Demo Alleviate: Demonstrating Artificial Intelligence Enabled Virtual Assistance for Telehealth: The Mental Health Case

• URL: http://arxiv.org/abs/2304.00025v1
• Date: Fri, 31 Mar 2023 16:41:15 GMT
• Title: Demo Alleviate: Demonstrating Artificial Intelligence Enabled Virtual Assistance for Telehealth: The Mental Health Case
• Authors: Kaushik Roy, Vedant Khandelwal, Raxit Goswami, Nathan Dolbir, Jinendra Malekar, Amit Sheth
• Abstract summary: We propose Alleviate to assist patients with mental health challenges with personalized care and assist clinicians with understanding their patients better. Alleviate draws from an array of publicly available clinically valid mental-health texts and databases, allowing Alleviate to make medically sound and informed decisions. In this paper, we explain the different modules of Alleviate and submit a short video demonstrating Alleviate's capabilities to help patients and clinicians understand each other better.
• Score: 20.602347045884617
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: After the pandemic, artificial intelligence (AI) powered support for mental health care has become increasingly important. The breadth and complexity of significant challenges required to provide adequate care involve: (a) Personalized patient understanding, (b) Safety-constrained and medically validated chatbot patient interactions, and (c) Support for continued feedback-based refinements in design using chatbot-patient interactions. We propose Alleviate, a chatbot designed to assist patients suffering from mental health challenges with personalized care and assist clinicians with understanding their patients better. Alleviate draws from an array of publicly available clinically valid mental-health texts and databases, allowing Alleviate to make medically sound and informed decisions. In addition, Alleviate's modular design and explainable decision-making lends itself to robust and continued feedback-based refinements to its design. In this paper, we explain the different modules of Alleviate and submit a short video demonstrating Alleviate's capabilities to help patients and clinicians understand each other better to facilitate optimal care strategies.

Related papers

• 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)
• Yes, this is what I was looking for! Towards Multi-modal Medical Consultation Concern Summary Generation [46.42604861624895]
  We propose a new task of multi-modal medical concern summary generation. Nonverbal cues, such as patients' gestures and facial expressions, aid in accurately identifying patients' concerns. We construct the first multi-modal medical concern summary generation corpus.
  arXiv  Detail & Related papers  (2024-01-10T12:56:47Z)
• 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)
• PaniniQA: Enhancing Patient Education Through Interactive Question Answering [25.717709820995367]
  PaniniQA is a patient-centric interactive question answering system designed to help patients understand their discharge instructions. PaniniQA first identifies important clinical content from patients' discharge instructions and then formulates patient-specific educational questions.
  arXiv  Detail & Related papers  (2023-08-07T02:18:23Z)
• MentalHealthAI: Utilizing Personal Health Device Data to Optimize Psychiatry Treatment [1.696974372855528]
  Mental health disorders remain a significant challenge in modern healthcare, with diagnosis and treatment often relying on subjective patient descriptions and past medical history. We propose a personalized mental health tracking and mood prediction system that utilizes patient physiological data collected through personal health devices.
  arXiv  Detail & Related papers  (2023-07-09T22:30:47Z)
• What Do End-Users Really Want? Investigation of Human-Centered XAI for Mobile Health Apps [69.53730499849023]
  We present a user-centered persona concept to evaluate explainable AI (XAI) Results show that users' demographics and personality, as well as the type of explanation, impact explanation preferences. Our insights bring an interactive, human-centered XAI closer to practical application.
  arXiv  Detail & Related papers  (2022-10-07T12:51:27Z)
• VBridge: Connecting the Dots Between Features, Explanations, and Data for Healthcare Models [85.4333256782337]
  VBridge is a visual analytics tool that seamlessly incorporates machine learning explanations into clinicians' decision-making workflow. We identified three key challenges, including clinicians' unfamiliarity with ML features, lack of contextual information, and the need for cohort-level evidence. We demonstrated the effectiveness of VBridge through two case studies and expert interviews with four clinicians.
  arXiv  Detail & Related papers  (2021-08-04T17:34:13Z)
• Enabling AI and Robotic Coaches for Physical Rehabilitation Therapy: Iterative Design and Evaluation with Therapists and Post-Stroke Survivors [66.07833535962762]
  Artificial intelligence (AI) and robotic coaches promise the improved engagement of patients on rehabilitation exercises through social interaction. Previous work explored the potential of automatically monitoring exercises for AI and robotic coaches, but deployment remains a challenge. We present our efforts on eliciting the detailed design specifications on how AI and robotic coaches could interact with and guide patient's exercises.
  arXiv  Detail & Related papers  (2021-06-15T22:06:39Z)

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.