Enhancing Depression Diagnosis with Chain-of-Thought Prompting

• URL: http://arxiv.org/abs/2408.14053v2
• Date: Tue, 27 Aug 2024 08:05:07 GMT
• Title: Enhancing Depression Diagnosis with Chain-of-Thought Prompting
• Abstract summary: We theorize that using chain-of-thought (CoT) prompting to evaluate Patient Health Questionnaire-8 (PHQ-8) scores will improve the accuracy of the scores determined by AI models. Our goal is to expand upon AI models' understanding of the intricacies of human conversation, allowing them to more effectively assess a patient's feelings and tone.
• Score: 1.8532406942078647
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: When using AI to detect signs of depressive disorder, AI models habitually draw preemptive conclusions. We theorize that using chain-of-thought (CoT) prompting to evaluate Patient Health Questionnaire-8 (PHQ-8) scores will improve the accuracy of the scores determined by AI models. In our findings, when the models reasoned with CoT, the estimated PHQ-8 scores were consistently closer on average to the accepted true scores reported by each participant compared to when not using CoT. Our goal is to expand upon AI models' understanding of the intricacies of human conversation, allowing them to more effectively assess a patient's feelings and tone, therefore being able to more accurately discern mental disorder symptoms; ultimately, we hope to augment AI models' abilities, so that they can be widely accessible and used in the medical field.

Related papers

• BenchX: Benchmarking AI Models for Cancer Detection and Localization with Demographic and Protocol Biases [59.19934491064188]
  Artificial intelligence (AI) has achieved remarkable success in medical imaging.<n>These models often perform inconsistently across real-world clinical settings.<n>We develop a benchmark of 85,355 CT scans that systematically evaluates 12 tumor-detection AI models.
  arXiv  Detail & Related papers  (2026-06-23T17:58:59Z)
• Fine-tuning LLMs for Passive Depression Severity Estimation from AI Mental Health Dialogue [4.051184721197422]
  Depression is the leading cause of disability worldwide, and early detection of symptom change is essential for timely intervention.<n>We propose a passive, continuous symptom monitoring in AI mental health platforms without requiring users to complete self-report measures.
  arXiv  Detail & Related papers  (2026-06-16T14:28:47Z)
• Predicting Neuromodulation Outcome for Parkinson's Disease with Generative Virtual Brain Model [95.41752463487008]
  Parkinson's disease affects over ten million people worldwide.<n>Inter-individual variability limits empirical treatment selection, increasing non-negligible surgical risk and cost.<n>We bridge this gap with a pretraining-finetuning framework to predict outcomes directly from resting-state fMRI.
  arXiv  Detail & Related papers  (2026-03-31T02:36:10Z)
• Explainable AI as a Double-Edged Sword in Dermatology: The Impact on Clinicians versus The Public [46.86429592892395]
  explainable AI (XAI) addresses this by providing AI decision-making insight.<n>We present results from two large-scale experiments combining a fairness-based diagnosis AI model and different XAI explanations.
  arXiv  Detail & Related papers  (2025-12-14T00:06:06Z)
• MindEval: Benchmarking Language Models on Multi-turn Mental Health Support [10.524387723320432]
  MindEval is a framework for automatically evaluating language models in realistic, multi-turn mental health therapy conversations.<n>We quantitatively validate the realism of our simulated patients against human-generated text and by demonstrating strong correlations between automatic and human expert judgments.<n>We evaluate 12 state-of-the-art LLMs and show that all models struggle, scoring below 4 out of 6 on average, with particular weaknesses in problematic AI-specific patterns of communication.
  arXiv  Detail & Related papers  (2025-11-23T15:19:29Z)
• An Explainable Hybrid AI Framework for Enhanced Tuberculosis and Symptom Detection [55.35661671061754]
  Tuberculosis remains a critical global health issue, particularly in resource-limited and remote areas.<n>We propose a framework which enhances disease and symptom detection on chest X-rays by integrating two supervised heads and a self-supervised head.<n>Our model achieves an accuracy of 98.85% for distinguishing between COVID-19, tuberculosis, and normal cases, and a macro-F1 score of 90.09% for multilabel symptom detection.
  arXiv  Detail & Related papers  (2025-10-21T17:18:55Z)
• How to Evaluate Medical AI [4.23552814358972]
  We introduce Relative Precision and Recall of Algorithmic Diagnostics (RPAD and RRAD)<n>RPAD and RRAD compare AI outputs against multiple expert opinions rather than a single reference.<n>Large-scale study shows that top-performing models, such as DeepSeek-V3, achieve consistency on par with or exceeding expert consensus.
  arXiv  Detail & Related papers  (2025-09-15T14:01:22Z)
• DepressLLM: Interpretable domain-adapted language model for depression detection from real-world narratives [6.1211540596331755]
  This study introduces DepressLLM, trained and evaluated on a novel corpus of 3,699 autobiographical narratives reflecting both happiness and distress.<n>DepressLLM provides interpretable depression predictions and, via its Score-guided Token Probability Summation (SToPS) module, delivers both improved classification performance and reliable confidence estimates.
  arXiv  Detail & Related papers  (2025-08-12T03:12:55Z)
• Detecting PTSD in Clinical Interviews: A Comparative Analysis of NLP Methods and Large Language Models [6.916082619621498]
  Post-Traumatic Stress Disorder (PTSD) remains underdiagnosed in clinical settings.<n>This study evaluates natural language processing approaches for detecting PTSD from clinical interview transcripts.
  arXiv  Detail & Related papers  (2025-04-01T22:06:28Z)
• Structured Outputs Enable General-Purpose LLMs to be Medical Experts [50.02627258858336]
  Large language models (LLMs) often struggle with open-ended medical questions. We propose a novel approach utilizing structured medical reasoning. Our approach achieves the highest Factuality Score of 85.8, surpassing fine-tuned models.
  arXiv  Detail & Related papers  (2025-03-05T05:24:55Z)
• Ask Patients with Patience: Enabling LLMs for Human-Centric Medical Dialogue with Grounded Reasoning [5.520419627866446]
  We introduce Ask Patients with Patience (APP), the first multi-turn dialogue that enables LLMs to iteratively refine diagnoses based on grounded reasoning. APP achieves higher similarity scores in diagnosis predictions, demonstrating better alignment with ground truth diagnoses. APP also excels in user accessibility and empathy, further bridging the gap between complex medical language and user understanding.
  arXiv  Detail & Related papers  (2025-02-11T00:13:52Z)
• LlaMADRS: Prompting Large Language Models for Interview-Based Depression Assessment [75.44934940580112]
  This study introduces LlaMADRS, a novel framework leveraging open-source Large Language Models (LLMs) to automate depression severity assessment. We employ a zero-shot prompting strategy with carefully designed cues to guide the model in interpreting and scoring transcribed clinical interviews. Our approach, tested on 236 real-world interviews, demonstrates strong correlations with clinician assessments.
  arXiv  Detail & Related papers  (2025-01-07T08:49:04Z)
• Depression and Anxiety Prediction Using Deep Language Models and Transfer Learning [8.677511646372086]
  We explore deep language models for detecting depression, anxiety, and their co-occurrence from conversational speech collected during 16k user interactions with an application. We find that results for binary classification range from 0.86 to 0.79 AUC, depending on condition and co-occurrence.
  arXiv  Detail & Related papers  (2024-12-30T06:33:39Z)
• Depression Detection and Analysis using Large Language Models on Textual and Audio-Visual Modalities [25.305909441170993]
  Depression has proven to be a significant public health issue, profoundly affecting the psychological well-being of individuals. If it remains undiagnosed, depression can lead to severe health issues, which can manifest physically and even lead to suicide.
  arXiv  Detail & Related papers  (2024-07-08T17:00:51Z)
• Explainable AI Enhances Glaucoma Referrals, Yet the Human-AI Team Still Falls Short of the AI Alone [6.740852152639975]
  We investigate how various AI explanations help providers distinguish between patients needing immediate or non-urgent specialist referrals. We built explainable AI algorithms to predict glaucoma surgery needs from routine eyecare data as a proxy for identifying high-risk patients. We incorporated intrinsic and post-hoc explainability and conducted an online study with optometrists to assess human-AI team performance.
  arXiv  Detail & Related papers  (2024-05-24T03:01:20Z)
• Automatically measuring speech fluency in people with aphasia: first achievements using read-speech data [55.84746218227712]
  This study aims at assessing the relevance of a signalprocessingalgorithm, initially developed in the field of language acquisition, for the automatic measurement of speech fluency.
  arXiv  Detail & Related papers  (2023-08-09T07:51:40Z)
• Understanding the Effect of Counterfactual Explanations on Trust and Reliance on AI for Human-AI Collaborative Clinical Decision Making [5.381004207943597]
  We conducted an experiment with seven therapists and ten laypersons on the task of assessing post-stroke survivors' quality of motion. We analyzed their performance, agreement level on the task, and reliance on AI without and with two types of AI explanations. Our work discusses the potential of counterfactual explanations to better estimate the accuracy of an AI model and reduce over-reliance on wrong' AI outputs.
  arXiv  Detail & Related papers  (2023-08-08T16:23:46Z)
• TREEMENT: Interpretable Patient-Trial Matching via Personalized Dynamic Tree-Based Memory Network [54.332862955411656]
  Clinical trials are critical for drug development but often suffer from expensive and inefficient patient recruitment. In recent years, machine learning models have been proposed for speeding up patient recruitment via automatically matching patients with clinical trials. We introduce a dynamic tree-based memory network model named TREEMENT to provide accurate and interpretable patient trial matching.
  arXiv  Detail & Related papers  (2023-07-19T12:35:09Z)
• AI and Non AI Assessments for Dementia [11.5631890541199]
  Current progress in the artificial intelligence domain has led to the development of various types of AI-powered dementia assessments. This paper fills the gap in the literature in explaining the existing solutions for the recognition of dementia to clinicians. It follows a review of papers on AI and non-AI assessments for dementia to provide valuable information about various dementia assessments for both the AI and medical communities.
  arXiv  Detail & Related papers  (2023-06-30T03:28:47Z)
• The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies [97.5153823429076]
  The benefits, challenges and drawbacks of AI in this field are reviewed. The use of data augmentation, explainable AI, and the integration of AI with traditional experimental methods are also discussed.
  arXiv  Detail & Related papers  (2022-12-08T23:23:39Z)
• Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence [79.038671794961]
  We launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the AI model can be distributedly trained and independently executed at each host institution. Our study is based on 9,573 chest computed tomography scans (CTs) from 3,336 patients collected from 23 hospitals located in China and the UK.
  arXiv  Detail & Related papers  (2021-11-18T00:43:41Z)
• To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-making [4.877174544937129]
  People supported by AI-powered decision support tools frequently overrely on the AI. Adding explanations to the AI decisions does not appear to reduce the overreliance. Our research suggests that human cognitive motivation moderates the effectiveness of explainable AI solutions.
  arXiv  Detail & Related papers  (2021-02-19T00:38:53Z)
• Evaluation Toolkit For Robustness Testing Of Automatic Essay Scoring Systems [64.4896118325552]
  We evaluate the current state-of-the-art AES models using a model adversarial evaluation scheme and associated metrics. We find that AES models are highly overstable. Even heavy modifications(as much as 25%) with content unrelated to the topic of the questions do not decrease the score produced by the models.
  arXiv  Detail & Related papers  (2020-07-14T03:49:43Z)
• DeepEnroll: Patient-Trial Matching with Deep Embedding and Entailment Prediction [67.91606509226132]
  Clinical trials are essential for drug development but often suffer from expensive, inaccurate and insufficient patient recruitment. DeepEnroll is a cross-modal inference learning model to jointly encode enrollment criteria (tabular data) into a shared latent space for matching inference.
  arXiv  Detail & Related papers  (2020-01-22T17:51:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.