A Survey on Human-Centered Evaluation of Explainable AI Methods in Clinical Decision Support Systems

• URL: http://arxiv.org/abs/2502.09849v1
• Date: Fri, 14 Feb 2025 01:21:29 GMT
• Title: A Survey on Human-Centered Evaluation of Explainable AI Methods in Clinical Decision Support Systems
• Authors: Alessandro Gambetti, Qiwei Han, Hong Shen, Claudia Soares,
• Abstract summary: This paper provides a survey of human-centered evaluations of Explainable AI methods in Clinical Decision Support Systems.<n>Our findings reveal key challenges in the integration of XAI into healthcare and propose a structured framework to align the evaluation methods of XAI with the clinical needs of stakeholders.
• Score: 45.89954090414204
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Explainable AI (XAI) has become a crucial component of Clinical Decision Support Systems (CDSS) to enhance transparency, trust, and clinical adoption. However, while many XAI methods have been proposed, their effectiveness in real-world medical settings remains underexplored. This paper provides a survey of human-centered evaluations of Explainable AI methods in Clinical Decision Support Systems. By categorizing existing works based on XAI methodologies, evaluation frameworks, and clinical adoption challenges, we offer a structured understanding of the landscape. Our findings reveal key challenges in the integration of XAI into healthcare workflows and propose a structured framework to align the evaluation methods of XAI with the clinical needs of stakeholders.

Related papers

• A Unified Framework for Evaluating the Effectiveness and Enhancing the Transparency of Explainable AI Methods in Real-World Applications [2.0681376988193843]
  "Black box" characteristic of AI models constrains interpretability, transparency, and reliability.<n>This study presents a unified XAI evaluation framework to evaluate correctness, interpretability, robustness, fairness, and completeness of explanations generated by AI models.
  arXiv  Detail & Related papers  (2024-12-05T05:30:10Z)
• Clinical Evaluation of Medical Image Synthesis: A Case Study in Wireless Capsule Endoscopy [63.39037092484374]
  Synthetic Data Generation based on Artificial Intelligence (AI) can transform the way clinical medicine is delivered. This study focuses on the clinical evaluation of medical SDG, with a proof-of-concept investigation on diagnosing Inflammatory Bowel Disease (IBD) using Wireless Capsule Endoscopy (WCE) images. The results show that TIDE-II generates clinically plausible, very realistic WCE images, of improved quality compared to relevant state-of-the-art generative models.
  arXiv  Detail & Related papers  (2024-10-31T19:48:50Z)
• 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)
• XAI for In-hospital Mortality Prediction via Multimodal ICU Data [57.73357047856416]
  We propose an efficient, explainable AI solution for predicting in-hospital mortality via multimodal ICU data. We employ multimodal learning in our framework, which can receive heterogeneous inputs from clinical data and make decisions. Our framework can be easily transferred to other clinical tasks, which facilitates the discovery of crucial factors in healthcare research.
  arXiv  Detail & Related papers  (2023-12-29T14:28:04Z)
• Elucidating Discrepancy in Explanations of Predictive Models Developed using EMR [2.1561701531034414]
  Lack of transparency and explainability hinders the clinical adoption of Machine learning (ML) algorithms. This study applies current state-of-the-art explainability methods to clinical decision support algorithms developed for Electronic Medical Records (EMR) data.
  arXiv  Detail & Related papers  (2023-11-28T10:13:31Z)
• 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)
• Polar-Net: A Clinical-Friendly Model for Alzheimer's Disease Detection in OCTA Images [53.235117594102675]
  Optical Coherence Tomography Angiography is a promising tool for detecting Alzheimer's disease (AD) by imaging the retinal microvasculature. We propose a novel deep-learning framework called Polar-Net to provide interpretable results and leverage clinical prior knowledge. We show that Polar-Net outperforms existing state-of-the-art methods and provides more valuable pathological evidence for the association between retinal vascular changes and AD.
  arXiv  Detail & Related papers  (2023-11-10T11:49:49Z)
• Exploring the Role of Explainability in AI-Assisted Embryo Selection [0.0]
  In Vitro Fertilization is among the most widespread treatments for infertility. One of its main challenges is the evaluation and selection of embryo for implantation. Deep learning based methods are gaining attention, but their opaque nature compromises their acceptance in the clinical context.
  arXiv  Detail & Related papers  (2023-08-01T09:46:31Z)
• Evaluation of Popular XAI Applied to Clinical Prediction Models: Can They be Trusted? [2.0089256058364358]
  The absence of transparency and explainability hinders the clinical adoption of Machine learning (ML) algorithms. This study evaluates two popular XAI methods used for explaining predictive models in the healthcare context.
  arXiv  Detail & Related papers  (2023-06-21T02:29:30Z)
• Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review [7.140215556873923]
  The impact of artificial intelligence during the COVID-19 pandemic was greatly limited by lack of model transparency. We find that successful use of XAI can improve model performance, instill trust in the end-user, and provide the value needed to affect user decision-making.
  arXiv  Detail & Related papers  (2021-12-23T16:55:27Z)
• Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration [55.88616573143478]
  Outcome prediction from clinical text can prevent doctors from overlooking possible risks. Diagnoses at discharge, procedures performed, in-hospital mortality and length-of-stay prediction are four common outcome prediction targets. We propose clinical outcome pre-training to integrate knowledge about patient outcomes from multiple public sources.
  arXiv  Detail & Related papers  (2021-02-08T10:26:44Z)
• Explaining Clinical Decision Support Systems in Medical Imaging using Cycle-Consistent Activation Maximization [112.2628296775395]
  Clinical decision support using deep neural networks has become a topic of steadily growing interest. clinicians are often hesitant to adopt the technology because its underlying decision-making process is considered to be intransparent and difficult to comprehend. We propose a novel decision explanation scheme based on CycleGAN activation which generates high-quality visualizations of classifier decisions even in smaller data sets.
  arXiv  Detail & Related papers  (2020-10-09T14:39:27Z)

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.