towards Reliable Medical LLMs: Benchmarking and Enhancing Confidence Estimation of Large Language Models in Medical Consultation
  7. 2025LLAMADRS: Evaluating Open-Source LLMs on Real Clinical Interviews--To Reason or Not to Reason?
  8. 2026From Answers to Arguments: Toward Trustworthy Clinical Diagnostic Reasoning with Toulmin-Guided Curriculum Goal-Conditioned Learning