WiseMind: Recontextualizing AI with a Knowledge-Guided, Theory-Informed Multi-Agent Framework for Instrumental and Humanistic Benefits
- URL: http://arxiv.org/abs/2502.20689v2
- Date: Wed, 28 May 2025 07:27:05 GMT
- Title: WiseMind: Recontextualizing AI with a Knowledge-Guided, Theory-Informed Multi-Agent Framework for Instrumental and Humanistic Benefits
- Authors: Yuqi Wu, Guangya Wan, Jingjing Li, Shengming Zhao, Lingfeng Ma, Tianyi Ye, Ion Pop, Yanbo Zhang, Jie Chen,
- Abstract summary: WiseMind is an interdisciplinary contextualization framework for NLP.<n>Tested on depression, anxiety, and bipolar disorder, WiseMind attains up to 84.2% diagnostic accuracy.<n>Results show that deep contextualization-across knowledge, process, and evaluation layers-can transform benchmark-driven NLP into clinically meaningful impact.
- Score: 10.8749978349074
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Translating state-of-the-art NLP into practice often stalls at the "last mile" owing to insufficient contextualization of the target domain's knowledge, processes, and evaluation. Psychiatric differential diagnosis exemplifies this challenge: accurate assessments depend on nuanced clinical knowledge, a delicate cognitive-affective interview process, and downstream outcomes that extend far beyond benchmark accuracy. We present WiseMind, a systematic interdisciplinary contextualization framework that delivers both instrumental (diagnostic precision) and humanistic (empathy) gains. WiseMind comprises three components:(i) structured knowledge-guided proactive reasoning, which embeds DSM-5 criteria in a knowledge graph to steer questioning; (ii) a theory-informed dual-agent architecture that coordinates a "reasonable-mind" reasoning agent and an "emotional-mind" empathy agent, inspired by Dialectical Behavior Therapy; and (iii) a multi-faceted evaluation strategy covering simulated patients, user studies, clinician review, and ethical assessment. Tested on depression, anxiety, and bipolar disorder, WiseMind attains up to 84.2% diagnostic accuracy, which is comparable to human experts, while outperforming single-agent baselines in perceived empathy and trustworthiness. These results show that deep contextualization-across knowledge, process, and evaluation layers-can transform benchmark-driven NLP into clinically meaningful impact.
Related papers
- Medical Reasoning in the Era of LLMs: A Systematic Review of Enhancement Techniques and Applications [59.721265428780946]
Large Language Models (LLMs) in medicine have enabled impressive capabilities, yet a critical gap remains in their ability to perform systematic, transparent, and verifiable reasoning.<n>This paper provides the first systematic review of this emerging field.<n>We propose a taxonomy of reasoning enhancement techniques, categorized into training-time strategies and test-time mechanisms.
arXiv Detail & Related papers (2025-08-01T14:41:31Z) - A Retrieval-Augmented Multi-Agent Framework for Psychiatry Diagnosis [44.4032296111169]
MoodAngels is the first specialized multi-agent framework for mood disorder diagnosis.<n>MoodSyn is an open-source dataset of 1,173 synthetic psychiatric cases.
arXiv Detail & Related papers (2025-06-04T09:18:25Z) - A Risk Taxonomy for Evaluating AI-Powered Psychotherapy Agents [10.405048273969085]
We introduce a novel risk taxonomy specifically designed for the systematic evaluation of conversational AI psychotherapists.<n>We discuss two use cases in detail: monitoring cognitive model-based risk factors during a counseling conversation to detect unsafe deviations, and in automated benchmarking of AI psychotherapists with simulated patients.
arXiv Detail & Related papers (2025-05-21T05:01:39Z) - Two Experts Are All You Need for Steering Thinking: Reinforcing Cognitive Effort in MoE Reasoning Models Without Additional Training [86.70255651945602]
We introduce a novel inference-time steering methodology called Reinforcing Cognitive Experts (RICE)<n>RICE aims to improve reasoning performance without additional training or complexs.<n> Empirical evaluations with leading MoE-based LRMs demonstrate noticeable and consistent improvements in reasoning accuracy, cognitive efficiency, and cross-domain generalization.
arXiv Detail & Related papers (2025-05-20T17:59:16Z) - Ψ-Arena: Interactive Assessment and Optimization of LLM-based Psychological Counselors with Tripartite Feedback [51.26493826461026]
We propose Psi-Arena, an interactive framework for comprehensive assessment and optimization of large language models (LLMs)<n>Arena features realistic arena interactions that simulate real-world counseling through multi-stage dialogues with psychologically profiled NPC clients.<n>Experiments across eight state-of-the-art LLMs show significant performance variations in different real-world scenarios and evaluation perspectives.
arXiv Detail & Related papers (2025-05-06T08:22:51Z) - TRUST: An LLM-Based Dialogue System for Trauma Understanding and Structured Assessments [8.618945530676614]
This study aims to bridge the gap in mental healthcare accessibility by developing an LLM-powered dialogue system that replicates clinician behavior.
We introduce TRUST, a framework of cooperative LLM modules capable of conducting formal diagnostic interviews and assessments for PTSD.
We develop a patient simulation approach based on real-life interview transcripts to replace time-consuming and costly manual testing by clinicians.
arXiv Detail & Related papers (2025-04-30T17:58:06Z) - MAGI: Multi-Agent Guided Interview for Psychiatric Assessment [50.6150986786028]
We present MAGI, the first framework that transforms the gold-standard Mini International Neuropsychiatric Interview (MINI) into automatic computational navigation.
We show that MAGI advances LLM- assisted mental health assessment by combining clinical rigor, conversational adaptability, and explainable reasoning.
arXiv Detail & Related papers (2025-04-25T11:08:27Z) - A Desideratum for Conversational Agents: Capabilities, Challenges, and Future Directions [51.96890647837277]
Large Language Models (LLMs) have propelled conversational AI from traditional dialogue systems into sophisticated agents capable of autonomous actions, contextual awareness, and multi-turn interactions with users.
This survey paper presents a desideratum for next-generation Conversational Agents - what has been achieved, what challenges persist, and what must be done for more scalable systems that approach human-level intelligence.
arXiv Detail & Related papers (2025-04-07T21:01:25Z) - ProMRVL-CAD: Proactive Dialogue System with Multi-Round Vision-Language Interactions for Computer-Aided Diagnosis [0.7430974817507225]
We develop an LLM-based dialogue system, namely proactive multi-round vision-language interactions for computer-aided diagnosis (ProMRVL-CAD)<n>The proposed ProMRVL-CAD system allows proactive dialogue to provide patients with constant and reliable medical access via an integration of knowledge graph into a recommendation system.
arXiv Detail & Related papers (2025-02-15T01:14:23Z) - Dialogue is Better Than Monologue: Instructing Medical LLMs via Strategical Conversations [74.83732294523402]
We introduce a novel benchmark that simulates real-world diagnostic scenarios, integrating noise and difficulty levels aligned with USMLE standards.<n>We also explore dialogue-based fine-tuning, which transforms static datasets into conversational formats to better capture iterative reasoning processes.<n>Experiments show that dialogue-tuned models outperform traditional methods, with improvements of $9.64%$ in multi-round reasoning scenarios and $6.18%$ in accuracy in a noisy environment.
arXiv Detail & Related papers (2025-01-29T18:58:48Z) - A Multimodal Emotion Recognition System: Integrating Facial Expressions, Body Movement, Speech, and Spoken Language [0.0]
This work presents a multimodal emotion recognition system that provides a standardised, objective, and data-driven tool to support evaluators.<n>The system integrates recognition of facial expressions, speech, spoken language, and body movement analysis to capture subtle emotional cues that are often overlooked in human evaluations.
arXiv Detail & Related papers (2024-12-23T19:00:34Z) - Script-Based Dialog Policy Planning for LLM-Powered Conversational Agents: A Basic Architecture for an "AI Therapist" [0.0]
Large Language Model (LLM)-Powered Conversational Agents have the potential to provide users with scaled behavioral healthcare support.<n>We introduce a novel paradigm for dialog policy planning in conversational agents enabling them to act according to an expert-written "script"<n>We implement two variants of Script-Based Dialog Policy Planning using different prompting techniques and synthesize a total of 100 conversations with LLM-simulated patients.
arXiv Detail & Related papers (2024-12-13T12:12:47Z) - Towards Next-Generation Medical Agent: How o1 is Reshaping Decision-Making in Medical Scenarios [46.729092855387165]
We study the choice of the backbone LLM for medical AI agents, which is the foundation for the agent's overall reasoning and action generation.<n>Our findings demonstrate o1's ability to enhance diagnostic accuracy and consistency, paving the way for smarter, more responsive AI tools.
arXiv Detail & Related papers (2024-11-16T18:19:53Z) - Interactive Dialogue Agents via Reinforcement Learning on Hindsight Regenerations [58.65755268815283]
Many real dialogues are interactive, meaning an agent's utterances will influence their conversational partner, elicit information, or change their opinion.
We use this fact to rewrite and augment existing suboptimal data, and train via offline reinforcement learning (RL) an agent that outperforms both prompting and learning from unaltered human demonstrations.
Our results in a user study with real humans show that our approach greatly outperforms existing state-of-the-art dialogue agents.
arXiv Detail & Related papers (2024-11-07T21:37:51Z) - CBT-Bench: Evaluating Large Language Models on Assisting Cognitive Behavior Therapy [67.23830698947637]
We propose a new benchmark, CBT-BENCH, for the systematic evaluation of cognitive behavioral therapy (CBT) assistance.<n>We include three levels of tasks in CBT-BENCH: I: Basic CBT knowledge acquisition, with the task of multiple-choice questions; II: Cognitive model understanding, with the tasks of cognitive distortion classification, primary core belief classification, and fine-grained core belief classification; III: Therapeutic response generation, with the task of generating responses to patient speech in CBT therapy sessions.<n> Experimental results indicate that while LLMs perform well in reciting CBT knowledge, they fall short in complex real-world scenarios
arXiv Detail & Related papers (2024-10-17T04:52:57Z) - Depression Diagnosis Dialogue Simulation: Self-improving Psychiatrist with Tertiary Memory [35.41386783586689]
This paper introduces the Agent Mental Clinic (AMC), a self-improving conversational agent system designed to enhance depression diagnosis through simulated dialogues between patient and psychiatrist agents.
We design a psychiatrist agent consisting of a tertiary memory structure, a dialogue control and a memory sampling module, fully leveraging the skills reflected by the psychiatrist agent, achieving great accuracy on depression risk and suicide risk diagnosis via conversation.
arXiv Detail & Related papers (2024-09-20T14:25:08Z) - Cross-subject Brain Functional Connectivity Analysis for Multi-task Cognitive State Evaluation [16.198003101055264]
This study adopts brain functional connectivity with electroencephalography signals to capture associations in brain regions across multiple subjects for evaluating real-time cognitive states.
Thirty subjects are acquired for analysis and evaluation. The results are interpreted through different perspectives, including inner-subject and cross-subject for task-wise and gender-wise underlying brain functional connectivity.
arXiv Detail & Related papers (2024-08-27T12:51:59Z) - Dr-LLaVA: Visual Instruction Tuning with Symbolic Clinical Grounding [53.629132242389716]
Vision-Language Models (VLM) can support clinicians by analyzing medical images and engaging in natural language interactions.
VLMs often exhibit "hallucinogenic" behavior, generating textual outputs not grounded in contextual multimodal information.
We propose a new alignment algorithm that uses symbolic representations of clinical reasoning to ground VLMs in medical knowledge.
arXiv Detail & Related papers (2024-05-29T23:19:28Z) - Chain-of-Interaction: Enhancing Large Language Models for Psychiatric Behavior Understanding by Dyadic Contexts [4.403408362362806]
We introduce the Chain-of-Interaction prompting method to contextualize large language models for psychiatric decision support by the dyadic interactions.
This approach enables large language models to leverage the coding scheme, patient state, and domain knowledge for patient behavioral coding.
arXiv Detail & Related papers (2024-03-20T17:47:49Z) - Emotional Intelligence Through Artificial Intelligence : NLP and Deep Learning in the Analysis of Healthcare Texts [1.9374282535132377]
This manuscript presents a methodical examination of the utilization of Artificial Intelligence in the assessment of emotions in texts related to healthcare.
We scrutinize numerous research studies that employ AI to augment sentiment analysis, categorize emotions, and forecast patient outcomes.
There persist challenges, which encompass ensuring the ethical application of AI, safeguarding patient confidentiality, and addressing potential biases in algorithmic procedures.
arXiv Detail & Related papers (2024-03-14T15:58:13Z) - AI Hospital: Benchmarking Large Language Models in a Multi-agent Medical Interaction Simulator [69.51568871044454]
We introduce textbfAI Hospital, a framework simulating dynamic medical interactions between emphDoctor as player and NPCs.
This setup allows for realistic assessments of LLMs in clinical scenarios.
We develop the Multi-View Medical Evaluation benchmark, utilizing high-quality Chinese medical records and NPCs.
arXiv Detail & Related papers (2024-02-15T06:46:48Z) - Empowering Psychotherapy with Large Language Models: Cognitive
Distortion Detection through Diagnosis of Thought Prompting [82.64015366154884]
We study the task of cognitive distortion detection and propose the Diagnosis of Thought (DoT) prompting.
DoT performs diagnosis on the patient's speech via three stages: subjectivity assessment to separate the facts and the thoughts; contrastive reasoning to elicit the reasoning processes supporting and contradicting the thoughts; and schema analysis to summarize the cognition schemas.
Experiments demonstrate that DoT obtains significant improvements over ChatGPT for cognitive distortion detection, while generating high-quality rationales approved by human experts.
arXiv Detail & Related papers (2023-10-11T02:47:21Z)
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.