Foundation Metrics for Evaluating Effectiveness of Healthcare Conversations Powered by Generative AI

• URL: http://arxiv.org/abs/2309.12444v3
• Date: Wed, 28 Feb 2024 20:15:54 GMT
• Title: Foundation Metrics for Evaluating Effectiveness of Healthcare Conversations Powered by Generative AI
• Authors: Mahyar Abbasian, Elahe Khatibi, Iman Azimi, David Oniani, Zahra Shakeri Hossein Abad, Alexander Thieme, Ram Sriram, Zhongqi Yang, Yanshan Wang, Bryant Lin, Olivier Gevaert, Li-Jia Li, Ramesh Jain, Amir M. Rahmani
• Abstract summary: Generative Artificial Intelligence is set to revolutionize healthcare delivery by transforming traditional patient care into a more personalized, efficient, and proactive process. This paper explores state-of-the-art evaluation metrics that are specifically applicable to the assessment of interactive conversational models in healthcare.
• Score: 38.497288024393065
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generative Artificial Intelligence is set to revolutionize healthcare delivery by transforming traditional patient care into a more personalized, efficient, and proactive process. Chatbots, serving as interactive conversational models, will probably drive this patient-centered transformation in healthcare. Through the provision of various services, including diagnosis, personalized lifestyle recommendations, and mental health support, the objective is to substantially augment patient health outcomes, all the while mitigating the workload burden on healthcare providers. The life-critical nature of healthcare applications necessitates establishing a unified and comprehensive set of evaluation metrics for conversational models. Existing evaluation metrics proposed for various generic large language models (LLMs) demonstrate a lack of comprehension regarding medical and health concepts and their significance in promoting patients' well-being. Moreover, these metrics neglect pivotal user-centered aspects, including trust-building, ethics, personalization, empathy, user comprehension, and emotional support. The purpose of this paper is to explore state-of-the-art LLM-based evaluation metrics that are specifically applicable to the assessment of interactive conversational models in healthcare. Subsequently, we present an comprehensive set of evaluation metrics designed to thoroughly assess the performance of healthcare chatbots from an end-user perspective. These metrics encompass an evaluation of language processing abilities, impact on real-world clinical tasks, and effectiveness in user-interactive conversations. Finally, we engage in a discussion concerning the challenges associated with defining and implementing these metrics, with particular emphasis on confounding factors such as the target audience, evaluation methods, and prompt techniques involved in the evaluation process.

Related papers

• VIEWER: an extensible visual analytics framework for enhancing mental healthcare [2.52780220954141]
  VIEWER is an open-source toolkit that employs distributed natural language processing and interactive visualisation techniques. VIEWER was developed to improve data accessibility and representation across various aspects of healthcare delivery.
  arXiv  Detail & Related papers  (2024-10-25T14:01:13Z)
• The Role of Language Models in Modern Healthcare: A Comprehensive Review [2.048226951354646]
  The application of large language models (LLMs) in healthcare has gained significant attention. This review examines the trajectory of language models from their early stages to the current state-of-the-art LLMs.
  arXiv  Detail & Related papers  (2024-09-25T12:15:15Z)
• 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)
• Designing Interpretable ML System to Enhance Trust in Healthcare: A Systematic Review to Proposed Responsible Clinician-AI-Collaboration Framework [13.215318138576713]
  The paper reviews interpretable AI processes, methods, applications, and the challenges of implementation in healthcare. It aims to foster a comprehensive understanding of the crucial role of a robust interpretability approach in healthcare.
  arXiv  Detail & Related papers  (2023-11-18T12:29:18Z)
• Generating medically-accurate summaries of patient-provider dialogue: A multi-stage approach using large language models [6.252236971703546]
  An effective summary is required to be coherent and accurately capture all the medically relevant information in the dialogue. This paper tackles the problem of medical conversation summarization by discretizing the task into several smaller dialogue-understanding tasks.
  arXiv  Detail & Related papers  (2023-05-10T08:48:53Z)
• Consultation Checklists: Standardising the Human Evaluation of Medical Note Generation [58.54483567073125]
  We propose a protocol that aims to increase objectivity by grounding evaluations in Consultation Checklists. We observed good levels of inter-annotator agreement in a first evaluation study using the protocol.
  arXiv  Detail & Related papers  (2022-11-17T10:54:28Z)
• Semi-Supervised Variational Reasoning for Medical Dialogue Generation [70.838542865384]
  Two key characteristics are relevant for medical dialogue generation: patient states and physician actions. We propose an end-to-end variational reasoning approach to medical dialogue generation. A physician policy network composed of an action-classifier and two reasoning detectors is proposed for augmented reasoning ability.
  arXiv  Detail & Related papers  (2021-05-13T04:14:35Z)
• Benchmarking Automated Clinical Language Simplification: Dataset, Algorithm, and Evaluation [48.87254340298189]
  We construct a new dataset named MedLane to support the development and evaluation of automated clinical language simplification approaches. We propose a new model called DECLARE that follows the human annotation procedure and achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2020-12-04T06:09:02Z)
• You Impress Me: Dialogue Generation via Mutual Persona Perception [62.89449096369027]
  The research in cognitive science suggests that understanding is an essential signal for a high-quality chit-chat conversation. Motivated by this, we propose P2 Bot, a transmitter-receiver based framework with the aim of explicitly modeling understanding.
  arXiv  Detail & Related papers  (2020-04-11T12:51:07Z)
• Opportunities of a Machine Learning-based Decision Support System for Stroke Rehabilitation Assessment [64.52563354823711]
  Rehabilitation assessment is critical to determine an adequate intervention for a patient. Current practices of assessment mainly rely on therapist's experience, and assessment is infrequently executed due to the limited availability of a therapist. We developed an intelligent decision support system that can identify salient features of assessment using reinforcement learning.
  arXiv  Detail & Related papers  (2020-02-27T17:04:07Z)

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.